Monitoring work from home
My boss has asked me if there is a way to see just how "busy" people who are working from home are. I have this data in Sentinel: Entra sign-in logs Defender for Endpoint logs Office 365 logs Most, if not all, on premise AD login events Netskope (current ZTNA solution) logs I have my known office location IPs so i could just exclude those and look for activity from other IPs however many times people will work in the morning or on the way to work appearing from a non corporate IP, come into the office appearing to come from a corporate IP, and then from home again in the evening. I need a way to query Sentinel looking for people who appear to be working but not coming from Corp IP. If they came from different IPs on the same day including Corp check to see if those non Corp are before and after business hours and exclude those. Anyone know of a good query to achieve this? Or maybe a tool that can extract and generate a report?Kerberos and the End of RC4: Protocol Hardening and Preparing for CVE‑2026‑20833
CVE-2026-20833 addresses the continued use of the RC4‑HMAC algorithm within the Kerberos protocol in Active Directory environments. Although RC4 has been retained for many years for compatibility with legacy systems, it is now considered cryptographically weak and unsuitable for modern authentication scenarios. As part of the security evolution of Kerberos, Microsoft has initiated a process of progressive protocol hardening, whose objective is to eliminate RC4 as an implicit fallback, establishing AES128 and AES256 as the default and recommended algorithms. This change should not be treated as optional or merely preventive. It represents a structural change in Kerberos behavior that will be progressively enforced through Windows security updates, culminating in a model where RC4 will no longer be implicitly accepted by the KDC. If Active Directory environments maintain service accounts, applications, or systems dependent on RC4, authentication failures may occur after the application of the updates planned for 2026, especially during the enforcement phases introduced starting in April and finalized in July 2026. For this reason, it is essential that organizations proactively identify and eliminate RC4 dependencies, ensuring that accounts, services, and applications are properly configured to use AES128 or AES256 before the definitive changes to Kerberos protocol behavior take effect. Official Microsoft References CVE-2026-25177 - Security Update Guide - Microsoft - Active Directory Domain Services Elevation of Privilege Vulnerability Microsoft Support – How to manage Kerberos KDC usage of RC4 for service account ticket issuance changes related to CVE-2026-20833 (KB 5073381) Microsoft Learn – Detect and Remediate RC4 Usage in Kerberos AskDS – What is going on with RC4 in Kerberos? Beyond RC4 for Windows authentication | Microsoft Windows Server Blog So, you think you’re ready for enforcing AES for Kerberos? | Microsoft Community Hub Risk Associated with the Vulnerability When RC4 is used in Kerberos tickets, an authenticated attacker can request Service Tickets (TGS) for valid SPNs, capture these tickets, and perform offline brute-force attacks, particularly Kerberoasting scenarios, with the goal of recovering service account passwords. Compared to AES, RC4 allows significantly faster cracking, especially for older accounts or accounts with weak passwords. Technical Overview of the Exploitation In simplified terms, the exploitation flow occurs as follows: The attacker requests a TGS for a valid SPN. The KDC issues the ticket using RC4, when that algorithm is still accepted. The ticket is captured and analyzed offline. The service account password is recovered. The compromised account is used for lateral movement or privilege escalation. Official Timeline Defined by Microsoft Important clarification on enforcement behavior Explicit account encryption type configurations continue to be honored even during enforcement mode. The Kerberos hardening associated with CVE‑2026‑20833 focuses on changing the default behavior of the KDC, enforcing AES-only encryption for TGS ticket issuance when no explicit configuration exists. This approach follows the same enforcement model previously applied to Kerberos session keys in earlier security updates (for example, KB5021131 related to CVE‑2022‑37966), representing another step in the progressive removal of RC4 as an implicit fallback. January 2026 – Audit Phase Starting in January 2026, Microsoft initiated the Audit Phase related to changes in RC4 usage within Kerberos, as described in the official guidance associated with CVE-2026-20833. The primary objective of this phase is to allow organizations to identify existing RC4 dependencies before enforcement changes are applied in later phases. During this phase, no functional breakage is expected, as RC4 is still permitted by the KDC. However, additional auditing mechanisms were introduced, providing greater visibility into how Kerberos tickets are issued in the environment. Analysis is primarily based on the following events recorded in the Security Log of Domain Controllers: Event ID 4768 – Kerberos Authentication Service (AS request / Ticket Granting Ticket) Event ID 4769 – Kerberos Service Ticket Operations (Ticket Granting Service – TGS) Additional events related to the KDCSVC service These events allow identification of: the account that requested authentication the requested service or SPN the source host of the request the encryption algorithm used for the ticket and session key This information is critical for detecting scenarios where RC4 is still being implicitly used, enabling operations teams to plan remediation ahead of the enforcement phase. If these events are not being logged on Domain Controllers, it is necessary to verify whether Kerberos auditing is properly enabled. For Kerberos authentication events to be recorded in the Security Log, the corresponding audit policies must be configured. The minimum recommended configuration is to enable Success auditing for the following subcategories: Kerberos Authentication Service Kerberos Service Ticket Operations Verification can be performed directly on a Domain Controller using the following commands: auditpol /get /subcategory:"Kerberos Service Ticket Operations" auditpol /get /subcategory:"Kerberos Authentication Service" In enterprise environments, the recommended approach is to apply this configuration via Group Policy, ensuring consistency across all Domain Controllers. The corresponding policy can be found at: Computer Configuration - Policies - Windows Settings - Security Settings - Advanced Audit Policy Configuration - Audit Policies - Account Logon Once enabled, these audits record events 4768 and 4769 in the Domain Controllers’ Security Log, allowing analysis tools—such as inventory scripts or SIEM/Log Analytics queries—to accurately identify where RC4 is still present in the Kerberos authentication flow. April 2026 – Enforcement with Manual Rollback With the April 2026 update, the KDC begins operating in AES-only mode (0x18) when the msDS-SupportedEncryptionTypes attribute is not