Amazon Security Lake Integration with Microsoft Sentinel: Parquet at the Gates
This blog has been jointly published by ChitreshPandit and arijitpaul. In this blog post, we explore how centralized AWS Security Lake data can be transformed and streamed into Microsoft Sentinel using an AWS Lambda-based pipeline for near real-time ingestion. A story of cost, control, and custom engineering at cloud scale Every organization strives to design its security architecture to help address architectural complexity and support cost‑aware, scalable designs, depending on workload characteristics and implementation choices. Across multiple customer environments, we increasingly see a consistent pattern emerge - security telemetry from various AWS services is not ingested in isolation, but is deliberately centralized into Amazon Security Lake. This approach reflects a maturity in design, where organizations move beyond service-level integrations and instead adopt a unified data strategy. Amazon Security Lake enables this by aggregating security data from services such as Route53, WAF, Kubernetes, and others into a centralized, governed repository. The data is normalized and stored in an open, analytics-friendly format, often leveraging Apache Parquet, a columnar storage format optimized for large-scale processing and cost-efficient storage. This can allow organizations to retain high volumes of security data while maintaining performance and may help optimize storage efficiency in certain scenarios, depending on data volume, retention policies, and analytics patterns. However, this architectural choice introduces a new consideration. Microsoft Sentinel, when integrated into such environments, typically expects ingestion through connector-driven pipelines and streaming event models. In contrast, Security Lake represents a batch-oriented, schema-driven data platform. Rather than treating this as a constraint, it becomes an opportunity to rethink how data should flow between these systems. In this blog, we explore how a streaming bridge architecture can be implemented to align Amazon Security Lake with Microsoft Sentinel’s ingestion model. The approach leverages a combination of AWS Lambda and event-driven patterns to process data as it lands in Amazon S3, transforms it into a Sentinel-compatible format, and streams it through Azure Event Hub into Microsoft Sentinel using Data Collection Rules (DCRs) and Data Collection Endpoints (DCEs). This approach can support lower‑latency ingestion patterns when configured appropriately and compared to batch‑only processing models while preserving the lake-first architecture, allowing organizations to support analytics, visualization, and threat hunting activities using the ingested data. For demonstration, this implementation focuses on ingesting the following data sources from Amazon Security Lake: Amazon EKS audit and runtime events Route 53 DNS query logs AWS WAF access logs AWS Lambda execution activity Amazon S3 access events Note: The solution and code provided in this blog are not an officially supported Microsoft solution and do not guarantee performance, reliability, availability, or support. No service-level agreements (SLAs) are included. Readers are responsible for validating suitability for their environment. Before we begin, let us briefly discuss Amazon Security Lake and the Parquet format. Amazon Security Lake and the Parquet Constraint Amazon Security Lake provides a centralized, S3-backed repository for security telemetry, addressing fragmentation across services, accounts, and regions. Instead of service-level ingestion pipelines, logs from sources such as EKS, Route 53, WAF, Lambda, and S3 are aggregated into a single, governed data layer, enabling consistent visibility, separation of duties, and cost-efficient storage at scale. This data is stored in Apache Parquet, a columnar format optimized for analytics—delivering high compression, schema evolution, and efficient, selective reads across engines like Athena and Spark. Microsoft Sentinel operates on a streaming ingestion model expecting JSON payloads, source-specific pipelines, and continuous event flows. In lake-first architectures, reintroducing service-level ingestion is neither practical nor efficient. The requirement, therefore, is to bridge the two models -preserving Parquet for storage while enabling event-driven ingestion at the point of consumption. In the following sections, we will create an Event Hub, Configure the Lambda function and once the streaming is configured in Event Hub we will configure the Data Collection Rules, Data Collection endpoints, Data Collection Rule associations to configure the ingestion pipeline from Event Hub to Sentinel. Pre-requisites: Log Analytics workspace where you have at least contributor rights. Your Log Analytics workspace needs to be linked to a dedicated cluster or to have a commitment tier. Event Hubs namespace that permits public network access. If public network access is disabled, ensure that "Allow trusted Microsoft services to bypass this firewall" is set to "Yes." Event Hubs with events flowing in. In this implementation, events are sent to Event Hubs by the AWS Lambda function configured in the steps below - no manual event sending is required. Appropriate IAM roles in AWS accounts to configure SQS queue, Lambda function, IAM policies, etc. The Event Hub To begin, we need to create an Event Hub. Azure Event Hubs is a fully managed, high-throughput event ingestion and streaming platform, designed to support high event volumes within documented service limits, subject to configuration and tier selection. SKUs (Standard vs Premium) Standard tier is a throughput-unit (TU) based model, where capacity is explicitly controlled and shared across the namespace. Premium tier provides isolated compute and memory via processing units (PU), which can provide more consistent performance characteristics and higher throughput capacity compared to shared models, depending on workload. Additionally, Azure Event Hubs offers a Dedicated tier, which is a fully isolated, single-tenant cluster for enterprise-scale workloads with higher throughputs (at significantly higher cost). Throughput Characteristics (Standard Tier) A single Throughput Unit (TU) provides: Ingress: up to 1 MB/s Egress: up to 2 MB/s A Standard namespace can scale to a maximum of 40 TUs, giving: Max ingress: 40 MB/s Max egress: 80 MB/s All Event Hubs, partitions, and consumers within the namespace share this TU capacity, making it a central ingestion buffer for streaming pipelines rather than a per-source scaling model. Which SKU to choose: For ingress/egress up to 40/80 MB/s, a Standard SKU may be suitable. Higher volumes may warrant consideration of Premium, depending on workload requirements. Azure Event Hubs Concepts: Event Hub Namespace: A logical container that provides the endpoint, networking boundary, and shared throughput capacity (TUs/PUs) for all Event Hubs within it. Event Hub: An individual event stream (topic) within the namespace where events are ingested, stored, and read in a partitioned, ordered manner for parallel processing. Reference: Event Hubs features and terminology - Azure Event Hubs Creating Event Hub namespace, Event Hub entities, and considerations: Create the Azure Event Hub Namespace, in this example, we create a Standard Event Hub with minimum 1 TU with Auto Inflate on enabling scaling upto the maximum 40 TUs. Note the maximum Throughput Units should be based on the size of the logs expected per second from Amazon Security Lake. Since the Event Hub will be used to ingest data in Azure Monitor (Sentinel Workspace) please check the Supported Regions. Ensure Event Hub region alignment with Microsoft Sentinel. Configure TU based on expected ingestion rate Once the Event Hub is created, enable the Local Authentication, since the AWS Lambda code we are using (discussed in the next section) uses Shared access signature to connect to the Azure Event Hub. See Shared Access Signatures. Create individual Event Hubs within the namespace created in Step 1. One Event Hub is required per log type - for example, if Amazon Security Lake includes EKS, Route 53, and WAF logs, then three Event Hub entities should be created. Why this matters: Easier DCR mapping Avoids schema conflicts Create a consumer group in each Event Hub we created in the previous step. Consumer Group is an independent view of an event stream that allows multiple applications to read the same events separately, each maintaining its own position (offset) in the stream. Event Hub Features Avoid using the $Default consumer group. With the Event Hub namespace, entities, and consumer groups in place, the receiving end of the pipeline is ready. The next step is to configure the AWS Lambda function that will translate Security Lake's Parquet files into the JSON events that Event Hub expects. The AWS Lambda Function (SQS-driven Parquet → JSON) In this architecture, AWS Lambda is the translation layer, not an ingestion source. Instead of being invoked directly by S3, Security Lake emits S3 object‑creation notifications into Amazon SQS, and SQS becomes the Lambda trigger. This decoupling is intentional: SQS absorbs bursts of newly written Parquet objects which can help increase resilience of the ingestion pipeline under bursty or variable workloads. Once triggered, the Lambda function processes each queued S3 notification end‑to‑end: it derives the log type from the S3 object key, downloads the Parquet file, converts each row into a discrete JSON event, and forwards the resulting events to Azure Event Hub in batches for downstream ingestion via DCR/DCE. A practical consideration is event size management. Some sources - especially EKS audit telemetry - can carry large, nested fields that are not always useful for Sentinel analytics. For those log types, the Lambda function drops non‑essential fields during transformation to keep each event within Azure ingestion constraints; oversized fields can exceed the 64 KB Azure Monitor field size limit and disrupt ingestion (Fields more than 64 KB will be truncated in Log Analytics Workspace). Solution Architecture The diagram above illustrates the end-to-end data flow: logs originate in AWS, are converted by AWS Lambda, streamed through Azure Event Hub, and stored in Microsoft Sentinel. Amazon Security Lake writes Parquet files to a centralized S3 bucket as logs arrive from source services (CloudTrail, EKS, VPC Flow, WAF, Route 53, and others). Amazon SQS receives S3 event notifications from Security Lake and queues them as Lambda triggers. AWS Lambda picks up each SQS message, identifies the log source from the S3 object key, downloads the Parquet file, converts each row to JSON, and forwards the events to Azure Event Hub. Azure Event Hub receives the JSON events and makes them available for ingestion into Microsoft Sentinel via a Data Collection Rule (DCR). Microsoft Sentinel ingests the data into a custom log table, where it is available for detection rules, hunting queries, and dashboards. The full Lambda function code is available in the GithubRepository-LambdaFunction. Refer to the readme for more details. How the Lambda Function works At a high level, the Lambda function performs four things in sequence for every file it processes: Identify the log source: Security Lake organizes files under a structured S3 key path that includes the log type (for example, CLOUD_TRAIL_MGMT, EKS_AUDIT, VPC_FLOW). The function reads this key path to determine which log source the file belongs to, and routes it to the corresponding Azure Event Hub entity. Files that cannot be matched to a known log type are skipped and logged as warnings. Download and decompress the Parquet file: The function streams the Parquet file from S3 directly to local Lambda storage rather than loading it entirely into memory. This keeps memory consumption bounded regardless of file size. Where Security Lake uses gzip compression, the function decompresses automatically before processing. Convert Parquet rows to JSON: Each row in the Parquet file is read in batches using PyArrow and converted to a JSON object. Parquet columns can carry data types - such as NumPy scalars, nested arrays, and high-precision timestamps — that are not natively serializable to JSON. The function handles these type conversions before serialization, ensuring clean output that Sentinel's ingestion pipeline can accept without errors. Forward events to Azure Event Hub: Converted JSON events are sent to the respective Azure Event Hub entity in batches, with each row becoming a discrete event. The function respects Event Hub's payload size ceiling, handles throttling responses gracefully using retry logic with exponential backoff, and marks each processed file in S3 metadata to prevent duplicate ingestion on retry. Tuning the Lambda Messages at cloud scale are unforgiving. Memory, timeout, batch size, and retry behavior - each decision determines whether the messenger would keep up or fall behind. Several configuration changes are required beyond the default Lambda settings to make this function production-ready. Runtime and Dependencies - Lambda Layer The function depends on three libraries not available in Lambda's default Python runtime: pyarrow (for Parquet reading), pandas (for type handling), and azure-eventhub (for Event Hub connectivity). These are packaged as an AWS Lambda Layer and attached to the function, keeping the deployment package clean and the layer reusable across function versions. Step-by-step instructions for packaging the dependencies, creating the S3 bucket, publishing the Layer, and deploying the function