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What's new in Defender for Cloud?
Defender for Cloud is now integrated into the Defender portal to bring together cloud security posture management and threat protection in a single experience. Rea...
Jun 04, 2026
Use the new Operations Guide to adopt alert-first workflows, standardize operations, and run Global Secure Access more reliably every day.
Jun 04, 2026
10 MIN READ
As enterprises adopt Microsoft Azure for large‑scale and regulated workloads, security architecture must be driven by traffic trust boundaries and inspection intent, not connectivity alone. Regulator...
Jun 03, 20264 MIN READ
Why hardened images are becoming the new baseline for container image security
Container security is evolving beyond vulnerability scanning alone. Across the ecosystem - spanning container platfor...
Jun 02, 2026Recent Discussions
I just want to secure AI. DLP vs Info Protection vs DSPM vs Governance vs...
I'm with an MSP, and I've avoided Purview like the plague, because it seems to be suffering from the same 'made by marketing teams' 'strategy' the 365 documentation is. However, it's my understanding Purview policies are needed for Data control of Copilot. Here's my issue: all of these different 'solutions' sound like the exact same thing, but are pitched as if they are something different. i'm going to post a couple of descriptions for these 'solutions' to illustrate this. 'discover, label, and protect sensitive and business-critical info' 'make sure your organization can identify, monitor, and protect sensitive info across the expanding Microsoft 365 landscape' 'discover and secure all your sensitive data across Microsoft 365 and non-365 data sources' 'Discover, label, and protect sensitive and business-critical info across your multicloud data estate.' I genuinely do not have time to figure out what each of these 'solutions' are, then figure out their policies, then their giant library of settings (below)... It's not even clear to me what's active NOW, considering we never licensed Purview - but somehow have been roped into it. It SEEMS like these are all variations of marketing terms, which all point to 3-4 actual technical implementations in obscure ways. Can someone advise on the ACTUAL technical policies we want to target and enable? Or just give some clarity? I've never felt so overwhelmed or disconnected from Microsoft's environment. We just want to secure our tenant's AI usage.
Prompted to sign in to Microsoft Defender Platform on W11/W2025 using Entra
Hi Microsoft Defender XDR community, Since around May 18th, our users on devices that are onboarded to Microsoft Defender for Endpoint are being prompted to sign-in to the following application using Entra on login to Windows. Application Microsoft Defender Platform Application ID cab96880-db5b-4e15-90a7-f3f1d62ffe39 Is anyone aware of a change that requires user sign-in to Entra as a requirement for Microsoft Defender for Endpoint? I have tried raising a support topic on this topic. Regards ChrisOperational 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, AccountDisplayName
No way to automate restoring user‑reported emails after “no threats found”
When a user reports an email as phishing in Defender, the message gets moved to Deleted Items. After we triage it, if we mark it as “no threats found,” there’s no way to push it back to the user’s inbox as part of that workflow. That creates a bit of a broken experience: User is told the email is safe with our customized email response, but has to go find it themselves In a lot of cases they don’t (Outlook search won’t find it) We end up with follow‑ups like “where did it go?” Technically we could restore the email as part of our triage process, but that just shifts the effort onto the SOC. It doesn’t scale, and it’s not really the right place for that work. We have tried to create an automation to do this, but we have not been able to create an advanced hunting query based on our triage result that can then trigger an action to restore it to the mailbox. So we end up choosing between: Users having a bad experience, or Analysts doing manual mailbox work Neither is ideal. Other platforms (like Proofpoint) handle this end‑to‑end — once something is confirmed clean, it can be returned to the user automatically. Right now Defender stops at classification instead of completing the workflow. Is there a reason this isn’t wired in, or anything on the roadmap to address it?