defined. This means RC4 is no longer accepted as an implicit fallback. During this phase, applications, accounts, or computers that still implicitly depend on RC4 may start failing. Manual rollback remains possible via explicit configuration of the attribute in Active Directory. July 2026 – Final Enforcement Starting in July 2026, audit mode and rollback options are removed. RC4 will only function if explicitly configured—a practice that is strongly discouraged. This represents the point of no return in the hardening process. Official Monitoring Approach Microsoft provides official scripts in the repository: https://github.com/microsoft/Kerberos-Crypto/tree/main/scripts The two primary scripts used in this analysis are: Get-KerbEncryptionUsage.ps1 The Get-KerbEncryptionUsage.ps1 script, provided by Microsoft in the Kerberos‑Crypto repository, is designed to identify how Kerberos tickets are issued in the environment by analyzing authentication events recorded on Domain Controllers. Data collection is primarily based on: Event ID 4768 – Kerberos Authentication Service (AS‑REQ / TGT issuance) Event ID 4769 – Kerberos Service Ticket Operations (TGS issuance) From these events, the script extracts and consolidates several relevant fields for authentication flow analysis: Time – when the authentication occurred Requestor – IP address or host that initiated the request Source – account that requested the ticket Target – requested service or SPN Type – operation type (AS or TGS) Ticket – algorithm used to encrypt the ticket SessionKey – algorithm used to protect the session key Based on these fields, it becomes possible to objectively identify which algorithms are being used in the environment, both for ticket issuance and session establishment. This visibility is essential for detecting RC4 dependencies in the Kerberos authentication flow, enabling precise identification of which clients, services, or accounts still rely on this legacy algorithm. Example usage: .\Get-KerbEncryptionUsage.ps1 -Encryption RC4 -Searchscope AllKdcs | Export-Csv -Path .\KerbUsage_RC4_All_ThisDC.csv -NoTypeInformation -Encoding UTF8 Data Consolidation and Analysis In enterprise environments, where event volumes may be high, it is recommended to consolidate script results into analytical tools such as Power BI to facilitate visualization and investigation. The presented image illustrates an example dashboard built from collected results, enabling visibility into: Total events analyzed Number of Domain Controllers involved Number of requesting clients (Requestors) Most frequently involved services or SPNs (Targets) Temporal distribution of events RC4 usage scenarios (Ticket, SessionKey, or both) This type of visualization enables rapid identification of RC4 usage patterns, remediation prioritization, and progress tracking as dependencies are eliminated. Additionally, dashboards help answer key operational questions, such as: Which services still depend on RC4 Which clients are negotiating RC4 for sessions Which Domain Controllers are issuing these tickets Whether RC4 usage is decreasing over time This combined automated collection + analytical visualization approach is the recommended strategy to prepare environments for the Microsoft changes related to CVE‑2026‑20833 and the progressive removal of RC4 in Kerberos. Visualizing Results with Power BI To facilitate analysis and monitoring of RC4 usage in Kerberos, it is recommended to consolidate script results into a Power BI analytical dashboard. 1. Install Power BI Desktop Download and install Power BI Desktop from the official Microsoft website 2. Execute data collection After running the Get-KerbEncryptionUsage.ps1 script, save the generated CSV file to the following directory: C:\Temp\Kerberos_KDC_usage_of_RC4_Logs\KerbEncryptionUsage_RC4.csv 3. Open the dashboard in Power BI Open the file RC4-KerbEncryptionUsage-Dashboards.pbix using Power BI Desktop. If you are interested, please leave a comment on this post with your email address, and I will be happy to share with you. 4. Update the data source If the CSV file is located in a different directory, it will be necessary to adjust the data source path in Power BI. As illustrated, the dashboard uses a parameter named CsvFilePath, which defines the path to the collected CSV file. To adjust it: Open Transform Data in Power BI. Locate the CsvFilePath parameter in the list of Queries. Update the value to the directory where the CSV file was saved. Click Refresh Preview or Refresh to update the data. Click Home → Close & Apply. This approach allows rapid identification of RC4 dependencies, prioritization of remediation actions, and tracking of progress throughout the elimination process. List-AccountKeys.ps1 This script is used to identify which long-term keys are present on user, computer, and service accounts, enabling verification of whether RC4 is still required or whether AES128/AES256 keys are already available. Interpreting Observed Scenarios Microsoft recommends analyzing RC4 usage by jointly considering two key fields present in Kerberos events: Ticket Encryption Type Session Encryption Type Each combination represents a distinct Kerberos behavior, indicating the source of the issue, risk level, and remediation point in the environment. In addition to events 4768 and 4769, updates released starting January 13, 2026, introduce new Kdcsvc events in the System Event Log that assist in identifying RC4 dependencies ahead of enforcement. These events include: Event ID 201 – RC4 usage detected because the client advertises only RC4 and the service does not have msDS-SupportedEncryptionTypes defined. Event ID 202 – RC4 usage detected because the service account does not have AES keys and the msDS-SupportedEncryptionTypes attribute is not defined. Event ID 203 – RC4 usage blocked (enforcement phase) because the client advertises only RC4 and the service does not have msDS-SupportedEncryptionTypes defined. Event ID 204 – RC4 usage blocked (enforcement phase) because the service account does not have AES keys and msDS-SupportedEncryptionTypes is not defined. Event ID 205 – Detection of explicit enablement of insecure algorithms (such as RC4) in the domain policy DefaultDomainSupportedEncTypes. Event ID 206 – RC4 usage detected because the service accepts only AES, but the client does not advertise AES support. Event ID 207 – RC4 usage detected because the service is configured for AES, but the service account does not have AES keys. Event ID 208 – RC4 usage blocked (enforcement phase) because the service accepts only AES and the client does not advertise AES support. Event ID 209 – RC4 usage blocked (enforcement phase) because the service accepts only AES, but the service account does not have AES keys. https://support.microsoft.com/en-gb/topic/how-to-manage-kerberos-kdc-usage-of-rc4-for-service-account-ticket-issuance-changes-related-to-cve-2026-20833-1ebcda33-720a-4da8-93c1-b0496e1910dc They indicate situations where RC4 usage will be blocked in future phases, allowing early detection of configuration issues in clients, services, or accounts. These events are logged under: Log: System Source: Kdcsvc Below are the primary scenarios observed during the analysis of Kerberos authentication behavior, highlighting how RC4 usage manifests across different ticket and session encryption combinations. Each scenario represents a distinct risk profile and indicates specific remediation actions required to ensure compliance with the upcoming enforcement phases. Scenario A – RC4 / RC4 In this scenario, both the Kerberos ticket and the session key are issued using RC4. This is the worst possible scenario from a security and compatibility perspective, as it indicates full and explicit dependence on RC4 in the authentication flow. This condition significantly increases exposure to Kerberoasting attacks, since RC4‑encrypted tickets can be subjected to offline brute-force attacks to recover service account passwords. In addition, environments remaining in this state have a high probability of authentication failure after the April 2026 updates, when RC4 will no longer be accepted as an implicit fallback by the KDC. Events Associated with This Scenario During the Audit Phase, this scenario is typically associated with: Event ID 201 – Kdcsvc Indicates that: the client advertises only RC4 the service does not have msDS-SupportedEncryptionTypes defined the Domain Controller does not have DefaultDomainSupportedEncTypes defined This means RC4 is being used implicitly. This event indicates that the authentication will fail during the enforcement phase. Event ID 202 – Kdcsvc Indicates that: the service account does not have AES keys the service does not have msDS-SupportedEncryptionTypes defined This typically occurs when: legacy accounts have never had their passwords reset only RC4 keys exist in Active Directory Possible Causes Common causes include: the originating client (Requestor) advertises only RC4 the target service (Target) is not explicitly configured to support AES the account has only legacy RC4 keys the msDS-SupportedEncryptionTypes attribute is not defined Recommended Actions To remediate this scenario: Correctly identify the object involved in the authentication flow, typically: a service account (SPN) a computer account or a Domain Controller computer object Verify whether the object has AES keys available using analysis tools or scripts such as List-AccountKeys.ps1. If AES keys are not present, reset the account password, forcing generation of modern cryptographic keys (AES128 and AES256). Explicitly define the msDS-SupportedEncryptionTypes attribute to enable AES support. Recommended value for modern environments: 0x18 (AES128 + AES256) = 24 As illustrated below, this configuration can be applied directly to the msDS-SupportedEncryptionTypes attribute in Active Directory. AES can also be enabled via Active Directory Users and Computers by explicitly selecting: This account supports Kerberos AES 128 bit encryption This account supports Kerberos AES 256 bit encryption These options ensure that new Kerberos tickets are issued using AES algorithms instead of RC4. Temporary RC4 Usage (Controlled Rollback) In transitional scenarios—during migration or troubleshooting—it may be acceptable to temporarily use: 0x1C (RC4 + AES) = 28 This configuration allows the object to accept both RC4 and AES simultaneously, functioning as a controlled rollback while legacy dependencies are identified and corrected. However, the final objective must be to fully eliminate RC4 before the final enforcement phase in July 2026, ensuring the environment operates exclusively with AES128 and AES256. Scenario B – AES / RC4 In this case, the ticket is protected with AES, but the session is still negotiated using RC4. This typically indicates a client limitation, legacy configuration, or restricted advertisement of supported algorithms. Events Associated with This Scenario During the Audit Phase, this scenario may generate: Event ID 206 Indicates that: the service accepts only AES the client does not advertise AES in the Advertised Etypes In this case, the client is the issue. Recommended Action Investigate the Requestor Validate operating system, client type, and advertised algorithms Review legacy GPOs, hardening configurations, or settings that still force RC4 For Linux clients or third‑party applications, review krb5.conf, keytabs, and Kerberos libraries Scenario C – RC4 / AES Here, the session already uses AES, but the ticket is still issued using RC4. This indicates an implicit RC4 dependency on the Target or KDC side, and the environment may fail once enforcement begins. Events Associated with This Scenario This scenario may generate: Event ID 205 Indicates that the domain has explicit insecure algorithm configuration in: DefaultDomainSupportedEncTypes This means RC4 is explicitly allowed at the domain level. Recommended Action Correct the Target object Explicitly define msDS-SupportedEncryptionTypes with 0x18 = 24 Revalidate new ticket issuance to confirm full migration to AES / AES Conclusion CVE‑2026‑20833 represents a structural change in Kerberos behavior within Active Directory environments. Proper monitoring is essential before April 2026, and the msDS-SupportedEncryptionTypes attribute becomes the primary control point for service accounts, computer accounts, and Domain Controllers. July 2026 represents the final enforcement point, after which there will be no implicit rollback to RC4.Authorization and Governance for AI Agents: Runtime Authorization Beyond Identity at Scale
Designing Authorization‑Aware AI Agents at Scale Enforcing Runtime RBAC + ABAC with Approval Injection (JIT) Microsoft Entra Agent Identity enables organizations to govern and manage AI agent identities in Copilot Studio, improving visibility and identity-level control. However, as enterprises deploy multiple autonomous AI agents, identity and OAuth permissions alone cannot answer a more critical question: “Should this action be executed now, by this agent, for this user, under the current business and regulatory context?” This post introduces a reusable Authorization Fabric—combining a Policy Enforcement Point (PEP) and Policy Decision Point (PDP)—implemented as a Microsoft Entra‑protected endpoint using Azure Functions/App Service authentication. Every AI agent (Copilot Studio or AI Foundry/Semantic Kernel) calls this fabric before tool execution, receiving a deterministic runtime decision: ALLOW / DENY / REQUIRE_APPROVAL / MASK Who this is for Anyone building AI agents (Copilot Studio, AI Foundry/Semantic Kernel) that call tools, workflows, or APIs Organizations scaling to multiple agents and needing consistent runtime controls Teams operating in regulated or security‑sensitive environments, where decisions must be deterministic and auditable Why a V2? Identity is necessary—runtime authorization is missing Entra Agent Identity (preview) integrates Copilot Studio agents with Microsoft Entra so that newly created agents automatically get an Entra agent identity, manageable in the Entra admin center, and identity activity is logged in Entra. That solves who the agent is and improves identity governance visibility. But multi-agent deployments introduce a new risk class: Autonomous execution sprawl — many