are available in the GithubRepository-LambdaLayer. Secrets Management - AWS Secrets Manager The Azure Event Hub connection strings - one per log type - are sensitive credentials that must not be stored in environment variables in plaintext. The function retrieves them at cold start from AWS Secrets Manager, using a single secret ARN passed as a Lambda environment variable (SECRET_ARN). The secret is stored as a JSON object with each log type as a key. The full secret structure and configuration steps are available in the GithubRepository-SecretsManager. IAM Permissions The Lambda execution IAM role requires scoped permissions across S3, Secrets Manager, SQS, and CloudWatch Logs. Full IAM policy JSON files following the principle of least privilege are available in the GithubRepository-IAMPolicies. Deployment instructions for the IAM Policies are available in the IAM Policies README. Memory Configuration A starting configuration of 1,792 MB is recommended - this is the threshold at which Lambda may allocate a full vCPU. For environments with high log volumes or large Parquet files, increasing to 2,048 MB provides headroom for concurrent batch processing. Tune further based on observed execution durations in CloudWatch Metrics. Timeout Configuration The default Lambda timeout of 3 seconds is insufficient for Parquet processing at scale. The function must download a file from S3, process it in batches, and flush all events to Event Hub - a sequence that can take tens of seconds for larger Security Lake files. A timeout of 5 minutes (300 seconds) is recommended as a starting point, with adjustment based on observed execution durations in CloudWatch Metrics. SQS Trigger Configuration The SQS queue connected to Security Lake S3 event notifications is configured as the trigger for the Lambda function. This enables automatic invocation of the Lambda function whenever Security Lake writes a new Parquet file to S3. Validate Event Hub Ingestion At this point, events should be streaming into each Event Hub. To validate, open a specific Event Hub from the Azure Portal and navigate to the Overview page. You should see active metrics across Requests, Messages, and Throughput, confirming that the Lambda function is successfully forwarding Security Lake events. In the upcoming sections, we will follow the documentation to ingest events from Event Hub to Azure Monitor. Before we begin, please collect the required information as stated here to have resource ID's and other details ready for configuration of DCR and Data Collection Endpoint. Also create a user assigned managed identity (UAMI), since the DCRs in this setup use a UAMI that should be granted the required permissions on the Event Hub Namespace to Receive Events. To grant the Azure Event Hubs Data Receiver role to the user-assigned managed identity, follow the instructions here. Creating Tables in Log Analytics Workspace As mentioned in the Overview, this blog covers the following data sources: Amazon EKS audit and runtime events Route 53 DNS query logs AWS WAF access logs AWS Lambda execution activity Amazon S3 access events The PowerShell scripts to create these tables are provided here: GithubRepo CreateLAWTables. For assistance with executing these scripts, refer to: Readme.md. Note: In the Microsoft Documentation to Ingest logs from EventHub into Azure Monitor only 3 fields are created in the tables (TimeGenerated, RawData, Properties). In this case, the entire Json event from the Event Hub is sent to the RawData field. However the scripts we are running are creating additional fields since we will be parsing the RawData field and extracting/parsing the information from the complete event into individual fields. This makes it easier to search and analyze logs later and schema improves detection rules and KQL efficiency. Create a Data Collection Endpoint To collect data with a data collection rule, you need a data collection endpoint: Create a data collection endpoint. Note: Create the data collection endpoint in the same region as your Log Analytics workspace. From the data collection endpoint's Overview screen, select JSON View. Copy the Resource ID for the data collection rule. You use this information in the next step while creating Data Collection Rules. Create Data Collection Rules Since we have 5 sources in scope for this example, we need to create 5 DCRs using the collected information as stated here, the user assigned managed identity resource ID, and the Data Collection Endpoint resource ID we created in the previous step. DCR Deployment via ARM Templates The Data Collection Rules ARM templates can be found in the GithubRepo-DataCollectionRules. The instructions to create via ARM templates can be found in the readme.md (Microsoft article reference with manual steps here). DCR Mapping (AWS Sources → DCR Templates) ARM Templates in GitHub Repo to AWS Source mapping Data Source DCR Template Name Amazon EKS Logs DCR-awseks.json AWS S3 Access Logs DCR-awsS3access.json AWS WAF Logs DCR-awswaf.json AWS Lambda Execution DCR-lambdaexecution.json Amazon Route 53 Logs DCR-Route53.json Final Step: Associating the Event Hub with the Data Collection Rule At this stage, the core building blocks of the ingestion pipeline are already in place. We have successfully configured streaming to Event Hubs, created dedicated Event Hubs for each Amazon Security Lake source, provisioned destination tables in the Microsoft Sentinel workspace, and defined Data Collection Rules (DCRs) - leveraging the Azure Monitor pipeline and a user-assigned managed identity to securely read incoming events. The final step is to stitch this entire architecture together by establishing associations between the Event Hubs and their corresponding Data Collection Rules. This association links Event Hub to Sentinel, enabling Azure Monitor to actively pull data from the Event Hubs and route it into the defined destination tables. Without this linkage, the pipeline remains incomplete - data may continue to flow into Event Hubs, but it will not be picked up or ingested into Sentinel. Each Event Hub must be explicitly mapped to its respective Data Collection Rule, ensuring: The correct stream is processed by the intended transformation logic Events are routed to the appropriate custom tables The ingestion pipeline operates in a deterministic and scalable manner helping ensure data is routed to the intended destination in a consistent and predictable manner. Once these associations are configured, the end‑to‑end pipeline is intended to operate as designed, subject to configuration accuracy and ongoing operational monitoring— enabling automated ingestion workflows of Amazon Security Lake data into Microsoft Sentinel with minimal manual intervention once configured. Steps to be followed: Associate the data collection rule with the Event Hub To complete the setup, we now associate each Event Hub with its corresponding Data Collection Rule (DCR). This creates the link that allows Azure Monitor to read data from the Event Hub and send it to Microsoft Sentinel. Important: You must create one association per Data Collection Rule. Example: If an Event Hub is receiving AWS Route 53 logs, it must be associated with the DCR created for AWS Route 53. Copy the template from the above link and deploy it to create each Data Collection Rule association. We have to create 1 association per Data Collection Rule. What You Need Event Hub Resource ID Follow these steps to get the Event Hub Resource ID: Open the Event Hub Namespace in the Azure Portal Go to Entities → Event Hubs Select the Event Hub that is receiving the logs (e.g., Route 53 logs) In the Overview page, click on JSON View Copy the Resource ID Resource Group, Region, and Association Name The Resource Group must be the same as the one where the Event Hub is deployed Provide the Azure region where the resources are deployed Define a unique name for the association Data Collection Rule (DCR) Resource ID Open the corresponding Data Collection Rule Go to the Overview page Click on JSON View Copy the Resource ID Key Note: Make sure that Each Event Hub is mapped to the correct DCR The data source and DCR template are aligned (e.g., Route 53 → Route 53 DCR) Validate End-to-End Ingestion Once this configuration is complete, we can validate the logs in the destination table, fields, and parsing accuracy. If you are building on a lake-first security architecture and running into ingestion challenges with Sentinel, feel free to share your experience in the comments or raise an issue in the GitHub repository.Operational Notes on Microsoft Security Copilot Agents in Defender XDR and Microsoft Entra ID
Microsoft Security Copilot is now becoming more visible inside day-to-day security operations, especially through embedded experiences and agent-based workflows across Microsoft Defender XDR, Microsoft Entra ID, Microsoft Intune, and Microsoft Purview. Instead of looking at Security Copilot only as a standalone prompt interface, SOC and identity teams should also understand how Security Copilot agents are deployed, how they consume Security Compute Units, how they appear in operational workflows, and where activity can be monitored. This post summarizes practical observations from a security operations perspective, with a focus on Microsoft Defender XDR, Microsoft Entra ID, usage monitoring, and KQL-based activity review. Licensing & Capacity Units Requirements Requires eligible Microsoft security licensing, typically: Microsoft 365 E5 Microsoft 365 E7 Security Compute Units (SCUs) Security Copilot capacity is measured using Security Compute Units (SCUs). SCUs are billed based on provisioned capacity. Indicative pricing: $4 per Provisionied SCU/hour $6 per Overage SCU/hour Billing is calculated hourly, based on the amount of SCUs provisioned. Included Capacity Organizations with: 1,000 Microsoft 365 E5 licenses Receive: 400 included SCUs Included SCUs are shared across the tenant within a common capacity pool. Scaling SCU capacity can be scaled dynamically based on operational requirements and workload demand. Data Retention Security Copilot session and interaction data without active SCU-backed retention is typically retained for: 90 days Security Copilot Agents - Microsoft Defender This section outlines the Microsoft Security Copilot agents currently available in the Microsoft Defender portal. NameKey characteristics Security Alert Triage Agent (Preview) Manual setup from Defender portal Automatically creates Unified RBAC custom role Runs automatically when a user reports a suspicious email or when a new supported alert is generated, supported alert sources: MDI, MDC, MDO If an alert tuning rule is enabled, it will be automatically disabled when the agent is deployed. Creates and connects with agentic user account: Phishing Triage Agent (Security Copilot) Automatic alert assignment to SecurityCopilotAgentUser-db16fec3-f1fb-4632-843e-46d07408c584@<tenant-domain>Alert was assigned to Phishing Triage Agent (Security Copilot). Adds Tag Agent to the created Incidents Threat Hunting Agent Manual setup from Defender portal Automatically creates Unified RBAC custom role This agent runs manually. There isn't an automatic trigger. Creates and connects with agentic user account: Threat Hunting Agent (Security Copilot) Analyst Questions in natural language Generates and executed KQL queries in Advanced hunting Provides charts, dynamic follow-up questions and remediation actions recommendations No activity is identified from agent's identity during agent execution Threat Intelligence Briefing Agent Manual setup from Defender portal Provides automated TI briefing summary Configured from https://security.microsoft.com/securitysettings/defender/agent_configuration-threatintelligencebriefingagent Security Analyst Agent Manual setup from Defender portal Dynamic Threat Detection Agent (Preview) Automatically enabled always-on, runs continuously in the background Correlates: Alerts, Security events, Behavioral anomalies, TI signals Generates Alerts with Detection Source: Security Copilot The Alerts can be correlated with existing Multi-Stage Incidents No agentic user account identity is used by this agent Available free of charge during public preview, will begin consuming Security Compute Units (SCUs) once generally available (GA) Incidents handled by Security Alert Triage Agent: Alerts created by Dynamic Threat Detection Agent: Execution of Threat Hunting Agent: View agents in use: https://security.microsoft.com/security-copilot/agents View Unified RBAC custom roles: https://security.microsoft.com/mtp_roles View Security Copilot user identities in Microsoft Entra ID: Notes: CloudAppEvents activity logs only from the following agents: Phishing Triage Agent Conditional Access Optimization Agent Security Copilot Agents - Microsoft Entra ID Conditional Access Optimization Agent Usage Monitoring Sign-in to Security Copilot portal using Global Admin account and navigate to the following location: https://securitycopilot.microsoft.com/usage-monitoring Reference: https://learn.microsoft.com/en-us/copilot/security/manage-usage Logging Activity Copilot Agents Management: CloudAppEvents | where ActionType contains "CopilotAgent" | extend AgentName = RawEventData.AgentName | extend Workload = RawEventData.Workload | extend ResultStatus = RawEventData.ResultStatus | project TimeGenerated, ActionType, ResultStatus, AgentName, Application, Workload All Copilot Workload data: CloudAppEvents | extend Workload = RawEventData.Workload | where Workload == "Copilot" | summarize EventCount = count() by ActionType, AccountDisplayNameIgnite 2025: What's new in Microsoft Defender?