June 4 - Secure Boot AMA
Microsoft is updating the Secure Boot certificates originally issued in 2011 to ensure Windows devices continue to verify trusted boot software. These older certificates begin expiring in June 2026. Devices that haven’t received the newer 2023 certificates will continue to start and operate normally, and standard Windows updates will continue to install. However, these devices will no longer be able to receive new security protections for the early boot process, including updates to Windows Boot Manager, Secure Boot databases, revocation lists, or mitigations for newly discovered boot level vulnerabilities. Whether you are already working through Secure Boot certificate updates across your estate, or aren't sure where to start, you can get answers to your questions and helpful insights at the next Secure Boot AMA on 8:00 a.m. PDT June 4, 2026. Can't attend live? No problem. Post your questions in advance. Visit https://aka.ms/AMA/SecureBoot to save the date and post your questions. For detailed, step-by-step guidance, see the following resources: Secure Boot Playbook for Windows client Secure Boot playbook for Windows Server Secure Boot Certificate Updates for Windows 365 Secure Boot Certificate Updates for Azure Virtual Desktop
SentinelHealth: Scheduled Rule Retry Logging Does Not Match Docs
## Objective I am working on a health checks architecture for Microsoft Sentinel analytic rules. The goal is to build a set of monitoring queries/approaches that cover rule execution failures, configuration issues (entity mapping, partial success), rule audit tracking, and auto-disabled rule detection. ## My Current Approach So far I have built monitoring for the following areas using the SentinelHealth and SentinelAudit tables: - Scheduled rule window failures (retry exhaustion) - NRT rule execution delays (cumulative delay over 25 minutes) - Partial success and configuration issues (entity mapping drops, alert size limits, semantic errors) with transient error codes filtered out - Auto-disabled rules detection - Rule disable/delete audit tracking via SentinelAudit + AzActivity ## The Issue: Scheduled Rule Retry Logging The documentation at https://learn.microsoft.com/en-us/azure/sentinel/monitor-analytics-rule-integrity#scheduled-rules states that when a scheduled rule fails, it is retried 5 more times on the same window (6 total attempts). It also provides this query to detect completely skipped windows: ```kql _SentinelHealth() | where SentinelResourceType == @"Analytics Rule" | where SentinelResourceKind == "Scheduled" | where Status != "Success" | extend startTime = tostring(ExtendedProperties["QueryStartTimeUTC"]) | summarize failuresByStartTime = count() by startTime, SentinelResourceId | where failuresByStartTime == 6 | summarize count() by SentinelResourceId ``` This query assumes that each retry attempt is logged as a separate event in SentinelHealth, all sharing the same QueryStartTimeUTC. You would then count 6 failure records per startTime to identify a fully skipped window. However, in practice I am seeing different behavior. I ran a diagnostic query with a 90-day lookback (480 non-success events total, 73 unique rules). Every single event had a count of 1 per unique (SentinelResourceName, startTime) combination. No grouping of retries was observed at all. I then found an actual failed-window event that confirms this. Here is the record: - Rule: Port scan detected (ASIM Network Session schema) - Status: Failure - Description: "Rule's scheduled run at 06/01/2026 10:43:55 failed after numerous attempts. It will be re-executed over the next scheduled time." - Issue Code: SemanticErrorInQuery - Only 1 SentinelHealth record exists for this failed window The Description field says "failed after numerous attempts" which indicates the retries happened internally, but only one consolidated Failure event was written to SentinelHealth after all retries were exhausted. The individual retry attempts do not appear as separate records. This means the failuresByStartTime == 6 query from the documentation would never match this pattern, because there is only 1 record per failed window, not 6. ## Why This Matters Yes, completely skipped windows are rare. In my 90-day dataset most failures were permanent types (SemanticErrorInQuery, QueryGeneralError) that would not benefit from retries anyway. But they still happen, and if a tenant experiences a transient issue that causes a higher rate of failed windows, the documented query would silently return nothing. For my health checks I have rewritten the detection to simply look for Status == "Failure" with Description containing "failed after numerous attempts" which matches the actual consolidated event Sentinel writes. ## Questions Is the documented failuresByStartTime == 6 query still accurate? Or has the retry logging behavior changed to write a single consolidated event per failed window? Are there specific failure types or conditions where individual retries are logged as separate events? Perhaps transient failures behave differently from permanent ones in this regard? For anyone else building health monitoring on SentinelHealth - am I missing any important use cases beyond what I described above? Any clarification would be appreciated.