agents, operating with delegated privileges, invoking the same backends independently. OAuth and API permissions answer “can the agent call this API?” They do not answer “should the agent execute this action under business policy, compliance constraints, data boundaries, and approval thresholds?” This is where a runtime authorization decision plane becomes essential. The pattern: Microsoft Entra‑Protected Authorization Fabric (PEP + PDP) Instead of embedding RBAC logic independently inside every agent, use a shared fabric: PEP (Policy Enforcement Point): Gatekeeper invoked before any tool/action PDP (Policy Decision Point): Evaluates RBAC + ABAC + approval policies Decision output: ALLOW / DENY / REQUIRE_APPROVAL / MASK This Authorization Fabric functions as a shared enterprise control plane, decoupling authorization logic from individual agents and enforcing policies consistently across all autonomous execution paths. Architecture (POC reference architecture) Use a single runtime decision plane that sits between agents and tools. What’s important here Every agent (Copilot Studio or AI Foundry/SK) calls the Authorization Fabric API first The fabric is a protected endpoint (Microsoft Entra‑protected endpoint required) Tools (Graph/ERP/CRM/custom APIs) are invoked only after an ALLOW decision (or approval) Trust boundaries enforced by this architecture Agents never call business tools directly without a prior authorization decision The Authorization Fabric validates caller identity via Microsoft Entra Authorization decisions are centralized, consistent, and auditable Approval workflows act as a runtime “break-glass” control for high-impact actions This ensures identity, intent, and execution are independently enforced, rather than implicitly trusted. Runtime flow (Decision → Approval → Execution) Here is the runtime sequence as a simple flow (you can keep your Mermaid diagram too). ```mermaid flowchart TD START(["START"]) --> S1["[1] User Request"] S1 --> S2["[2] Agent Extracts Intent\n(action, resource, attributes)"] S2 --> S3["[3] Call /authorize\n(Entra protected)"] S3 --> S4 subgraph S4["[4] PDP Evaluation"] ABAC["ABAC: Tenant · Region · Data Sensitivity"] RBAC["RBAC: Entitlement Check"] Threshold["Approval Threshold"] ABAC --> RBAC --> Threshold end S4 --> Decision{"[5] Decision?"} Decision -->|"ALLOW"| Exec["Execute Tool / API"] Decision -->|"MASK"| Masked["Execute with Masked Data"] Decision -->|"DENY"| Block["Block Request"] Decision -->|"REQUIRE_APPROVAL"| Approve{"[6] Approval Flow"} Approve -->|"Approved"| Exec Approve -->|"Rejected"| Block Exec --> Audit["[7] Audit & Telemetry"] Masked --> Audit Block --> Audit Audit --> ENDNODE(["END"]) style START fill:#4A90D9,stroke:#333,color:#fff style ENDNODE fill:#4A90D9,stroke:#333,color:#fff style S1 fill:#5B5FC7,stroke:#333,color:#fff style S2 fill:#5B5FC7,stroke:#333,color:#fff style S3 fill:#E8A838,stroke:#333,color:#fff style S4 fill:#FFF3E0,stroke:#E8A838,stroke-width:2px style ABAC fill:#FCE4B2,stroke:#999 style RBAC fill:#FCE4B2,stroke:#999 style Threshold fill:#FCE4B2,stroke:#999 style Decision fill:#fff,stroke:#333 style Exec fill:#2ECC71,stroke:#333,color:#fff style Masked fill:#27AE60,stroke:#333,color:#fff style Block fill:#C0392B,stroke:#333,color:#fff style Approve fill:#F39C12,stroke:#333,color:#fff style Audit fill:#3498DB,stroke:#333,color:#fff ``` Design principle: No tool execution occurs until the Authorization Fabric returns ALLOW or REQUIRE_APPROVAL is satisfied via an approval workflow. Where Power Automate fits (important for readers) In most Copilot Studio implementations, Agents calls Power Automate (agent flows), is the practical integration layer that calls enterprise services and APIs. Copilot Studio supports “agent flows” as a way to extend agent capabilities with low-code workflows. For this pattern, Power Automate typically: acquires/uses the right identity context for the call (depending on your tenant setup), and calls the /authorize endpoint of the Authorization Fabric, returns the decision payload to the agent for branching. Copilot Studio also supports calling REST endpoints directly using the HTTP Request node, including passing headers such as Authorization: Bearer <token>. Protected endpoint only: Securing the Authorization Fabric with Microsoft Entra For this V2 pattern, the Authorization Fabric must be protected using Microsoft Entra‑protected endpoint on Azure Functions/App Service (built‑in auth). Microsoft Learn provides the configuration guidance for enabling Microsoft Entra as the authentication provider for Azure App Service / Azure Functions. Step 1 — Create the Authorization Fabric API (Azure Function) Expose an authorization endpoint: HTTP Step 2 — Enable Microsoft Entra‑protected endpoint on the Function App In Azure Portal: Function App → Authentication Add identity provider → Microsoft Choose Workforce configuration (enterprise tenant) Set Require authentication for all requests This ensures the Authorization Fabric is not callable without a valid Entra token. Step 3 — Optional hardening (recommended) Depending on enterprise posture, layer: IP restrictions / Private endpoints APIM in front of the Function for rate limiting, request normalization, centralized logging (For a POC, keep it minimal—add hardening incrementally.) Externalizing policy (so governance scales) To make this pattern reusable across multiple agents, policies should not be hardcoded inside each agent. Instead, store policy definitions in a central policy store such as Cosmos DB (or equivalent configuration store), and have the PDP load/evaluate policies at runtime. Why this matters: Policy changes apply across all agents instantly (no agent republish) Central governance + versioning + rollback becomes possible Audit and reporting become consistent across environments (For the POC, a single JSON document per policy pack in Cosmos DB is sufficient. For production, add versioning and staged rollout.) Store one PolicyPack JSON document per environment (dev/test/prod). Include version, effectiveFrom, priority for safe rollout/rollback. Minimal decision contract (standard request / response) To keep the fabric reusable across agents, standardize the request payload. Request payload (example) Decision response (deterministic) Example scenario (1 minute to understand) Scenario: A user asks a Finance agent to create a Purchase Order for 70,000. Even if the user has API permission and the agent can technically call the ERP API, runtime policy should return: REQUIRE_APPROVAL (threshold exceeded) trigger an approval workflow execute only after approval is granted This is the difference between API access and authorized business execution. Sample Policy Model (RBAC + ABAC + Approval) This POC policy model intentionally stays simple while demonstrating both coarse and fine-grained governance. 