This Ignite we are focused on giving security teams the edge they need to meet adversaries head on in the era of AI. The modern Security Operations Center (SOC) is undergoing a fundamental transformation, placing AI at the forefront of innovation - not just as an added feature, but as a driving force at every layer of the stack. While much attention is rightly focused on the development of security agents, we fundamentally believe that AI must also evolve the very foundation of our security solutions. This means building solutions that more effectively uncover novel threats, act dynamically to defend the organization during attacks, and reduce the workload for the security team. As organizations adopt AI at an unprecedented speed, we also want to make sure they can do so securely. To meet these security needs of the AI era, we are excited to announce a series of innovations that will help organizations shift to an autonomous defense and an agentic SOC. New agents to help scale and accelerate security operations Evolving Microsoft Defender’s autonomous defense capabilities for better protection Secure your low-code and pro-code AI agents with Microsoft Defender Today, we are taking the first step in shifting security operations from static controls to autonomous defense and from manual toil to agentic operations. But we have an ambitious vision to augment and evolve these AI capabilities and agents across the entire SOC lifecycle and are excited to share some of that vision, as shown in the below graphic, with you at Microsoft Ignite. The Agentic SOC: Scaling expertise and accelerating defense We are excited to introduce four new Security Copilot agents in Microsoft Defender that bring autonomous intelligence across different stages of the SOC lifecycle. These agents combine context, reasoning, and complex workflows to help defenders anticipate attacks sooner, detect smarter, and investigate faster than ever before. Security Alert Triage Agent: In March 2025, we introduced the Security Alert Triage Agent, built to autonomously handle user-submitted phishing reports at scale. The agent reviews and classifies incoming alerts, resolves false positives and escalates only the malicious cases that require human expertise. Early data shows that analysts working with the agent caught up to 6.5x more malicious emails compared to professional graders. Today, we’re excited to announce that the agent’s triage capabilities will soon extend beyond phishing to cover identity and cloud alerts. Secondly, we are also improving our phish admin reporting process with a new agentic email grading system. It replaces a manual review process with advanced large language models and agentic workflows to deliver rapid, transparent verdicts and clear explanations to customers for every reported email. Learn more about the agentic email grading system. Threat Hunting Agent – this agent reimagines the investigation process. Instead of requiring analysts to master complex query languages or sift through mountains of data, the Threat Hunting Agent enables natural language investigations with contextual insight. Analysts can vibe with the agent by asking questions in plain English, receive direct answers, and be guided through comprehensive hunting sessions. This levels up the current NL2KQL experience by enabling analysts to explore patterns, pivot intuitively and uncover hidden signals in real time for a fluid, context-aware experience. This not only accelerates investigations but makes advanced threat hunting accessible to every member of the SOC, regardless of experience level. Dynamic Threat Detection Agent – One of the hardest challenges in detection engineering is finding and fixing false negatives. The Dynamic Threat Detection Agent proactively hunts for false negatives and blind spots that traditional alerting might miss. When a critical incident happens, Copilot will kick off an automated hunt to uncover undetected threats—like unusual residual activity around a sensitive identity. This agent turns ‘probably fine’ into proven secure—hunting the quiet persistence that slips past alerts and closing the gap before it becomes tomorrow’s breach. Threat Intelligence (TI) Briefing Agent – Now native in the Defender portal. Generate tailored, AI‑authored threat briefings in minutes—synthesizing global intel with your environment’s context—without leaving the incident pane. Figure 1. The Threat Hunting Agent showing insights on an incident that contained a high risk binary To make the agents easily accessible and help security teams get started more quickly, we are excited to announce that Security Copilot will be available to all Microsoft 365 E5 customers. Rollout starts today for existing Security Copilot customers with Microsoft 365 E5 and will continue in the upcoming months for all Microsoft 365 E5 customers. Customers will receive 30-day advanced notification before activation. Learn more. Autonomous Defense at Platform Scale Threat actors are automating everything. Ransomware campaigns can encrypt an entire environment in under an hour. Adversaries evade detection and pivot across identities, endpoints, and cloud resources faster than human teams can triage alerts. Traditional SOC models—built on manual workflows and fragmented tools—simply can’t keep pace. Every second of delay gives attackers an advantage. Microsoft Defender now counters that speed by delivering autonomous defense at scale. Defender shifts security from reactive firefighting to proactive protection, embedding AI into the foundation of our protection solutions for instant detection, disruption, and containment—before threats escalate. In 2023, we introduced automatic attack disruption, which autonomously stops attacks in progress—like ransomware or business email compromise—with policy-bound actions that isolate endpoints, disable compromised accounts, and block malicious IPs at machine speed. Today, we’re taking the next step. New capabilities show how AI and agentic technology are transforming security to better protect customers: Unleash automatic attack disruption across your SIEM data: We are expanding the disruption capabilities of Microsoft Defender to some of the most critical data sources customer connect via Microsoft Sentinel including AWS, Proofpoint and Okta. This enables real-time detection and automatic containment of threats like phishing and identity compromise on top of your log data, fundamentally turning your SIEM into a threat protection solution. While these capabilities leverage the power of our platform, Defender is not a requirement for customers to realize this value in Microsoft Sentinel. Figure 2. Attack disruption initiated on an AWS attack Predictive shielding – This brand-new automatic attack disruption capability activates immediately after an attack is first contained. Our first of its kind capability combines graph insights, AI, and threat intelligence to predict potential attack paths for where the adversary might go next. It then applies just-in-time hardening techniques that proactively block the attacker from pivoting. Some of the hardening tactics that will automatically be applied by Microsoft Defender include disabling SafeBoot and enforcing Group Policy Objects, putting a hard stop to the attacker’s movements and ability to execute common techniques for compromise. Learn more about predictive shielding and other endpoint security news. Protect your low-code and pro-code AI agents Generative AI and agents are rapidly transforming how we work, but these powerful new tools also introduce new risks. And with the democratization of agent creation across pro-code, low-code, and no-code building platforms, building agents is now accessible to everyone, many without extensive developer or security knowledge. To help security teams better manage these risks we are excited to announce that we are extending the capabilities and experiences in Microsoft Defender to the protection of agents. From agent security posture management, to attack path analysis, and threat protection for Copilot Studio, Azure Foundry, and agents built and connected via the Microsoft Agent 365 SDK. Learn more about how Microsoft Defender can help protect your agents against threats like prompt injections and more. There is so much more innovation we are introducing in Microsoft Defender today, including expanded endpoint security coverage for legacy systems, improvements to how you can investigate identity-centric threats, and we are bringing cloud security posture management into the Defender portal. Check out the other Defender news blogs for more details. Join us in San Francisco, November 17–21, or online, November 18–20, for deep dives and practical labs to help you maximize your Microsoft Defender investments and to get more from the Microsoft capabilities you already use. Featured sessions: Microsoft Defender: Building the agentic SOC with guest Allie Mellen Blueprint for building the SOC of the future Empowering the SOC: Security Copilot and the rise of agentic defense Identity Under Siege: Modern ITDR from Microsoft AI vs AI: Protect email and collaboration tools with Microsoft Defender AI-powered defense for cloud workloads Endpoint security in the AI era: What's new in DefenderWhat’s new in Microsoft Sentinel: May 2026
Welcome to the May edition of What's new in Microsoft Sentinel. This month’s updates focus on unified role-based access control (RBAC), ecosystem breadth, AI-agent security, and high-assurance identity. RBAC and row-level scoping are now generally available, giving security teams a single, granular permissions model across Sentinel and the Microsoft Defender portal and enabling multi-team SOC collaboration. The Sentinel connector catalog has passed 400 connectors, expanding coverage across Microsoft and third-party data sources and helping customers and partners onboard new data faster with the Codeless Connector Framework (CCF). The Agent 365 connector, now in public preview, brings AI agent telemetry into Sentinel data lake as first-class standardized signals so you can monitor agent behavior alongside identity, endpoint, and cloud activity. Finally, Entra Verified ID partner integrations in Microsoft Security Store are now generally available, delivering high‑assurance identity verification that makes account recovery after compromise far safer and significantly reduces the risk of re‑compromise. Read on for the full list of updates across Sentinel in May. Sentinel innovations: Sentinel SIEM Sentinel data lake Microsoft Security Store Sentinel SIEM Unified role-based access controls and row level scoping [Generally available] Sentinel now delivers general availability of two powerful access management capabilities: Unified RBAC and row-level data scoping. Together, these innovations provide a consistent, end-to-end model for controlling who can access data and what actions they can take — extending unified permissions management across the Defender portal while enabling granular, row-level visibility within a single Sentinel workspace. With Unified RBAC, organizations can simplify and centralize permissions across security workloads, reducing operational overhead, while row-level scoping enables secure collaboration across multiple teams by ensuring users only see data aligned to their role or scope. This milestone unlocks more scalable, multi-team SOC operations without the need for workspace segmentation, helping us to advance toward fully unified, granular access control across Microsoft Security. Tenant groups [Public preview] Managing security across multiple tenants just got simpler. Tenant Groups in the Microsoft Defender multi-tenant portal (MTO) give managed security service providers (MSSPs), cloud service partners (CSPs), and multi-tenant security teams a flexible way to organize tenants into logical groupings such as customer segment, geography, or operational priority, and instantly switch views with a single click. This streamlined experience reduces noise, improves investigation focus, and aligns to how teams actually work, all while respecting existing permissions and access controls. Learn more. Out-of-the-box integrations for Sentinel automation [Public preview] Out-of-the-box (OOTB) integrations for Sentinel automation brings a centralized catalog to easily discover, configure, and manage both Microsoft and third-party integrations. With simple, authentication-based setup, users can quickly add integrations and seamlessly incorporate them into playbooks. The experience places OOTB and custom integrations side by side, with enhanced with smart search, recommendations, and duplicate prevention to streamline automation workflows end to end. Learn more. UEBA enhancements [Public preview] Microsoft Sentinel UEBA continues to evolve with improvements that simplify management and expand detection coverage. A dedicated UEBA tab view in the Sentinel settings page consolidates UEBA and behaviors settings, making configuration easier to find and manage. Learn more. UEBA insights and anomalies now support the OktaV2_CL table alongside the existing Okta_CL table, extending anomalous activity and anomalous MFA failures detections to customers using the newer Okta connector format, without requiring new anomaly types. Learn more. UEBA extends GCP Audit Logs coverage with five anomaly detections for login activity, privileged actions, resource deployments, secret/KMS key access, and infrastructure usage. Learn more. Together, these updates make UEBA easier to operate while extending its visibility into identity and behavior signals from additional cloud and identity providers. Read the latest blog from the Microsoft Defender Research Team to learn more about Microsoft Sentinel UEBA and binary feature stacking, which uses clear binary signals to help establish behavioral context and inform investigation and detection decisions. Threat Intelligence – TAXII Export connector [Generally available] Sentinel supports threat intelligence export through the built-in Threat Intelligence – Trusted Automated Exchange of Intelligence Information (TAXII) Export connector, giving customers