Data System Wide Lineage via API Request
I'm struggling with finding a solution. My goal is to identify all existing lineage relationships for any data objects within a specific data system they belong to. I've been using the Purview REST API (Datamap Dataplane) but I haven't found an endpoint returning data system side lineage/relationships. For my scenario I have a Databricks metastore and need to know the existing lineage relationships of those data objects within Purview so I can purge them out when we are doing our scheduled lineage refresh.
Why “Data in Switzerland” Is Not Enough
Moving from Residency to Control in Microsoft 365 Every conversation about data sovereignty in regulated industries tends to start the same way: “We use Multi-Geo. The data stays in Switzerland.” It’s the right starting point. Microsoft 365 Multi-Geo allows organizations to place selected workloads - SharePoint sites, OneDrive accounts, Teams data, or Exchange mailboxes - into specific regions, including Switzerland, while maintaining a single global tenant. This makes it possible to align sensitive data with regulatory or customer requirements without fragmenting the overall environment. But it only answers one question: Where is the data stored? It does not answer who accessed the data, from where, under which conditions, or what happened after access. That is where the real problem begins. A scenario that happens every day A Swiss engineering firm stores sensitive project documentation in Switzerland using Multi-Geo. An external contractor - working from an unmanaged device outside Switzerland - is granted access to review a file. The document opens. The data is now on a screen in an unknown location, on a device with no compliance posture, in a session with no restrictions. From the platform’s perspective, residency was enforced. From a sovereignty perspective, control was lost the moment access was granted without conditions. The file never left Switzerland. But sovereignty did. Residency is static. Control is not. The moment a document is opened, storage location stops being the relevant boundary. The file is no longer just “in Switzerland.” It moves instantly across endpoints and browsers, collaboration tools like Teams, external users and partners, and increasingly AI-driven contexts. The infrastructure remains unchanged. The data does not. From the platform’s perspective, everything is working as designed - access was granted, residency was enforced - and control was lost. Most “data in Switzerland” strategies fail at exactly this moment: when the data is used. The shift: from location to conditions If data sovereignty is the goal, the question must change. Not “Where is the data stored?” but: Under which conditions can data be accessed and used? This shift fundamentally changes the architecture. Control must be applied across three distinct layers - and all three must be connected. Layer 1: Access is conditional, not static Conditional Access extends control beyond authentication and turns it into continuous evaluation. Access decisions can depend on: Device compliance Location (geo-restriction) Identity and risk signals Multi-Geo ensures data is placed correctly. Conditional Access ensures it is reachable only under defined conditions. The two must work together - residency without access governance is an incomplete control. Layer 2: The session is the real risk surface Even with strict access controls, risk remains. A session is an exposure surface by design. During an active session, data is viewed, copied, shared, processed by applications, and connected to AI prompts. The gap does not appear at storage or authentication. It appears during active usage - inside the session. This is the layer most architectures do not explicitly address. Controls must extend into the session itself: limiting data transfer and replication, restricting interaction patterns, and enforcing policies in real time. Access is no longer a one-time event. It becomes continuously governed. This becomes even more critical as AI assistants consume content across SharePoint, Teams, Exchange, and other Microsoft 365 services. The question is no longer only where the source document resides - but whether the AI interaction itself is governed by the same access and protection controls as direct access. Layer 3: The document becomes the control point The most durable control does not sit in the network or in the session. It sits in the data itself. In regulated industries, organizations often arrive at this architecture having first evaluated sovereign or national encryption solutions. The decision to rely on native Microsoft 365 Purview encryption rather than a separate layer comes down to integration: AES-256 protection operating natively at file, user, and SharePoint level - including geo-based access restrictions - without an additional system to maintain. When protection is applied directly to the document through Microsoft Purview: Sensitivity labels define classification - automatically assigned based on content Encryption enforces access - AES-256, bound to the file itself IRM controls usage - view, copy, print, share, and