1) Coarse‑grained RBAC (roles → actions) FinanceAnalyst CreatePO up to 50,000 ViewVendor FinanceManager CreatePO up to 100,000 and/or approve higher spend 2) Fine‑grained ABAC (conditions at runtime) ABAC evaluates context such as region, classification, tenant boundary, and risk: 3) Approval injection (Agent‑level JIT execution) For higher-risk/high-impact actions, the fabric returns REQUIRE_APPROVAL rather than hard deny (when appropriate): How policies should be evaluated (deterministic order) To ensure predictable and auditable behavior, evaluate in a deterministic order: Tenant isolation & residency (ABAC hard deny first) Classification rules (deny or mask) RBAC entitlement validation Threshold/risk evaluation Approval injection (JIT step-up) This prevents approval workflows from bypassing foundational security boundaries such as tenant isolation or data sovereignty. Copilot Studio integration (enforcing runtime authorization) Copilot Studio can call external REST APIs using the HTTP Request node, including passing headers such as Authorization: Bearer <token> and binding response schema for branching logic. Copilot Studio also supports using flows with agents (“agent flows”) to extend capabilities and orchestrate actions. Option A (Recommended): Copilot Studio → Agent Flow (Power Automate) → Authorization Fabric Why: Flows are a practical place to handle token acquisition patterns, approval orchestration, and standardized logging. Topic flow: Extract user intent + parameters Call an agent flow that: calls /authorize returns decision payload Branch in the topic: If ALLOW → proceed to tool call If REQUIRE_APPROVAL → trigger approval flow; proceed only if approved If DENY → stop and explain policy reason Important: Tool execution must never be reachable through an alternate topic path that bypasses the authorization check. Option B: Direct HTTP Request node to Authorization Fabric Use the Send HTTP request node to call the authorization endpoint and branch using the response schema. This approach is clean, but token acquisition and secure secretless authentication are often simpler when handled via a managed integration layer (flow + connector). AI Foundry / Semantic Kernel integration (tool invocation gate) For Foundry/SK agents, the integration point is before tool execution. Semantic Kernel supports Azure AI agent patterns and tool integration, making it a natural place to enforce a pre-tool authorization check. Pseudo-pattern: Agent extracts intent + context Calls Authorization Fabric Enforces decision Executes tool only when allowed (or after approval) Telemetry & audit (what Security Architects will ask for) Even the best policy engine is incomplete without audit trails. At minimum, log: agentId, userUPN, action, resource decision + reason + policyIds approval outcome (if any) correlationId for downstream tool execution Why it matters: you now have a defensible answer to: “Why did an autonomous agent execute this action?” Security signal bonus: Denials, unusual approval rates, and repeated policy mismatches can also indicate prompt injection attempts, mis-scoped agents, or governance drift. What this enables (and why it scales) With a shared Authorization Fabric: Avoid duplicating authorization logic across agents Standardize decisions across Copilot Studio + Foundry agents Update governance once (policy change) and apply everywhere Make autonomy safer without blocking productivity Closing: Identity gets you who. Runtime authorization gets you whether/when/how. Copilot Studio can automatically create Entra agent identities (preview), improving identity governance and visibility for agents. But safe autonomy requires a runtime decision plane. Securing that plane as an Entra-protected endpoint is foundational for enterprise deployments. In enterprise environments, autonomous execution without runtime authorization is equivalent to privileged access without PIM—powerful, fast, and operationally risky.Your Sentinel AMA Logs & Queries Are Public by Default - AMPLS Architectures to Fix That
When you deploy Microsoft Sentinel, security log ingestion travels over public Azure Data Collection Endpoints by default. The connection is encrypted, and the data arrives correctly — but the endpoint is publicly reachable, and so is the workspace itself, queryable from any browser on any network. For many organisations, that trade-off is fine. For others — regulated industries, healthcare, financial services, critical infrastructure — it is the exact problem they need to solve. Azure Monitor Private Link Scope (AMPLS) is how you solve it. What AMPLS Actually Does AMPLS is a single Azure resource that wraps your monitoring pipeline and controls two settings: Where logs are allowed to go (ingestion mode: Open or PrivateOnly) Where analysts are allowed to query from (query mode: Open or PrivateOnly) Change those two settings and you fundamentally change the security posture — not as a policy recommendation, but as a hard platform enforcement. Set ingestion to PrivateOnly and the public endpoint stops working. It does not fall back gracefully. It returns an error. That is the point. It is not a firewall rule someone can bypass or a policy someone can override. Control is baked in at the infrastructure level. Three Patterns — One Spectrum There is no universally correct answer. The right architecture depends on your organisation's risk appetite, existing network infrastructure, and how much operational complexity your team can realistically manage. These three patterns cover the full range: Architecture 1 — Open / Public (Basic) No AMPLS. Logs travel to public Data Collection Endpoints over the internet. The workspace is open to queries from anywhere. This is the default — operational in minutes with zero network setup. Cloud service connectors (Microsoft 365, Defender, third-party) work immediately because they are server-side/API/Graph pulls and are unaffected by AMPLS. Azure Monitor Agents and Azure Arc agents handle ingestion from cloud or on-prem machines via public network. Simplicity: 9/10 | Security: 6/10 Good for: Dev environments, teams getting started, low-sensitivity workloads Architecture 2 — Hybrid: Private Ingestion, Open Queries (Recommended for most) AMPLS is in place. Ingestion is locked to PrivateOnly — logs from virtual machines travel through a Private Endpoint inside your own network, never touching a public route. On-premises or hybrid machines connect through Azure Arc over VPN or a dedicated circuit and feed into the same private pipeline. Query access stays open, so analysts can work from anywhere without needing a VPN/Jumpbox to reach the Sentinel portal — the investigation workflow stays flexible, but the log ingestion path is fully ring-fenced. You can also split ingestion mode per DCE if you need some sources public and some private. This is the architecture most organisations land on as their steady state. Simplicity: 6/10 | Security: 8/10 Good for: Organisations with mixed cloud and on-premises estates that need private ingestion without restricting analyst access Architecture 3 — Fully Private (Maximum Control) Infrastructure is essentially identical to Architecture 2 — AMPLS, Private Endpoints, Private DNS zones, VPN or dedicated circuit, Azure Arc for on-premises machines. The single difference: query mode is also set to PrivateOnly. Analysts can only reach Sentinel from inside the private network. VPN or Jumpbox required to access the portal. Both the pipe