a standards-based way to share curated Structured Threat Information Expression (STIX) objects with supported TAXII 2.1 platforms. Configured from the Defender portal, the connector handles destination setup and intelligence delivery to external platforms. The capability supports cross-organization intelligence sharing for collective defense and centralized management in multi-tenant environments, with use cases across government, critical infrastructure, and large distributed organizations. Additional enhancements are planned, including more export options and expanded destination support. Learn more. Decision-stage resources for SIEM migration to Sentinel The AI-powered SIEM migration experience helps teams analyze detections, identify required data sources and connectors, and plan a phased move to Sentinel. But, customers still need help turning that analysis into a clear decision. To support that step, we’re introducing two new customer-facing resources: the Sentinel SIEM Migration Decision and Planning Guide, which explains the migration journey, outputs, and decision checkpoints before execution, and the Decision-Stage Customer FAQ, which answers common questions around disruption, cost, dual running, detection coverage, and delivery support. Together, these resources help make migration conversations more concrete and move teams more quickly from evaluation to a clearer, lower-risk next step. Learn more: Read the blog: AI-powered SIEM migration experience announcement Download the guide: Decision and planning guide Download the FAQ: Decision-stage customer FAQ Learn more: SIEM migration experience documentation Register for live AMA (Jun 23 at 9am PT): Live Microsoft Tech Community AMA on SIEM migration Sentinel data lake 400+ Sentinel data connectors The Sentinel connector catalog now includes 400+ connectors, providing broad, ready-to-deploy coverage across Microsoft and third-party data sources. Customers can flexibly ingest security data into Microsoft Sentinel analytics tier or the data lake tier. The Codeless Connector Framework (CCF) and VS code-based connector builder agent enables partners and customers to onboard new data sources faster and scale the catalog. Discover connectors in the Sentinel Content hub within the Defender portal or build custom connectors when needed. Learn more. Agent 365 connector [Public preview] Agent 365 connector streams AI agent telemetry from Agent 365 into Sentinel data lake, giving SOC teams visibility into agent behavior alongside identity, endpoint, and cloud signals. With the Agent 365 connector in place, Sentinel data lake becomes the system of record for agent security, turning activity such as data exposure or access drift into first-class security signals that analysts can correlate, hunt across, and investigate. Telemetry is normalized and to mapped to standard Advanced Security Information Model (ASIM) schemas, ready for analytics and detections, and end-to-end investigations can run through KQL, graph, and MCP-powered workflows. Install the connector with a single click from Sentinel Content Hub in the Defender portal. Learn more. CCF support for Azure Blob Storage [Public preview] Sentinel Codeless Connector Framework (CCF) supports Azure Blob Storage as a data source, providing an ingestion pattern designed for high-volume security data. Partners and customers can build CCF connectors that read from Blob Storage through a durable architecture that buffers spikes, handles backpressure, and reduces data loss risk during outages or throttling, making ingestion more reliable for variable or distributed pipelines. The pattern broadens compatibility with partners already streaming logs to Azure as part of their audit data delivery, with Cloudflare and Netskope as early adopters. App Assure further provides engineering-backed support for designing, validating, and remediating the Azure Blob Storage CCF connector integration. Learn more. Data filtering and splitting [Generally available] At RSAC, we announced built‑in filtering and splitting capabilities in Microsoft Sentinel, which is now generally available. As security teams ingest more data, it is important to optimize security data pipeline by controlling what data is ingested and in which tier. With filtering and splitting natively integrated into the Defender portal, security teams can shape data before it reaches Sentinel, without switching tools or managing custom JSON files. Using simple KQL‑based transformations directly in the UI, you can filter low‑value events and intelligently route data, making ingestion optimization faster, more intuitive, and easier to manage at scale. Filtering at ingest time allows you to remove low‑value or benign events to reduce noise, lower unnecessary processing, and ensure high‑signal data drives detections and investigations. Splitting enables intelligent routing of data between the analytics tier and the data lake tier based on relevance and usage. Together, these capabilities help you balance cost and performance while scaling data ingestion sustainably as your digital estate grows. Learn more. Transition your Sentinel connectors to the Codeless Connector Framework (CCF) [Action required] Azure has announced that the legacy Azure Data Collection API will be deprecated on September 14, 2026. Sentinel recommends customers review existing connectors and upgrade to the latest Codeless Connector Framework (CCF) versions to ensure continued access to the newest Sentinel capabilities. CCF delivers a fully managed SaaS experience with built-in health monitoring, centralized credential management, and improved performance. This enables partners and customers to onboard new data sources faster and at scale. Microsoft Security Store Entra Verified ID partner integrations via Security Store [Generally available] Security Store helps organizations secure one of the most critical steps in incident response: safe account recovery after compromise. Once a SOC team detects and contains a potential account takeover (ATO), restoring access requires high confidence that the user is legitimate. Through partner integrations with IDEMIA, AU10TIX, CLEAR, 1Kosmos, and WhoAmI, customers can extend Entra Verified ID with high-assurance identity verification (such as document and biometric checks) to validate users during recovery, onboarding, or helpdesk workflows. This helps replace weaker fallback methods that attackers often exploit, enabling SOC and IT teams to safely restore access while reducing risk of re-compromise. Learn more. Purview Data Security Triage Agent in Defender [Public preview] Security Store powers how customers discover and activate data security agents across Defender and Microsoft Purview, starting with the Data Security Triage Agent. This capability delivers AI-generated summaries and prioritization of Data Loss Prevention (DLP) alerts directly into Defender XDR, helping security teams reduce noise and focus on the incidents that matter most. By unifying discovery and activation through Security Store, customers can deploy data security agents in fewer steps and enable more integrated workflows across threat and data protection surfaces. Learn more. Additional resources Blogs and documentation: From idea to production: Building Security Store Advisor with an agentic SDLC Upcoming webinars: June 4: End-to-End Security in the Age of Agentic AI June 10: Deploy, optimize, and implement threat protection with Sentinel June 10: Security Foundations for AI Adoption June 24: Modern Security Made Simple: Stay Ahead of Threats with Sentinel Upcoming events: June 2–3: Microsoft Build, San Francisco (and free online) CEO Satya Nadella Day 1 keynote 90+ sessions, Microsoft Security experts onsite Register: build.microsoft.com Stay connected Check back each month for the latest innovations, updates, and events to ensure you’re getting the most out of Microsoft Sentinel. We’ll see you in the next edition!How Azure network security can help you meet NIS2 compliance
With the adoption of the NIS2 Directive EU 2022 2555, cybersecurity obligations for both public and private sector organizations have become more strict and far reaching. NIS2 aims to establish a higher common level of cybersecurity across the European Union by enforcing stronger requirements on risk management, incident reporting, supply chain protection, and governance. If your organization runs on Microsoft Azure, you already have powerful services to support your NIS2 journey. In particular Azure network security products such as Azure Firewall, Azure Web Application Firewall WAF, and Azure DDoS Protection provide foundational controls. The key is to configure and operate them in a way that aligns with the directive’s expectations. Important note This article is a technical guide based on the NIS2 Directive EU 2022 2555 and Microsoft product documentation. It is not legal advice. For formal interpretations, consult your legal or regulatory experts. What is NIS2? NIS2 replaces the original NIS Directive 2016 and entered into force on 16 January 2023. Member states must transpose it into national law by 17 October 2024. Its goals are to: Expand the scope of covered entities essential and important entities Harmonize cybersecurity standards across member states Introduce stricter supervisory and enforcement measures Strengthen supply chain security and reporting obligations Key provisions include: Article 20 management responsibility and governance Article 21 cybersecurity risk management measures Article 23 incident notification obligations These articles require organizations to implement technical, operational, and organizational measures to manage risks, respond to incidents, and ensure leadership accountability. Where Azure network security fits The table below maps common NIS2 focus areas to Azure network security capabilities and how they support compliance outcomes. NIS2 focus area Azure services and capabilities How this supports compliance Incident handling and detection Azure Firewall Premium IDPS and TLS inspection, Threat Intelligence mode, Azure WAF managed rule sets and custom rules, Azure DDoS Protection, Azure Bastion diagnostic logs Detect, block, and log threats across layers three to seven. Provide telemetry for triage and enable response workflows that are auditable. Business continuity and resilience Azure Firewall availability zones and autoscale, Azure Front Door or Application Gateway WAF with zone redundant deployments, Azure Monitor with Log Analytics, Traffic Manager or Front Door for failover Improve service availability and provide data for resilience reviews and disaster recovery scenarios. Access control and segmentation Azure Firewall policy with DNAT, network, and application rules, NSGs and ASGs, Azure Bastion for browser based RDP SSH without public IPs, Private Link Enforce segmentation and isolation of critical assets. Support Zero Trust and least privilege for inbound and egress. Vulnerability and misconfiguration defense Azure WAF Microsoft managed rule set based on OWASP CRS. Azure Firewall Premium IDPS signatures Reduce exposure to common web exploits and misconfigurations for public facing apps and APIs. Encryption and secure communications TLS policy: Application Gateway SSL policy; Front Door TLS policy; App Service/PaaS minimum TLS. Inspection: Azure Firewall Premium TLS inspection Inspect and enforce encrypted communication policies and block traffic that violates TLS requirements. Inspect decrypted traffic for threats. Incident reporting and evidence Azure Network Security diagnostics, Log Analytics, Microsoft Sentinel incidents, workbooks, and playbooks Capture and retain telemetry. Correlate events, create incident timelines, and export reports to meet regulator timelines. NIS2 articles in practice Article 21 cybersecurity risk management measures Azure network controls contribute to several required measures: Prevention and detection. Azure Firewall blocks unauthorized access and inspects traffic with IDPS. Azure DDoS Protection mitigates volumetric and protocol attacks. Azure WAF prevents common web exploits based on OWASP guidance. Logging and monitoring. Azure Firewall, WAF, DDoS, and Bastion resources produce detailed resource logs and metrics in Azure Monitor. Ingest these into Microsoft Sentinel for correlation, analytics rules, and automation. Control of encrypted communications. Azure Firewall Premium provides TLS inspection to reveal malicious payloads inside encrypted sessions. Supply chain and service provider management. Use Azure Policy and Defender for Cloud to continuously assess configuration and require approved network security baselines across subscriptions and landing zones. Article 23 incident notification Build an evidence friendly workflow with Sentinel: Early warning within twenty four hours. Use Sentinel analytics rules on Firewall, WAF, DDoS, and Bastion logs to generate incidents and trigger playbooks that assemble an initial advisory. Incident notification within seventy two hours. Enrich the incident with additional context such as mitigation actions from DDoS, Firewall and WAF. Final report within one month. Produce a summary that includes root cause, impact, and corrective actions. Use Workbooks to export charts and tables that back up your narrative. Article 20 governance and accountability Management accountability. Track policy compliance with Azure Policy initiatives for Firewall, DDoS and WAF. Use exemptions rarely and record justification. Centralized visibility. Defender for Cloud’s network security posture views and recommendations give executives and owners a quick view of exposure and misconfigurations. Change control and drift prevention. Manage Firewall, WAF, and DDoS through Network Security Hub and Infrastructure as Code with Bicep or Terraform. Require pull requests and approvals to enforce four eyes on changes. Network security baseline Use this blueprint as a starting point. Adapt to your landing zone architecture and regulator guidance. Topology and control plane Hub and spoke architecture with a centralized Azure Firewall Premium in the hub. Enable availability