presentation rights DLP governs movement across services - preventing data from leaving defined boundaries Dynamic watermarking tracks exposure - applied on open, view, or print At that point, access is enforced by the file, usage restrictions travel with it, and control persists regardless of location. The document becomes the perimeter. Platform control: limiting provider access One dimension often overlooked in sovereignty discussions is platform access itself. Even a perfectly configured tenant is only as sovereign as the controls placed on the operator. Customer Lockbox ensures that even Microsoft support cannot access customer data without explicit, logged, time-bound approval. Every access request is visible, auditable, and subject to customer veto. Data control applies not only to users - but also to the platform operating the service. Enforcement requires an integrated architecture Most organizations already have the required capabilities: Multi-Geo, Conditional Access, session control, Purview (labels, encryption, DLP, IRM), and monitoring. The issue is not capability. It is fragmentation. In practice, fragmentation looks like this: residency is configured in one project, Conditional Access policies are managed by a different team, and Purview labels were applied during a compliance initiative that never connected to the access layer. The tools exist. The signals do not flow between them. When designed as a single architecture: Data is placed intentionally - residency aligned to regulatory requirements Access is governed by context - device, location, and identity evaluated continuously Usage is controlled dynamically - session-level restrictions enforced in real time Protection is embedded in the document - encryption and IRM travel with the file Signals are connected across the platform - monitoring feeds access policy, not just audit logs “Data in Switzerland” becomes not just a statement - but an enforceable system property. Closing thought Placing data in Switzerland is the right first step. Multi-Geo makes it possible, even in global environments. But residency alone is not control. Data residency answers where information is stored. Data sovereignty requires proving who can access it, under which conditions, and what controls remain in place after access is granted. In Microsoft 365, sovereignty is no longer defined by geography alone. It is defined by the ability to enforce control wherever the data travels.Lakebase connector
I'm requesting a native Purview connector for Databricks Lakebase. Although Lakebase reached general availability in January, no dedicated connector currently exists. As a workaround, we're scanning Lakebase Postgres tables as generic PostgreSQL sources, but this approach only supports username/password authentication and prevents us from using service principals—a requirement for our security posture. I believe that Lakebase volume will grow and it would be a great add on.
Understanding AI workloads on Linux
Hi everyone, I’m a PM working on security for Linux environments and trying to better understand how AI workloads are actually showing up in production today. Would appreciate hearing from folks here: Are you running any AI workloads on Linux today? Or actively exploring? What does your deployment/setup look like — e.g., model training/inference, agents, MCP servers, data pipelines, etc.? How are you thinking about securing this stack, if at all? If you’re open to a quick 30-min chat, I’d love to learn more from your experience as well. Thanks in advance — this will directly help shape where we invest next.Passkey Sign in Method (Entra Account) missing in Security
Hi Microsoft Support we enable FIDO2 passkey in entraId. However, when we try to register the FIDO2 passkey on myaccount.microsoft.com -> Security -> Add a Sign-in Method -> Passkey is missing. Attached screenshot. For a personal account, the Passkey method is available at the same location, even though interface is slightly different than an Entra Id account. Attached screenshot for the personal account as well. Kindly guide us on where to register the passkey or if we need to enable certain settings in EntraId for the passkey to show up in sign-in methods. We have Auth Strengths enabled in EntraId for the particular user in question and this reflects in the Device Lockscreen during login on Entra Registred Device. Thanks ChandraPerformance in scanning
We are trying to search for CUI data on internal file stores. Last week, I decided to run another discovery scan, this time using ALL instead of Policy Only. It took much longer and left the scanner server in an almost unusable state and didn’t give really any more information than the first one did. Based on my research, we need to define and set the policy before we run scans. This is the information tip from the Purview scanner settings: Scan started at: 2026-05-20 22:54:06Z Scan ended at: 2026-05-24 16:16:51Z Scan duration: 3 days, 17 hours, 22 minutes, 45 seconds Scan id: 93acb922-e2ac-4fb7-b259-d6184e7aa434 Repository: \\cab-filesrv-01.fg.com\Departments. Enforce mode is Off Scanned files:3509640 Actions: Classified:3369456 Classified as Public:14 Classified as Fg Private:3369442 Labeled:0 Remove label:0 Protected:0 Remove protection:0 Files with matched information types:572895 Skipped due to - No match:0 Skipped due to - Not supported:0 Skipped due to - Already labeled:0 Skipped due to - Already scanned:0 Skipped due to - Require justification:0 Skipped due to - Unknown reason:0 Skipped due to - Excluded:98833 Skipped due to - Attribute:0 Failed:41318Fido passkeys blocked by policy