that carries logs in and the channel analysts use to read them are fully contained within the defined boundary. This is the right choice when your organisation needs to demonstrate — not just claim — that security data never moves outside a defined network perimeter. Simplicity: 2/10 | Security: 10/10 Good for: Organisations with strict data boundary requirements (regulated industries, audit, compliance mandates) Quick Reference — Which Pattern Fits? Scenario Architecture Getting started / low-sensitivity workloads Arch 1 — No network setup, public endpoints accepted Private log ingestion, analysts work anywhere Arch 2 — AMPLS PrivateOnly ingestion, query mode open Both ingestion and queries must be fully private Arch 3 — Same as Arch 2 + query mode set to PrivateOnly One thing all three share: Microsoft 365, Entra ID, and Defender connectors work in every pattern — they are server-side pulls by Sentinel and are not affected by your network posture. Please feel free to reach out if you have any questions regarding the information provided.Sentinel Foundry - MCP Server (Github Community Release)
I’ve been cooking something that a lot of people in SOC have been struggling with — especially on the engineering side of Microsoft Sentinel. Thanks to the Microsoft Security team for shaping the capabilities of Sentinel even better with Sentinel Data Lake & Modern SecOps. Today’s the day I can finally share it. Note: This is not an official Microsoft product, but it is designed to make the Sentinel Build even better (complement) with much more intelligence. 🚀 Sentinel Foundry is now in public preview with 43 tools. (Sentinel Foundry - MCP Server) It’s an MCP server built to act like the brain of a strong Sentinel engineer — helping make building, improving, and operating Sentinel far more practical, faster, and honestly more enjoyable. For a lot of teams, the challenge is not understanding what Sentinel can do. The hard part is the engineering work around it: -> Deciding what data should actually be ingested -> Building a clean, scalable Sentinel foundation -> Writing useful detections instead of noisy ones -> Balancing security value with cost -> Turning ideas into deployable engineering outputs That is exactly why I built Sentinel Foundry to help communities grow stronger. It helps with the real engineering tasks behind Sentinel — from architecture thinking to detection design, deployment planning, ingestion strategy, automation ideas, and many of the workflows outlined in the GitHub project. How does it work? Here’s one of the flagship prompts I ran with it: “Give me a complete security posture report for our workspace. Score each pillar and tell me what to prioritise.” And within seconds, it produced a structured engineering blueprint that would normally take a lot longer to pull together manually. You can see the example prompts here in what it can do: https://github.com/prabhukiranveesam/Sentinel-Foundry#what-can-it-do I want building Sentinel to feel less like repetitive engineering overhead — and more like real security engineering that is fast, creative, and enjoyable. If you work with Sentinel as a SOC L2 analyst, engineer, detection engineer, consultant, or architect, I’d genuinely love for you to try it and tell me what you think. 🔗 Public Preview: https://github.com/prabhukiranveesam/Sentinel-Foundry This is just the start of an AI era — and I’m excited to keep shaping it with more powerful features over the coming days. This is very easy to set up and will be available to all of you at no cost during this month as part of the public preview, and your feedback is extremely valuable to shape this as a powerful solution.Unexpected button behaviour when using the prompt=create parameter in Entra External ID user flows
Hi, In a recent workload, I'm assisting a client to implement Entra External ID for streamlined authorization as well as single sign-on for associated registered external facing third-party applications for external customer users through their Entra External ID identity, as well as assisting the client with auth branding and other UI customizations, and preparing Entra ID federation custom OIDC providers and configuration for enabling SSO with organizational internal and remote work- and school accounts etc. The sign-up / create account experience is of importance to the client, as a rather substantial amount of users are expected to sign-up via self-service sign-up following having received an invite via another app. To ensure that the user lands on the create account view, in order to minimize the number of steps and actions the users need to take to get there, the prompt=create parameter is used to pre-select the sign-up/register experience in the user flows UI. Having stress-tested the user flows and experience recently, we noticed that in a specific scenario, some UI elements behave in a manner that could be described as unexpected, and even though a workaround has mitigated it to some extent, the behaviour of some elements could probably be improved a bit to ensure an even more consistent user experience in the built-in user flows. Specifically, if the user flow is invoked with the prompt parameter set, the user lands on the create account screen as expected, however, unless custom CSS modification is applied, the Back button that would typically be there, as if having arrived there from the initial sign-in screen where it’s also displayed. If pressing the Back button displayed on the Create account view when having navigated there with the prompt=create set in the /authorize request, pressing the button seemingly doesn’t have any impact or result in any action, one can click it, but nothing happens. I'd suspect it's perhaps a "remnant" from if the flow is invoked without any prompt parameter set, or with prompt=login set, but when prompt=create is set, there is no state/page history in the UI to navigate back to, as no previous page has been displayed or rendered yet and added to the history, and the Back button click thus doesn’t have any effect. In general, I think buttons that don't have any tangible action should not be displayed, also, if prompt=login is set, the Back button isn't shown then either on the very initial user flow view shown then, so it would seem the built-in user flows actually already follow such approach in fact, but not when the prompt=create has been set, thus causing some inconsistency in the UI that users could notice unless custom CSS styling is applied. The expected behaviour for our business case would be that if prompt=create is set, then, similar to the prompt=login, no Back button should be displayed, or, if shown, it should result in some navigation (perhaps something like javascript.go(-1), but that could/would be dependent on from where the user arrived, e.g., going back a step in the browser history won't work if the user clicked a link from an invite email ) and preferably not be non-actionable. Furthermore, on the topic of the Back button and its behaviour when the prompt=create parameter is set, there is another case at which we observed some unexpected button behaviour as well, that could probably