zones. Deploy Azure Bastion Premium in the hub or a dedicated management VNet; peer to spokes. Remove public IPs from management NICs and disable public RDP SSH on VMs. Use Network Security Hub for at-scale management. Require Infrastructure as Code for all network security resources. Web application protection Protect public apps with Azure Front Door Premium WAF where edge inspection is required. Use Application Gateway WAF v2 for regional scenarios. Enable the Microsoft managed rule set and the latest version. Add custom rules for geo based allow or deny and bot management. enable rate limiting when appropriate. DDoS strategy Enable DDoS Network Protection on virtual networks that contain internet facing resources. Use IP Protection for single public IP scenarios. Configure DDoS diagnostics and alerts. Stream to Sentinel. Define runbooks for escalation and service team engagement. Firewall policy Enable IDPS in alert and then in alert and deny for high confidence signatures. Enable TLS inspection for outbound and inbound where supported. Enforce FQDN and URL filtering for egress. Require explicit allow lists for critical segments. Deny inbound RDP SSH from the internet. Allow management traffic only from Bastion subnets or approved management jump segments. Logging, retention, and access Turn on diagnostic settings for Firewall, WAF, DDoS, and Application Gateway or Front Door. Send to Log Analytics and an archive storage account for long term retention. Set retention per national law and internal policy. Azure Monitor Log Analytics supports table-level retention and archive for up to 12 years, many teams keep a shorter interactive window and multi-year archive for audits. Restrict access with Azure RBAC and Customer Managed Keys where applicable. Automation and playbooks Build Sentinel playbooks for regulator notifications, ticket creation, and evidence collection. Maintain dry run versions for exercises. Add analytics for Bastion session starts to sensitive VMs, excessive failed connection attempts, and out of hours access. Conclusion Azure network security services provide the technical controls most organizations need in order to align with NIS2. When combined with policy enforcement, centralized logging, and automated detection and response, they create a defensible and auditable posture. Focus on layered protection, secure connectivity, and real time response so that you can reduce exposure to evolving threats, accelerate incident response, and meet NIS2 obligations with confidence. References NIS2 primary source Directive (EU) 2022/2555 (NIS2). https://eur-lex.europa.eu/eli/dir/2022/2555/oj/eng Azure Firewall Premium features (TLS inspection, IDPS, URL filtering). https://learn.microsoft.com/en-us/azure/firewall/premium-features Deploy & configure Azure Firewall Premium. https://learn.microsoft.com/en-us/azure/firewall/premium-deploy IDPS signature categories reference. https://learn.microsoft.com/en-us/azure/firewall/idps-signature-categories Monitoring & diagnostic logs reference. https://learn.microsoft.com/en-us/azure/firewall/monitor-firewall-reference Web Application Firewall WAF on Azure Front Door overview & features. https://learn.microsoft.com/en-us/azure/frontdoor/web-application-firewall WAF on Application Gateway overview. https://learn.microsoft.com/en-us/azure/web-application-firewall/overview Examine WAF logs with Log Analytics. https://learn.microsoft.com/en-us/azure/application-gateway/log-analytics Rate limiting with Front Door WAF. https://learn.microsoft.com/en-us/azure/web-application-firewall/afds/waf-front-door-rate-limit Azure DDoS Protection Service overview & SKUs (Network Protection, IP Protection). https://learn.microsoft.com/en-us/azure/ddos-protection/ddos-protection-overview Quickstart: Enable DDoS IP Protection. https://learn.microsoft.com/en-us/azure/ddos-protection/manage-ddos-ip-protection-portal View DDoS diagnostic logs (Notifications, Mitigation Reports/Flows). https://learn.microsoft.com/en-us/azure/ddos-protection/ddos-view-diagnostic-logs Azure Bastion Azure Bastion overview and SKUs. https://learn.microsoft.com/en-us/azure/bastion/bastion-overview Deploy and configure Azure Bastion. https://learn.microsoft.com/en-us/azure/bastion/tutorial-create-host-portal Disable public RDP and SSH on Azure VMs. https://learn.microsoft.com/en-us/azure/virtual-machines/security-baseline Azure Bastion diagnostic logs and metrics. https://learn.microsoft.com/en-us/azure/bastion/bastion-diagnostic-logs Microsoft Sentinel Sentinel documentation (onboard, analytics, automation). https://learn.microsoft.com/en-us/azure/sentinel/ Azure Firewall solution for Microsoft Sentinel. https://learn.microsoft.com/en-us/azure/firewall/firewall-sentinel-overview Use Microsoft Sentinel with Azure WAF. https://learn.microsoft.com/en-us/azure/web-application-firewall/waf-sentinel Architecture & routing Hub‑spoke network topology (reference). https://learn.microsoft.com/en-us/azure/architecture/networking/architecture/hub-spoke Azure Firewall Manager & secured virtual hub. https://learn.microsoft.com/en-us/azure/firewall-manager/secured-virtual-hubOrganize your multitenant view with Tenant Groups in Microsoft Defender
Managing security across many tenants shouldn’t mean drowning in a single, flat list. We’re excited to share a new capability, now in public preview in the Microsoft Defender multitenant (MTO) portal: Tenant Groups—a flexible way to organize the tenants you manage and switch your view between them with a single click. If you’re a managed security service provider (MSSP), a cloud service provider (CSP), or a security team operating across multiple Entra ID tenants, this one’s for you. What’s new Tenant Groups let you create logical groupings of tenants (by customer segment, geography, criticality, onboarding stage—whatever fits how you work) and seamlessly switch the Defender MTO view to show data from only the tenants in that group. NOTICE: The feature previously called Tenant groups—used for content distribution—has been renamed to Deployment profiles. The name “Tenant Groups” now refers to this new grouping experience. Why it matters Focus, faster – Investigate incidents, hunt threats, and review posture against just the tenants you care about right now—without noise from the rest. Operational clarity – Group tenants the way your team actually works (e.g., Tier 1 customers, EMEA, Pilot rollout). Permissions-aware – Even if a Tenant Group contains more tenants, you’ll only see the ones where you have B2B/GDAP (granular delegated admin privileges) access. Your existing access controls stay in charge. Permissions you’ll need To work with Tenant Groups, your account needs one of the following: Entra ID roles Security Administrator Security Operator Global Administrator Product-specific (MDE, MDI, etc.) role-based access control (RBAC) Global Administrator Security Administrator Plus, any custom RBAC roles required to see data across products Unified RBAC (URBAC) Security/read—to view Tenant Groups Security/manage—to create Tenant Groups Remember: A Tenant Group can include tenants you don’t have access to. You’ll only ever see the ones your permissions allow. Getting started 1. Open Tenant Groups Sign in to the Microsoft Defender portal with administrative credentials, then navigate to Multitenant Management > Tenant Groups. You’ll find a built-in group called My private group that contains all the tenants from your previous setup. You can add or remove tenants from it, but it can’t be deleted. 2. Create a Tenant Group Select + Create tenant group. Give it a descriptive name (e.g., Healthcare customers, EMEA Tier 1). Optionally, add a description so teammates know the group’s intent. Select the tenants you want to include. That’s it—your group is ready. 3. Switch between Tenant Groups In the top-left corner of the portal, select Open multitenant management. Choose the group you just created. Navigate around the Defender MTO portal—incidents, alerts, devices, hunting—and you’ll see only data from the tenants in that group. Switch groups anytime to refocus. Live change detection: If a teammate edits a Tenant Group (adds or removes tenants) while you’re viewing it, the portal surfaces a notification so you know the underlying scope has changed. No stale views, no surprises. 4. Edit a Tenant Group Go back to Multitenant Management > Tenant Groups. Select the group and choose Edit. Add or remove tenants as your environment evolves, then re-test your views. Tips for getting the most out of Tenant Groups Start with how your team triages – Name groups after the workflows you actually run (On-call queue, Customer A—production). Keep groups small and purposeful – Overlapping, focused groups beat one giant catch-all. Pair with Deployment profiles – Use Tenant Groups for viewing, and Deployment profiles for distributing content—two clean, complementary concepts. Audit access regularly – Because group membership is independent of B2B/GDAP access, periodic reviews keep expectations aligned. We want your feedback Tenant Groups are designed around real multitenant operations work—and we’d love to hear how you’re using them. Try it out in your environment, share what’s working (and what isn’t), and let us know what you’d like to see next.Introducing the next generation of SOC automation: Sentinel playbook generator
Security teams today operate under constant pressure. They are expected to respond faster, automate more, and do so without sacrificing precision. Traditional security orchestration, automation and response (SOAR) approaches have helped, but they still rely heavily on rigid templates, limited action libraries, and workflows stretched across multiple portals. Building and maintaining automation is often slow and constrained at exactly the time organizations need more flexibility. Something needs to change – and with the introduction of AI and coding models the future of automation is going to look very different than it is today. Today, we’re introducing the Microsoft Sentinel playbook generator (now Generally Available), a new way to design code-based playbooks using natural language. With the introduction of generative AI and coding models, coding itself is becoming democratized, and we are excited to bring these new capabilities into our experience. This release represents the first milestone in our next‑generation security automation journey. The playbook generator allows users to design and generate fully functional playbooks simply by describing what they need. The tool generates a Python playbook with documentation and a visual flowchart, streamlining workflows from creation to execution for greater efficiency. This approach is highly flexible, allowing users to automate tasks like team notifications, ticket updates, data enrichment, or incident response across Microsoft and third-party tools. By defining an Integration Profile (base URL, authentication, credentials), the playbook generator can create API calls dynamically without needing predefined connectors. The system also identifies missing integrations and guides users to add them from the Automation tab or within the authoring page Users especially value this capability, allowing for more advanced automations. Playbook creation starts by outlining the workflow. The playbook generator asks questions, proposes a plan, then generates code and documentation once approved. Users can validate playbooks with real alerts and refine code anytime through chat instructions or manual edits. This approach combines the speed of natural language with transparent code, enabling engineers to automate efficiently without sacrificing control or flexibility. Preview customers report that the playbook generator speeds up automation development, simplifies automations for teams, and enables flexible workflow customization without reliance on templates. The playbook generator focuses on fast, intuitive, natural‑language‑driven automation creation, supported by a powerful coding foundation. It aligns with how security teams want to work: flexible, integrated, and deeply customizable. We’re excited to see how customers will use this capability to simplify operations, eliminate repetitive work, and automate tasks that previously demanded deep engineering effort. This marks the start of a new chapter, as AI continues to evolve and reshape what’s possible in security automation. How to get started With just a few prerequisites in place, you can begin creating code‑based automations through natural‑language conversations, directly inside the Microsoft Defender portal. Here’s a quick guide to help you move from first steps to your first generated playbook: 1. Make sure the prerequisites are in place Before you open your first chat in the playbook generator, the AI coding agent behind the playbook generator, confirm that your environment is ready: Security Copilot enabled: Your tenant must have a Security Copilot workspace, configured to use a Europe or US-based capacity. Sentinel workspace in the Defender portal: Ensure your Microsoft Sentinel workspace is onboarded to the Microsoft Defender portal. 2. Ensure you have the right permissions To build and deploy generated playbooks, make sure you have the same permissions required to author Automation Rules—the Microsoft Sentinel Contributor role on the relevant workspaces or resource groups. 3. Configure your integration profiles Integration profiles allow the playbook generator to create and execute any dynamic API calls—one of the most powerful capabilities of this new system. Before you create your first playbook: Go to Automation → Integration Profiles in the Defender portal. Create a Graph API Integration Create Integration to the services you want to have in the playbook (Microsoft Graph, ticketing tools, communication systems, third‑party providers, or others). Provide the base URL, authentication method, and required credentials. 4. Create your first generated playbook From the Automation tab: Select Create → Generated Playbook. Give your playbook a name. 3. The embedded Visual Studio Code window opens— Start in plan mode by simply describing what you want your automation to do. Be explicit about: What data to extract What actions to perform Any conditions or branches Example prompt you can use: “Based on the alert, extract the user principal name, check if the account exists in Entra ID, and if it does, disable the account, create a ticket in ServiceNow, and post a message to the security team channel.” The playbook generator will guide the process, ask clarifying questions, propose a plan, and then—once approved—switch to Act mode to generate the full Python playbook, documentation with a visual flow diagram, and tests. Completing your first playbook marks the beginning of a more intuitive, responsive, and intelligent automation experience—one where your expertise and AI work side by side to transform how your SOC operates. This is more than a new tool; it’s a foundation that will continue to evolve, adapt, and empower defenders as security automation enters its next era. Watch a demo here: https://aka.ms/NLSOARDEMO For deeper guidance, advanced scenarios, and end‑to‑end instructions, you can explore the full playbook generator documentation: Generate playbooks using AI in Microsoft Sentinel | Microsoft LearnThe Fileless Paradox: How My 33-Day-Old Research Became Today's Ransomware Reality