Hi all I'm helping out a customer with deploying physical passkeys and I'm running into a weird error. I've activated the sign in method and selected the two AAGuids for the Authenticator app and I've added the right AAGuid for the brand and model of passkey we are using. We can select the authentication method and enroll the security correctly but when trying to sign in using it we get the error as displayed in the attached picture. When checking the sign in logs i get this error message FIDO sign-in is disabled via policy and the error code is: 135016 I've not been able to track down any policy that would be blocking passkeys. anyone got any ideas?The 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.Entra ID Governance vs Saviynt for SAP IGA Use Cases
Hi everyone, We are currently evaluating Microsoft Entra ID Governance as a potential replacement for Saviynt for SAP-focused IGA requirements across a mixed SAP landscape, including: SAP SuccessFactors SAP Concur SAP S/4HANA Private Cloud Other SAP SaaS and enterprise applications I wanted to get insights from anyone who has implemented or worked extensively with Entra Governance in SAP-centric environments, specifically around the following areas: 1. Birthright RBAC Provisioning Can Entra Governance provision a single composite/business role (similar to Saviynt Enterprise Roles) through HR-driven JML events? For example: HR event triggers provisioning User automatically receives bundled SAP access/business roles Role assignment follows birthright/access package logic How mature/scalable is this approach in Entra compared to Saviynt? 2. SoD (Segregation of Duties) Capabilities Saviynt supports preventative SoD checks directly during request submission, including SAP-specific SoD analysis. Questions: Does Entra Governance support preventative SoD evaluation at request time? Can conflicts be surfaced before approval/provisioning? Is there native SAP SoD support or dependency on external tooling (for example SAP GRC/IAG)? Additionally, Saviynt supports granular SAP authorization object analysis down to field-level min/max values within SAP Private Cloud environments. Does Entra provide similar depth for SAP authorization analysis? 3. SAP Integrations / Connectors While Entra provides OOTB Enterprise Applications and provisioning connectors for SAP applications: What differences or limitations have you observed compared to Saviynt’s SAP connectors? How well does Entra handle SAP role imports, entitlement hierarchy, and provisioning workflows? Any known gaps for SAP Private Cloud integrations? Would appreciate any implementation experiences, architecture guidance, lessons learned, or recommendations from teams who have evaluated or deployed Entra Governance in SAP-heavy environments. Thanks in advance.Filtrer les résultats de la recherche des produits de données à l'aide des termes de glossaire
Bonjour, Je teste Microsoft Purview (Unified Catalog) avec des produits de données auxquels j’ai associé des termes de glossaire. Les termes de glossaire sont publiés et visibles dans l’onglet Découverte → Glossaire d’entreprise. Les produits de données sont également publiés et retrouvables via la recherche. Cependant, je ne vois pas d’option (ou elle ne retourne aucun résultat) pour filtrer les résultats de recherche des produits de données par termes de glossaire, contrairement à d’autres filtres disponibles (ex. Propriétaire, Type de produit). Est-ce que le filtrage des produits de données par termes de glossaire est supporté dans l’onglet Découverte ? Si oui, y a-t-il des pré-requis ou conditions particulières (ex. type de glossaire, indexation/délai, association au niveau data product vs assets, etc.) ?runHuntingQuery API and 'evaluate pivot'
Seem to have a problem where any request to the runHuntingQuery API with 'evaluate pivot' fails with error": { "code": "UnknownError", "message": "", Is this just a 'feature' ? The query happily runs trough the website/XDR portal. :-( Is there a way to simulate a pivot (easily) in powerapps ?
Has anyone else been experiencing frequent Chrome freezes lately?
I've noticed that Google Chrome occasionally becomes completely unresponsive on several Windows 11 devices that are Microsoft Entra ID joined. In some cases, the browser freezes to the point where users are unable to recover without performing a hard reboot of the device. Unfortunately, the issue tends to reoccur after some time, even after restarting the machine. Has anyone else encountered similar behaviour in a Windows 11 and Entra ID-joined environment? If so, were you able to identify the root cause or find a reliable fix?
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