benefit from some attention. At a specific second scenario, it seems that the Continue button is displayed without providing any action, and when clicking the Back button then, clicking it seemingly resets the "create account", state likely set by the prompt=create initially, and instead switches the flow back to the sign-in state, which can at least affect the display text of some subsequent buttons shown in later steps in the UI. The user arrives at a built-in Entra External ID user flow /authorize endpoint, with the prompt=create query parameter set The user triggers the OTP challenge by entering an email address to verify the email address The user receives the OTP code but enters it incorrectly, i.e., doesn't copy the full code length of eight digits, and instead enters/pastes six of the eight code digits. The UI then shows an error message that the code could not be used/validated, and the email address field is displayed again with the email address that was used for the verification attempt prefilled. If the user clicks the Continue button at this stage, nothing happens. If the user clicks the Back button instead (given that it’s not hidden), it shows the same view one more time, with the Back and Continue buttons at the bottom. However, if the user clicks the Continue button this time, it works and a new code is sent. On the next screen, where the newly sent code can be entered, instead of a button named "Continue", it will now instead show a button with the text "Sign-in" (it would seem like the create account state has gotten lost somewhere along the way at this point, perhaps when the back button in the step 5 above was pressed). If one omits the prompt=create parameter, or when using prompt=login, things seem to work fine, the above occurs when the prompt=create is set. The reason why is the prompt=create is used is due to a business requirement to try to minimize the steps, especially in conjunction with the registration/sign-up, as far a possible when signing up. We have opted for the built-in user flows, not the self-hosted native authentication UI pages, for this project, and then as I understand it, it is the prompt=create parameter that can/should be used to enable the user to land directly on the registration page without having to navigate there manually via the sign-up link, that is otherwise shown on the initial sign-in page. It would thus be great if the prompt=create parameter could have some attention (or perhaps if a dedicated sign-up user flow type could be added potentially, even though that would perhaps warrant some update to the user flow app linking as well, as my understanding is that one user flow can be linked to one app registration at a time currently) to avoid that some buttons become unresponsive when the parameter is used, or falls back to sign-in, to improve the built-in user flows further, as the prompt=create fulfils the business requirement well otherwise. If there would be any further/follow-up questions on the above, e.g., to clarify the requirement, or further explain the reproduction steps and behaviour observed, or anything else, please tell, and I'll ensure to get back as soon as possible. Also, would someone have some input on potential other/additional ways to pre-select the create account/sign-up experience, that would naturally be much appreciated as well, thanks! Regards Kristoffer
Exciting Announcements: New Data Connectors Released Using the Codeless Connector Framework
Microsoft Sentinel’s Codeless Connector Framework or ‘CCF’ (formerly called Codeless Connector Platform [CCP]) represents a paradigm shift in data ingestion, making it easier than ever for organisations to do more with Microsoft Sentinel by integrating diverse data sources seamlessly. Designed to simplify and expedite the onboarding of data sources, CCF eliminates the need for extensive coding expertise and maintaining additional services to facilitate ingestion, allowing security teams to focus on what truly matters – safeguarding their environment. Advantages of the Codeless Connector Framework The Codeless Connector Framework offers several compelling benefits: Ease of Use: CCF configuration-based templates allows advanced users to create data connectors without writing exhausting code, making the onboarding process quicker and more accessible to a broader audience. Flexibility: Users can customise data streams to meet their specific needs; optimizing efficacy while ensuring more control on the data being ingested. Scalability: The connectors built using CCF follows a true SaaS auto-expansion model making them highly scalable and natively reliable for large data volumes. Efficiency: By reducing the time and effort required to develop and deploy data connectors, CCF accelerates the availability of critical insights for security monitoring and more rapidly expands the value Microsoft Sentinel provides. What are we up to? We recognize that Codeless Connectors offer substantial advantages over Azure Function App based ingestion in Microsoft Sentinel in most cases. That motivates us to continue investing in modernizing our ingestion patterns for out-of-box connectors; one connector at a time. Another goal of modernizing these connectors is to replace the deprecated HTTP Data Collector API with the Log Ingestion API to send data to Microsoft Sentinel. Announcing the General Availability of New Data Connectors We are continually improving the Data Collection experience for our customers and are thrilled to announce that the following data connectors are now Generally Available (GA) on the Codeless Connector Framework. Atlassian Confluence Ingesting Confluence audit logs allows organizations to monitor collaboration activity, detect security risks, and troubleshoot configuration issues using Confluence audit records. Auth0 With the Auth0 Connector, organizations can effortlessly integrate authentication and authorization data from Auth0 into Microsoft Sentinel. This connector provides valuable insights into user activities and access patterns, bolstering identity security and compliance efforts. Azure DevOps Audit logs from Azure DevOps, allows security teams to monitor user activities, detect anomalous behavior, and investigate potential threats across DevOps environments. Box The Box Connector facilitates the ingestion of file storage and sharing data from Box into Microsoft Sentinel. By leveraging this connector, security teams can monitor file access and sharing activities, ensuring data integrity, and preventing unauthorized access. Google Cloud Platform Load Balancer With GCP Load Balancer and Web Application Firewall (Cloud Armor) logs, security teams can monitor inbound network activity, enforce security policies, and detect threats across GCP environments. Proofpoint POD The ingestion of email security logs allows organizations to monitor message traceability, detect threats, and investigate data exfiltration attempts by attackers and malicious insiders. Proofpoint TAP Email threat intelligence logs, including message and click events, provides visibility into malware and phishing activity to support custom alerts, dashboards, and