33 Days Before BARADAI Emerged 🔴 Before You Read: What Is This Article About? This is the first article I have published on Microsoft Tech Community, and this is not a standard threat report. This is the story of being right before anyone believed it — and of a ransomware family called BARADAI that proved it. On April 5, 2026, I published a technical research article documenting, in detail, a fileless malware architecture that operated entirely in RAM using steganography and Windows Registry persistence. When I shared it on social media, the reactions were immediate and brutal: “A fileless payload cannot be persistent. If it leaves no trace on disk, it cannot survive a reboot.” “This technique is entirely theoretical. No real threat actor would ever use this in production.” “You cannot have persistence without leaving traces. Pick one.” And the most absurd ones: “Stop writing articles with AI.” “This level of technical detail is unrealistic — did AI generate this?” “Forensic artifacts cannot be erased. What kind of technique is this?” At that moment, I could not prove myself. I had a working proof-of-concept. I had built the architecture myself. The technical logic was sound. But I did not yet have a real-world threat actor using it in production. 33 days later, BARADAI appeared. And it used the exact same playbook I had written. This article is the first volume of the “We Saw It Coming” series. In this series, I correlate my independent research with emerging real-world threats, document technical overlaps, and provide actionable detection and defense guidance for Microsoft environments. Right now, I am actively trying to reverse and decrypt BARADAI. I do not yet have a definitive solution. But I am publishing this journey because my goal is to finalize a solution by collecting additional logs and intelligence. 📌 Table of Contents The Moment Nobody Believed 33 Days Later: Meet BARADAI The B-Family: Shared Infrastructure Ecosystem Side-by-Side: Technical Overlap Analysis Deep Dive: The Fileless Paradox — How Both Architectures Work The PAIDMEMES Anomaly: Forensic Residue Inside BARADAI My Technique vs BARADAI: Shared Technical Patterns Microsoft Sentinel Detection Rules (KQL) MITRE ATT&CK Mapping Decryption Research and My Current Approaches Defensive Recommendations Sources and References ------------------------------------------------------------------------------ 1. The Moment Nobody Believed April 5, 2026 — A Research Paper, a Community, and Silence On April 5, 2026, I published a detailed technical research article on Medium titled: “STEGOMALWARE — PNG Persistence Through Steganography and Windows Registry” The article documented a complete attack architecture that I designed and tested from scratch in a controlled laboratory environment. My core thesis was this: A fileless malware strain can achieve persistent, reboot-resilient execution without ever writing a malicious executable to disk — by hiding its payload inside the pixels of a PNG image using LSB steganography and leveraging the Windows Registry for persistence. I demonstrated this by building a keylogger. The architecture had four defining characteristics: Feature 1 — Fileless Execution (RAM-Only) The malicious payload never touches disk as an executable file. Instead, a small, “clean-looking” loader script extracts hidden code from the pixel data of a PNG image and executes it directly in RAM. No .exe, no .py, no .dll on disk. Traditional antivirus file-scanning mechanisms are effectively blind to this. Feature 2 — Registry-Based Persistence Contrary to critics claiming that fileless malware cannot survive reboots, the loader writes itself into the Windows Registry Run key: HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Run This means that every time Windows starts, the loader executes again, extracts the payload from the PNG, and runs it back in memory. The malware lives in the Registry — not on disk. Feature 3 — Process Masquerading I compiled the loader under the name svchost.exe and assigned it a Windows service icon. When viewed in Task Manager, it appeared indistinguishable from a legitimate Windows system process. Feature 4 — Self-Repair (Self-Integrity Check) The loader continuously validated both its Registry entry and its file copy. If an antivirus product deleted the file or removed the Registry entry, the loader detected the modification and restored itself during the next execution cycle. Feature 5 — Intelligent Data Collection The keylogger I built automatically embedded collected data into the pixels of a PNG image every 10 characters or every 30 seconds — whichever occurred first. After each cycle, it reset itself, cleared temporary memory artifacts, and initiated a fresh collection loop. This architectural design enabled the malware to remain undetected on a system for months. Because there was no ever-growing log file on disk — the data was continuously transferred into images. ------------------------------------------------------------------------------------------ The Reactions The reactions I received when sharing this research did not surprise me, but they disappointed me. Technical objections: “Fileless malware, by definition, cannot survive reboots. No disk means no persistence.” “Forensic evidence cannot be erased. This makes no technical sense.” “If you are writing to the Registry, then it is not truly fileless.” Personal attacks: “Stop writing with AI.” “If you can perform technical analysis this detailed, why has nobody heard of you before?” “Copied from AI — even the formatting looks AI-generated.” This feedback revealed two things: First, people fundamentally misunderstood the concept of fileless malware — they were confusing “fileless execution” with “leaving absolutely no traces anywhere.” The Registry is not a traditional file in the conventional sense, yet it remains a persistent storage mechanism resilient across reboots. Second, it demonstrated how easily independent researchers are dismissed. Research not published by a major corporation or university was automatically labeled “AI-generated” or “theoretical.” At that moment, I could not prove myself. 33 days later, BARADAI proved me right. ------------------------------------------------------------------------------ 2. 33 Days Later: Meet BARADAI May 5–8, 2026 — A New Threat Surfaces On May 5, 2026, researchers at PCrisk documented a new ransomware sample submitted to VirusTtl. On the same day, CYFIRMA’s underground forum monitoring team flagged it in their threat intelligence feeds. By May 8, CYFIRMA’s Weekly Intelligence Report had published the first structured analysis. The threat was named BARADAI — derived from the extension it appends to encrypted files: .BARADAI -------------------------------------------- What Is BARADAI? BARADAI is a Windows ransomware variant belonging to the MedusaLocker family. MedusaLocker has been active since late 2019 and remains one of the most prolific and long-lived ransomware-as-a-service (RaaS) operations in the threat landscape. BARADAI is a specific variant of the MedusaLocker v3 architecture — sometimes tracked in threat intelligence repositories as “BabyLockerKZ.” Detection names across major security vendors: Microsoft Defender: Ransom:Win64/MedusaLocker.MZT!MTB ESET: Win64/Filecoder.MedusaLocker.A Avast: Win64:MalwareX-gen [Ransom] Kaspersky: HEUR:Trojan-Ransom.Win32.Generic ------------------------------------------------------------ How Does It Operate? BARADAI follows a double-extortion model. Silent Phase (Reconnaissance) After initial access, BARADAI does not immediately begin encryption. Instead, it performs systematic reconnaissance: -Enumerates running processes -Maps network topology -Collects browser-stored credentials -Harvests session cookies and SSL certificates -Captures desktop screenshots -Exfiltrates collected data to attacker-controlled C2 infrastructure Encryption Phase After exfiltration is complete, BARADAI activates its cryptographic payload: -AES-256-CBC for file content encryption -RSA-4096 for key protection Extortion Phase A ransom note (read_to_decrypt_files.html or WHATS_HAPPEND.txt) is dropped into every encrypted directory. Victims are given a 72-hour deadline. If payment is not made before expiration, stolen data is published on the group’s Data Leak Site (DLS). ------------------------------------------------------------------- Confirmed Targeting as of May 2026 Geographies -United States -Brazil -France -Australia -Italy -Israel -Malaysia Sectors -Education -Manufacturing -Engineering -Retail -Logistics -NGOs Ransom Demand Range -USD $10,000 — $80,000 per incident (CYFIRMA, May 2026) ------------------------------------------------------------------ 3. The B-Family: Shared Infrastructure Ecosystem One of the most important findings that emerged during my analysis was this: BARADAI is not operating alone. Threat intelligence monitoring identified a cluster of MedusaLocker variants sharing: -The same naming conventions -Similar code architecture -And most critically — the same Tor-based infrastructure I named this cluster: “The B-Family” --------------------------------------------- Evidence of Shared Infrastructure The strongest evidence of coordination inside the B-Family is not behavioral similarity — it is shared infrastructure. BARADAI’s ransom note lists the following Tor hidden service for victim negotiations: t33zoj4qwv455fog7qnb2azi5xcdxkixughmmduzbw2rtdgryqfbh6id.onion This is identical to the Tor address listed as the Data Leak Site and file leak server for BAVACAI — independently verified by ransomware.live, which identified the server running NGINX 1.24.0. PCrisk’s BARADAI documentation also includes screenshots of the leak site using the filename prefix: bavacai- This is structural evidence confirming that the same backend infrastructure serves both variants. What This Means The B-Family is not a collection of copycat operations. It is a single operation — or a tightly coordinated RaaS affiliate ecosystem — using different “brand names” per campaign in order to complicate attribution, tracking, and law enforcement disruption. ----------------------------------------------------------- Known Victims (BAVACAI DLS — Shared Backend) As of May 8, 2026, the BAVACAI DLS listed 16 victims — all published simultaneously on May 5. ------------------------------------------------------------ 4. Side-by-Side: Technical Overlap Analysis This section is the core of the article. The table below correlates the exact techniques documented in my April 5, 2026 research with the verified BARADAI behaviors documented by CYFIRMA, PCrisk, and the broader MedusaLocker analysis corpus. The conclusion is direct and unavoidable: The architecture I built, tested, documented, and published in a controlled laboratory environment on April 5, 2026 — the same architecture the community dismissed as “theoretical,” “AI-generated,” and “impossible” — was operationalized by a real threat actor 33 days later. -------------------------------------------------------- 5. Deep Dive: The Fileless Paradox Let us settle the debate permanently. The Misconception: “Fileless Malware Cannot Be Persistent” The argument I repeatedly encountered was this: “If malware does not leave files on disk, it cannot survive a reboot because RAM is volatile.” Technically correct. Strategically incomplete. It is true that RAM-resident code disappears when the system powers off. However, persistence does not require the malicious payload itself to reside on disk. It requires a mechanism that re-executes the payload after reboot. Those are two different things. -------------------------------------------------------------- The Architecture: How It Actually Works ┌──────────────────────────────────────────────────────────┐ │ ATTACK ARCHITECTURE │ │ │ │ DISK (minimal footprint): │ │ ┌──────────────────────────────────────────────────┐ │ │ │ loader.exe (masquerading as svchost.exe) │ │ │ │ cover_image.png (contains hidden payload) │ │ │ └──────────────────────────────────────────────────┘ │ │ │ │ │ REGISTRY (persistence): │ │ │ ┌──────────────────────────────────────────────────┐ │ │ │ HKCU\...\Run\WindowsUpdateService │ │ │ │ → points to loader.exe │ │ │ └──────────────────────────────────────────────────┘ │ │ │ │ │ ON EVERY BOOT: │ │ │ Registry triggers → loader.exe executes → │ │ Reads PNG pixels → extracts payload → │ │ Loads into RAM → executes │ │ (No malicious .exe is ever written to disk) │ │ │ │ RAM (execution): │ │ ┌──────────────────────────────────────────────────┐ │ │ │ Keylogger / RAT / Ransomware module │ │ │ │ Executes entirely in memory │ │ │ │ Invisible to disk-based AV scanning │ │ │ └──────────────────────────────────────────────────┘ │ └──────────────────────────────────────────────────────────┘ Only the loader exists on disk — and the loader itself is a small, legitimate-looking executable without a malicious signature. The malicious payload lives in: -The pixel data of the PNG image (steganographically encoded) -RAM (during active execution) The Registry provides the trigger mechanism — not the payload itself. That was the exact distinction critics failed to understand. ------------------------------------------------------------------ Why It Evades Traditional Detection BARADAI’s Implementation BARADAI uses the same logical architecture at larger scale. The MedusaLocker v3 binary: - Achieves persistence via Registry Run Key: HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Run\BabyLockerKZ -Executes core ransomware logic in memory without writing recoverable payload components to disk -Uses Parent PID Spoofing (T1134.004) to appear as a child process of explorer.exe or svchost.exe -Restores itself through persistence mechanisms if binaries are deleted ------------------------------------------------------------------------------ 6. The PAIDMEMES Anomaly: Forensic Residue Inside BARADAI One of BARADAI’s most distinctive — and frankly bizarre — technical characteristics is its configuration and key storage mechanism. Unlike most ransomware variants that attempt to keep all cryptographic material exclusively in volatile memory, BARADAI writes directly into the Windows Registry under an extremely unusual hive: HKCU\SOFTWARE\PAIDMEMES\PUBLIC HKCU\SOFTWARE\PAIDMEMES\PRIVATE - HKCU\SOFTWARE\PAIDMEMES\PUBLIC stores the Base64-encoded RSA public key extracted from the malware configuration. - HKCU\SOFTWARE\PAIDMEMES\PRIVATE stores encrypted runtime state and configuration parameters required for persistence across multiple execution instances. ------------------------------------------- Why This Matters The PAIDMEMES Registry hive is not random — it serves a specific operational purpose. When BARADAI is launched with the -network flag (instructing it to encrypt network shares), it spawns a secondary instance of itself as a non-elevated process. By storing cryptographic keys and configuration inside the Registry, that secondary instance — even without administrative privileges — can access everything necessary to continue the attack. These two Registry artifacts represent your highest-confidence BARADAI detection signals: HKCU\SOFTWARE\PAIDMEMES (Key creation = active infection) HKCU\...\Run\BabyLockerKZ (Persistence = infection survived reboot) ------------------------------------------------------------ 7. My Technique vs BARADAI: Detailed Technical Similarities Now let us go deeper technically and explain why I believe I am one of the people closest to understanding BARADAI. 7.1 Payload Concealment: LSB Steganography My Technique I replaced the least significant bits (LSB) of RGB channels in PNG pixels with Base64-encoded keylogger payload bits. A 1/255 modification inside an 8-bit value is visually imperceptible to the human eye. In BARADAI The stegomalware technique forms the core of payload transportation. The same LSB logic applies: -No visible image corruption -No signature-based scanner triggers -Payload blended into image “noise” Shared Point Mathematically, it is the same approach. The only difference is scale: I concealed a keylogger. BARADAI conceals a ransomware module. -------------------------------------------------------- 7.2 Fileless + Registry: The “Impossible” Combination My Technique I registered my loader under: HKCU\...\Run\WindowsUpdateService Every time Windows booted, the loader executed, read the PNG, extracted the payload into RAM, and launched it. A .py file never existed on disk. In BARADAI HKCU\...\Run\BabyLockerKZ Exactly the same mechanism. Same Registry path. Same logic. Same “fileless yet persistent” paradox. ------------------------------------------------- Shared Point When critics claimed these two concepts could not coexist, they were wrong. Both BARADAI and I proved it. 7.3 Process Concealment: svchost.exe Masquerading My Technique I compiled the loader with PyInstaller under the name svchost.exe and assigned it a Windows service icon. Inside Task Manager, it appeared identical to a legitimate system process. In BARADAI BARADAI uses Parent PID Spoofing. Through Windows API manipulation, it makes execution appear as if initiated by svchost.exe or explorer.exe. EDR behavioral engines typically flag unknown processes performing system-level modifications. This technique bypasses those checks. Shared Point Same concealment strategy. Different implementation layer. 7.4 Timers and Silent Collection My Technique The keylogger embedded data into PNG images every 10 characters OR every 30 seconds — whichever occurred first. After each cycle: -Temporary memory artifacts were cleared -The process reset -No ever-growing log file existed on disk This is why antivirus products could not see it. This is why it could remain undetected for months. In BARADAI “Ghost Software.” After initial compromise, BARADAI does not immediately encrypt. It silently waits. Harvests credentials. Maps the network. Exfiltrates data. Encryption is the final signature. Shared Point Both architectures rely on a “silent hunter” model. I used 30-second image-based exfiltration loops. BARADAI remains dormant for days or weeks while collecting intelligence. The logic is identical. Only the timescale differs. ---------------------------------------------------------------- 7.5 Why I Believe I Am One of the People Closest to Solving BARADAI These similarities are not coincidence. They reflect the same technical mindset reaching the same solutions to the same problems. Because I built this architecture from scratch: -I understand its weak points — because I encountered the same weak points myself -I can reverse-engineer LSB steganography workflows — because I wrote the same algorithm -I understand Registry-based configuration logic — the PAIDMEMES hive pattern is familiar to me - I understand interruption points inside timer-based collection loops — because I built the same cycle architecture myself ------------------------------------------------------------------------------ 8. Microsoft Sentinel Detection Rules (KQL) The following Kusto Query Language (KQL) queries are designed for deployment in Microsoft Sentinel. They target specific behavioral artifacts associated with BARADAI and the broader MedusaLocker family. Deploy all three as scheduled analytics rules. Rule 1: PAIDMEMES / BabyLockerKZ Registry Artifact Detection High confidence. Detects exact forensic strings unique to MedusaLocker v3 / BARADAI. If This Rule Triggers The device is actively infected with BARADAI or the malware has successfully established persistence. Treat as a P1 incident. Immediately isolate the endpoint. Rule 2: Shadow Copy & Backup Deletion Chain Detection High confidence. Detects BARADAI’s recovery-destruction sequence. If This Rule Triggers A ransomware payload is actively preparing for encryption. This is your final detection window before data loss begins. Immediately isolate the affected endpoint and every reachable network share. Rule 3: EnableLinkedConnections — Network Share Privilege Escalation Detection Medium-High confidence. Detects BARADAI’s technique for accessing administrator-mapped network drives from non-elevated processes. If This Rule Triggers An attacker is preparing to encrypt network shares normally visible only to administrator-level processes. This is a pre-encryption lateral movement signal. ---------------------------------------------------------------- 9. MITRE ATT&CK Mapping ------------------------------------------------------------------------------ 10. Decryption Research and My Current Approaches Let me be completely transparent. Current status: There is no verified public decryptor available for BARADAI. -The No More Ransom project lists no decryptor for any MedusaLocker v3 / BabyLockerKZ variant -The AES-256-CBC + RSA-4096 implementation is mathematically sound -Historical decryptors existed only for significantly older MedusaLocker v1 and early v2 variants by exploiting key sanitization weaknesses in memory management -Those vulnerabilities were patched in v3 What We Know About the Encryption BARADAI uses intermittent encryption for large files: -Files larger than ~7.7MB are not fully encrypted -The malware encrypts 750KB, skips 250KB, encrypts another 750KB, and repeats This dramatically reduces encryption time while still rendering the file structurally unusable. --------------------------------------------------------------- What I Am Currently Researching I am currently analyzing the BARADAI binary from multiple angles: PRNG Weaknesses I am investigating the entropy source used during AES key generation. If the PRNG is insufficiently random, the effective key space may be reducible. Key Sanitization Behavior I am investigating whether AES keys remain in memory after usage. This weakness existed in MedusaLocker v1 and v2 and enabled historical decryptors. Although patched in v3, implementation mistakes remain possible. PAIDMEMES Registry Storage Analysis The PAIDMEMES hive stores runtime state. I am investigating whether this storage area contains recoverable cryptographic material. Registry-stored cryptographic data could provide a viable decryption foothold. Weaknesses in Intermittent Encryption The 750KB-encrypt / 250KB-skip pattern enables structural comparisons between encrypted and unencrypted regions. Known file formats (.docx, .xlsx, etc.) contain predictable header structures. This creates potential for partial known-plaintext attacks. ------------------------------------------------------------------------------ I will publish my findings in Vol.4 of this series regardless of the outcome. ------------------------------------------------- If You Are a BARADAI Victim -Do not pay the ransom until all alternatives are exhausted -Contact professional incident response services -Preserve all encrypted files and ransom notes — a future decryptor may eventually become available -Regularly monitor nomoreransom.org ---------------------------------------------------- 11. Defensive Recommendations Priority 1: Phishing-Resistant MFA (Against AiTM) Traditional MFA — push notifications, SMS codes, authenticator apps — can be defeated by AiTM reverse-proxy attacks. Deploy: -FIDO2 hardware security keys (YubiKey, etc.) -Windows Hello for Business These technologies cryptographically bind authentication tokens to the legitimate TLS session of the login portal. Stolen cookies become useless in separate sessions. ------------------------------------------------------- Priority 2: Eliminate RDP Exposure BARADAI’s primary initial access vector is exposed RDP on TCP 3389. -Disable Internet-facing RDP at the perimeter firewall -Enforce MFA + VPN for all remote administrative access -Implement account lockout policies and Network Level Authentication (NLA) Priority 3: Immutable Backups BARADAI deletes Volume Shadow Copies via vssadmin. Implement: -A 3–2–1 backup strategy with at least one offline/immutable copy -Azure Immutable Blob Storage (WORM) -Multi-user authorization for backup vaults -Monthly restoration testing --------------------------------------------- Priority 4: FSRM Canary Files Configure Windows File Server Resource Manager (FSRM): Immediately alert when files with extensions: .BARADAI .BAVACAI .BASANAI .BAGAJAI are created. Trigger automated scripts that: -Terminate the originating user session -Revoke network share access -------------------------------------------------- Priority 5: Deploy the Sentinel KQL Rules Above The three rules in Section 8 provide layered behavioral detection that signature-based tooling cannot replicate. Deploy them before an incident occurs. -------------------------------------------------------------------------- Priority 6: Zero Trust Architecture BARADAI’s EnableLinkedConnections Registry modification allows standard user processes to encrypt administrator-mapped drives. -Segment backup servers, Domain Controllers, and critical infrastructure -Require hardware-backed MFA for sensitive segments -Implement least privilege and Just-In-Time (JIT) administrative access with Azure PIM ------------------------------------------------------------------------ 📢 Call to Action: Collective Intelligence I started this research alone. But disrupting the impact of the B-Family requires collective effort. If your organization or threat-hunting operations have observed additional logs, unusual network traffic, or alternative steganographic payload samples associated with the B-Family (BARADAI, BAVACAI, BASANAI, etc.), do not remain silent. Data Sharing You may share anonymized IoCs or log artifacts with us. and Direct Contact If you have technically significant observations or findings related to BARADAI analysis, you can contact me directly through my Webex profile. Webex Contact - email address removed for privacy reasons Our collective security depends on the aggregation of these small signals. --------------------------------------------- Sources and References For technical verification and further investigation, refer to the following resources: Threat Intelligence & Ransomware Reports CYFIRMA: Weekly Threat Intelligence Report (2026–05–08) Ransomware.live: BAVACAI Group & DLS Infrastructure PCrisk: BAVACAI | BAGAJAI | BASANAI Analysis Technical Foundations & MITRE TTPs CISA: MedusaLocker Advisory (AA22–181A) Picus Security: MedusaLocker TTPs and Simulation Barracuda: GhostFrame Phishing Kit Spotlight (2025–12–04) Detection & Response Tools Microsoft Sentinel: Official Shadow Copy Deletion Analytics Rule GitHub (Bert-JanP): Hunting Queries and Detection Rules No More Ransom: Global Decryption Tools Repository Cassandra MARE Independent Research Deniz Tektek: Stegomalware & Fileless Persistence (2026–04–05) https://medium.com/@deniizz/stegomalware-steganografi-ve-windows-registry-ile-kalıcılık-sağlayan-png-01e50849a218 Cassandra Community: Initial BARADAI Analysis (2026–05–14) https://medium.com/@cassandracommunity/baradai-ransomware-hayalet-yazılım-ı-parçalarına-ayırıyoruz-0c04bb008f73 This article has been published strictly for defensive purposes. All described techniques have been analyzed within the context of threat detection and defense. This is my debut article on the Microsoft Tech Community. I am Deniz Tektek, a Red Team Operator, Cybersecurity Analyst, and Founder of the Cassandra community. My work focuses on the intersection of human psychology, IoT security, and the development of zero-trust local AI agents. This article, “The Fileless Paradox,” is the inaugural entry in my "We Saw It Coming" threat intelligence series, where I document technical overlaps between independent research and active real-world threats. What’s Next? Vol. 2: "Invisible Exfiltration" — Analyzing how BARADAI’s C2 hides in plain sight. Vol. 3: "The Human Gateway" — Why your MFA and AI-driven defenses are currently being bypassed. Vol. 4: "Cracking BARADAI" — My ongoing decryption research. Connect With Me If you want to discuss these findings, exchange logs, or collaborate on security research, please check my profile bio for contact information or connect with me via LinkedIn. I welcome all technical perspectives and peer reviews. My LinkedIn: https://www.linkedin.com/in/deniz-t-91166438a Deniz Tektek — May 2026 © Deniz Tektek & Cassandra — All Rights Reserved. Originally published on Microsoft Tech Community. Cross-posted on Medium.Microsoft Security Community Spotlight: Marcel Graewer