threat investigation. SentinelOne The SentinelOne Connector enables seamless ingestion of threat intelligence and endpoint security data from SentinelOne into Microsoft Sentinel. This integration empowers security teams to enhance their threat detection capabilities and respond swiftly to potential threats. New Connectors in Public Preview CrowdStrike Falcon Data Replicator (S3 based Polling) Google Cloud Platform VPC Flow Google Cloud Platform DNS Google IAM These new additions are not new out-of-box sources in Microsoft Sentinel, but they do improve how data is collected. The previously Azure Function App based polling has now been upgraded to the Codeless Connector Framework for these products to ensure data collection adheres to the more scalable; advantageous pattern with CCF. As noted previously, the newer version of these connectors replaces the deprecated HTTP Data Collector API with the Log Ingestion API to send data to Microsoft Sentinel. Call to Action! Microsoft Sentinel customers collecting data from any of the mentioned sources using Azure Function Apps are advised to migrate their ingestion streams to newer versions to utilize the Codeless Connector Framework. While we continue to improve the data collection experience across all connectors, we encourage our customers and partners to join the Microsoft Security Communities to benefit from early insights about the latest and greatest with Microsoft Security. Call to Action for ISV Partners We invite our ISV partners to migrate their Azure Function App-based data connectors to the Codeless Connector Framework. By leveraging CCF for data ingestion, we can ensure that our mutual customers benefit from streamlined data integration and enhanced security monitoring in Microsoft Sentinel. We are committed to ensuring partners have all the support needed in this transformation. For any support, please reach out to us at Microsoft Sentinel Partners. Join us in this transformative journey to empower our customers by unlocking the full potential of their security investments with Microsoft Sentinel’s Codeless Connector Framework. References Create a codeless connector for Microsoft Sentinel Migrate from the HTTP Data Collector API to the Log Ingestion API to send data to Azure Monitor Logs
Making AI Apps Enterprise-Ready with Microsoft Purview and Microsoft Foundry
Building AI apps is easy. Shipping them to production is not. Microsoft Foundry lets developers bring powerful AI apps and agents to production in days. But managing safety, security, and compliance for each one quickly becomes the real bottleneck. Every enterprise AI project hits the same wall: security reviews, data classification, audit trails, DLP policies, retention requirements. Teams spend months building custom logging pipelines and governance systems that never quite keep up with the app itself. There is a faster way. Enable Purview & Ship Faster! Microsoft Foundry now includes native integration with Microsoft Purview. When you enable it, every AI interaction in your subscription flows into the same enterprise data governance infrastructure that already protects your Microsoft 365 and Azure data estate. No SDK changes. No custom middleware. No separate audit system to maintain. Here is what you get: Visibility within 24 hours. Data Security Posture Management (DSPM) shows you total interactions, sensitive data detected in prompts and responses, user activity across AI apps, and insider risk scoring. This dashboard exists the moment you flip the toggle. Automatic data classification. The same classification engine that scans your Microsoft 365 tenant now scans AI interactions. Credit card numbers, health information, SSNs, and your custom sensitive information types are all detected automatically. Audit logs you do not have to build. Every AI interaction is logged in the Purview unified audit log. Timestamps, user identity, the AI app involved, files accessed, sensitivity labels applied. When legal needs six months of AI interactions for an investigation, the data is already there. DLP policy enforcement. Configure policies that block prompts containing sensitive information before they reach the model. This uses the same DLP framework you already know. eDiscovery, retention, and communication compliance. Search AI interactions alongside email and Teams messages. Set retention policies by selecting "Enterprise AI apps" as the location. Detect harmful or unauthorized content in prompts. How to Enable Prerequisite: You need the “Azure AI Account Owner” role assigned by your Subscription Owner. Open the Microsoft Foundry portal (make sure you are in the new portal) Select Operate from the top navigation Select Compliance in the left pane Select the Security posture tab Select the Azure Subscription Enable the toggle next to Microsoft Purview Repeat the above steps for other subscriptions By enabling this toggle, data exchanged within Foundry apps and agents' starts flowing to Purview immediately. Purview reports populate within 24 hours. What shows up in Purview? Purview Data Security Admins: Go to the Microsoft Purview portal, open DSPM, and follow the recommendation to setup “Secure interactions from enterprise AI apps” . Navigate to DSPM > Discover > Apps and Agents to review and monitor the Foundry apps built in your organization Navigate to DSPM > Activity Explorer to review the activity on a given agent/application What About Cost? Enabling the integration is free. Audit Standard is included for Foundry apps. You will only be charged for data security policies you setup for governing Foundry data. A Real-World Scenario: The Internal HR Assistant Consider a healthcare company building an internal AI agent for HR questions. The Old Way: The developer team spends six weeks building a custom logging solution to strip PII/PHI from prompts to meet HIPAA requirements. They have to manually demonstrate these logs to compliance before launch. The Foundry Way: The team enables the Purview toggle. Detection: Purview automatically flags if an employee pastes a patient ID into the chat. Retention: The team selects "Enterprise AI Apps" in their retention policy, ensuring all chats are kept for the required legal period. Outcome: The app ships on schedule because Compliance trusts the controls are inherited, not bolted on. Takeaway Microsoft Purview DSPM is a gamechanger for organizations looking to adopt AI responsibly. By integrating with Microsoft Foundry, it provides a comprehensive framework to discover, protect, and govern AI interactions ensuring compliance, reducing risk, and enabling secure innovation. We built this integration because teams kept spending months on compliance controls that already exist in Microsoft's stack. The toggle is there. The capabilities are real. Your security team already trusts Purview. Your compliance team already knows the tools. Enable it. Ship your agent. Let the infrastructure do what infrastructure does best: work in the background while you focus on what your application does. Additional Resources Documentation: Use Microsoft Purview to manage data security & compliance for Microsoft Foundry | Microsoft Learn