Globally, Marcel shares practical detection engineering insights on Microsoft Sentinel and Microsoft Defender XDR through forums and blog posts. Locally, he represents his employer in the IT-Security group of the Microsoft Business User Forum, where German companies using Microsoft technologies exchange real-world experience and expertise. The work Marcel values most is helping people enter the IT field. In Germany, "Fachinformatiker" is a recognized IT profession learned through a multi-year apprenticeship, and he is proud to have trained apprentices. He also serves as an examiner for the IHK (the German Chamber of Industry and Commerce), evaluating the final exams of these IT apprentices. This commitment also led him to support younger learners by teaching school cybersecurity classes and participating in Girls’ Day, where he introduced female students to the field. “I do this because most people don’t get an honest view of security work until much later in their education—if they see it at all. Showing someone early that this field is creative, varied, and genuinely interesting can change their path. Being part of that, even for a few people, means more to me than anything that fits neatly on a CV.” Let’s hear more from Marcel about his Microsoft Security Community and product paths. All responses to questions are direct quotes from Marcel. What do you find most rewarding about being a member of the Microsoft Security Community? The most rewarding part for me is how practical the exchange is. Microsoft security tooling moves fast - Microsoft Sentinel, Microsoft Defender XDR and Microsoft Security Copilot all change month to month- and no single person keeps up with all of it alone. The community is where that gap gets closed. When I read how someone else tuned a detection in their environment, or when someone responds to something I posted with a problem I hadn't considered, my own work gets better. It's a feedback loop you don't get from documentation. The other part I value is that it works in both directions: I started as a reader, learning from people more experienced than me, and now I'm at a point where I can give some of that back. Watching that shift happen has been genuinely motivating. How long have you been working with Microsoft Security Products? Over ten years! My way into Microsoft security ran through infrastructure rather than security itself. I started out administering Active Directory and VMware environments, the on-premises world, and that is where I first understood identity, endpoints and the quiet attack surface they create. At the time, security was something layered on top of infrastructure. What changed everything was the shift to the cloud. As the environments I worked in moved into Microsoft Azure and Microsoft 365, the old separation between "running things" and "securing things" stopped making sense. In a cloud-first world, the identity is the perimeter, the sign-in log is the crime scene, and the telemetry that used to be scattered across servers suddenly lives in one place you could actually query. That was the moment Microsoft's security stack became less of a product set and more of a working environment for me. As I moved from running infrastructure into roles centered on defending it, first leading IT infrastructure and security as a team lead, then as an IT Security Expert, and now as IT Security Manager focused on architecture and incident response in an Azure and M365 environment, Sentinel and Defender XDR went from tools I knew of to tools I work in every day. The infrastructure background turned out to be an advantage rather than a detour. Detection engineering makes far more sense once you have run the Active Directory and the endpoints that generate the very signals you are now writing detections against, and cloud security makes far more sense once you have felt the limits of the on-premises model it replaced. The part that keeps me engaged is that none of this stands still. The cloud security landscape changes constantly, the work is never quite finished, and that is exactly what I like about it. What Microsoft Security features or products have provided the most impact? The single biggest impact for me comes from Microsoft Sentinel as a cloud-native SIEM and SOAR platform. The move away from a self-hosted SIEM matters more than it first appears. A traditional SIEM is itself a piece of infrastructure that has to be sized, hosted, patched, and scaled, and that effort constantly competes with the actual security work. Microsoft Sentinel removes that layer. There is no platform estate to keep alive and no capacity planning for the SIEM itself, which frees attention for what actually matters: getting the right telemetry in and getting detection and response right. What I value most is how naturally Sentinel fits into modern, cloud-first environments. When the landscape you are protecting already lives in Azure and Microsoft 365, a security platform that lives in the same place removes an entire class of integration friction. The other strength is the breadth of data onboarding. With a traditional SIEM, connecting a new log source was often a small project of its own, with connectors to build and parsers to maintain. With Sentinel, that friction is largely gone. Whether a source sits on-premises, in another cloud or in a third-party product, getting it in is straightforward, and the platform still provides the integration depth that genuinely matters rather than a shallow connection. Microsoft Sentinel handles almost anything you point it at. Equally important is that SIEM and SOAR are not two separate platforms here. The orchestration and automation layer is built into the same solution, so response playbooks run on the same data that the detections are built on. For architecture, that is a real advantage: detection and response are designed as one system rather than stitched together afterwards. The central telemetry layer is one of the few decisions that is genuinely hard to reverse later, and Sentinel makes that an easy one to defend. What advice do you have for others who would like to get involved in the Microsoft Community? My advice is to start before you feel ready. I read Microsoft Tech Community (forums) for years before I posted anything myself, always with the feeling that I needed more experience first, that I would just be adding noise. That was the wrong instinct. The moment I actually started contributing, the feedback I got back made my own work better, and I realised the bar for being useful is far lower than it looks from the outside. You do not need to be the leading expert on a topic. You need a real problem you have worked through and the willingness to write down how you solved it. Someone else is stuck on exactly that problem right now. Start small, stay consistent, and treat the community as an exchange rather than a stage. Consistency matters more than any single brilliant post. Alles rund um sein Buch (All About His Book) Last year, I published "Die neue Realität der Cybersecurity" (2025). It tackles a question every security team is dealing with right now: “Where does AI genuinely strengthen security architecture and incident response, and where is it just noise?” Rather than staying abstract, the book takes the practitioner's side of that question, looking at how AI actually changes the work of designing defensible systems and responding to incidents, and where the limits and risks really are. It is written for the people doing the work, security architects, IR practitioners and the leaders who have to make decisions about AI without the marketing gloss. If that question is on your desk too, it is worth a look. Connect with Marcel Microsoft Tech Community: @marcel_graewer Linkedin: https://www.linkedin.com/in/mgraewer/ Github: https://github.com/bifrost0x Blogs: graewer.com and magra-sec.de Book: Die neue Realität der Cybersecurity (ISBN: 9783695708833) Marcel Graewer is currently an IT-Security Manager at Festool Group and holds the CISSP certification. Outside of work, he is happiest when experimenting with technology on his own terms. He runs a Proxmox-based homelab with a range of self-hosted services and Docker containers, using it as both a playground and a testing ground. It gives him space to break things, learn, and explore without the constraints of formal change processes. He also spends time on Hack The Box and TryHackMe, believing that staying sharp on the offensive side makes him a stronger defender. Away from the keyboard, his life is refreshingly analog. He and his family, including two children, live in an old house that always seems to have one more project waiting. Between the homelab and the house, there is never a shortage of things to fix, and that suits him just fine. Learn and Engage with the Microsoft Security Community Log in and follow this Microsoft Security Community Blog. Follow = Click the heart in the upper right when you're logged in 🤍. Join the Microsoft Security Community and be notified of upcoming events, product feedback surveys, and more. Get early access to Microsoft Security products and provide feedback to engineers by joining the Microsoft Security Advisors. Join the Microsoft Security Community LinkedIn Group and follow the Microsoft Entra Community on LinkedIn
How Granular Delegated Admin Privileges (GDAP) allows Sentinel customers to delegate access
Simplifying Defender SIEM and XDR delegated access As Microsoft Sentinel and Defender converge into a unified experience, organizations face a fundamental challenge: the lack of a scalable, comprehensive, delegated access model that works seamlessly across Entra ID and Sentinel’s Azure Resource Manage creating a significant barrier for Managed Security Service Providers (MSSPs) and large enterprises with complex multi-tenant structures. Extending GDAP beyond CSPs: a strategic solution In response to these challenges, we have developed an extension to GDAP that makes it available to all Sentinel and Defender customers, including non-CSP organizations. This expansion enables both MSSPs and customers with multi-tenant organizational structures to establish secure, granular delegated access relationships directly through the Microsoft Defender portal. This is now available in public preview. The GDAP extension aligns with zero-trust security principles through a three-way handshake model requiring explicit mutual consent between governing and governed tenants before any relationship is established. This consent-based approach enhances transparency and accountability, reducing risks associated with broad, uncontrolled permissions. By integrating with Microsoft Defender, GDAP enables advanced threat detection and response capabilities across tenant boundaries while maintaining granular permission management through Entra ID roles and Unified RBAC custom permissions. Delivering unified management of delegated access across SIEM and XDR With GDAP, customers gain a truly unified way to manage access across both Microsoft Sentinel and Defender—using a single, consistent delegated access model for SIEM and XDR. For Sentinel customers, this brings parity with the Azure portal experience: where delegated access was previously managed through Azure Lighthouse, it can now be handled directly in the Defender portal using GDAP. More importantly, for organizations running SIEM and XDR together, GDAP eliminates the need to switch between portals—allowing teams to view, manage, and govern security access from one centralized experience. The result is simpler administration, reduced operational friction, and a more cohesive way to secure multi-tenant environments at scale. How GDAP for non-CSPs works: the three-step handshake The GDAP handshake model implements a security-first approach through three distinct steps, each requiring explicit approval to prevent unauthorized access. Step 1 begins with the governed tenant initiating the relationship, allowing the governing tenant to request GDAP access. Step 2 shifts control to the governing tenant, which creates and sends a delegated access request with specific requested permissions through the multi-tenant organization (MTO) portal. Step 3 returns to the governed tenant for final approval. The approach provides customers with complete visibility and control over who can access their security data and with what permissions, while giving MSSPs a streamlined, Microsoft-supported mechanism for managing delegated relationships at scale. Step 4 assigns Sentinel permissions. In Azure resource management, assign governing tenant’s groups with Sentinel workspaces permissions (in the governed tenant), selecting the governing tenant’s security groups used in the created relationship. Learn more here: Configure delegated access with governance relationships for multitenant organizations - Unified se…
