Build a Local Microsoft Sentinel Triage Agent in VS Code (Copilot + MCP)
Modern SOC work is not limited by data—it’s limited by the friction of collecting it. This post shows a local-first workflow that lets you investigate Microsoft Sentinel incidents from inside VS Code using GitHub Copilot Chat for reasoning and a small, deterministic MCP toolset for evidence retrieval and (optionally) approval-gated writeback. What you’ll take away: How to structure a Copilot + MCP triage loop that stays grounded in Azure evidence A reliability pattern: fall back to KQL when Sentinel subresource APIs are flaky A safety pattern: draft-first, explicit-approval writeback for incident comments Why This Exists Sentinel triage is powerful but fragmented: you jump between the portal, KQL, entity pivots, and case notes just to answer “what happened?” The goal here is to collapse that into a single, repeatable loop inside the editor. Resolve the incident and pull the underlying alerts/entities Pivot into AzureActivity (and other logs) to identify the actor and outcome Use threat intelligence (TI) for context—not as the decision Generate an evidence-backed narrative and draft comment; write back only on explicit approval Design Principles Evidence first: every claim must be traceable to Sentinel APIs or Log Analytics results Small tool surface: fewer tools, clearer prompting, easier hardening Reliability by design: if one API path fails, pivot to KQL and continue Safety boundary: investigation and writeback are separate, and writeback is approval-gated Architecture & Data Flow A local TypeScript MCP server exposes a handful of triage tools to Copilot Chat in VS Code. Reads come from Sentinel + Log Analytics; writes (incident comments) are optional and require explicit approval. Copilot Chat (VS Code) decides the next step and summarizes outputs MCP server executes allowed tools: incident lookup, alert/entity retrieval, KQL queries, optional comment writeback Evidence sources: Sentinel Incident APIs + Log Analytics tables (SecurityIncident, SecurityAlert, AzureActivity, TI tables) Safety gate: writeback happens only after explicit approval; otherwise you get a draft Tool Surface MCP is useful here because it separates reasoning from execution: Copilot can decide what to do, but only the MCP server can do it—and only through tools you explicitly define and can audit. list_incidents / get_incident (ground the case) get_incident_alerts / get_incident_entities (fast path) run_incident_kql (reliable fallback + pivots) add_incident_comment (draft-first; writes only with approval) The Investigation Loop (3 Steps) Prompt used sentinel-triage-local Investigate Sentinel incident 1478 end to end in workspace Subscription ID/Resource Group/Workspace Name. Resolve the incident ID first, collect underlying alerts and entities, enrich with AzureActivity and TI, determine whether the activity is malicious or benign, and return: 1. Investigation summary 2. Key evidence 3. Entity analysis 4. TI enrichment result 5. Risk assessment 6. Recommended disposition 7. Final incident comment draft Rules: - Use tool output only, no guessing. - If alert/entity subresource APIs fail, pivot to KQL and continue. - Do not submit the comment unless I explicitly say: APPROVE COMMENT. 1) Ground the incident Resolve the human-friendly incident number to the Sentinel incident resource ID, then capture the metadata you need to drive every later pivot. Incident numbers are convenient for analysts, but the actual investigation flow depends on the underlying incident resource ID. Resolving that first gives the workflow a concrete anchor for: Title Severity Owner Status Alert count Analytic rule IDs Incident URL This gives you the stable identifiers (and the URL) needed to retrieve alerts, entities, and supporting logs. 2) Collect alerts and entities (fast path) Pull the alerts behind the incident and the entities they reference. When the incident subresource APIs behave, this is the fastest way to assemble the working set. In the ideal path, the agent can call the incident alert and entity subresources directly. That gives fast access to: Alert IDs Alert names Timestamps Severities Entities Provider metadata 3) Stay reliable: pivot to KQL when APIs fail In real environments, the incident subresource APIs for alerts/entities are not always dependable. When they fail, the workflow switches to Log Analytics and reconstructs the same evidence via KQL—so the investigation continues. SecurityIncident to recover the incident record and alert IDs SecurityAlert to retrieve alert details and entities AzureActivity to determine who or what performed the operation ThreatIntelligenceIndicator and ThreatIntelIndicators for enrichment The High-Signal Pivot: AzureActivity In the incidents I tested, AzureActivity was the fastest way to classify “suspicious deployment” alerts: it tells you who did the action, what operation ran, and whether it succeeded. The evidence showed: The caller was a single Microsoft Entra ID object ID Claims_d.idtyp = "app" Authorization_d.evidence.principalType = "ServicePrincipal" The activity was tied to a policy assignment The operation was MICROSOFT.RESOURCES/DEPLOYMENTS/WRITE The result was BadRequest with InvalidTemplate That pattern typically points to automation (service principal + policy-driven deployment) failing due to a bad template—not an interactive attacker. Threat Intelligence: Use It as Context Enrich observables against TI, but treat it as corroboration: a hit is not proof, and a miss is not a clean bill of health. In my test runs, TI mainly helped refine confidence after AzureActivity and alert evidence established the likely story. Output: An Evidence-Backed Narrative (and a Draft Comment) Once the tools return results, Copilot’s job is synthesis: turn structured evidence into a short narrative an analyst can paste into the case. What happened, who/what triggered it, and whether it succeeded Key supporting evidence (alerts, entities, AzureActivity pivots, TI context) A recommended disposition and a draft incident comment Incident comment written back automatically (after approval) (screenshot): Safety + Reliability: Approval-Gated Writeback The agent can draft a comment automatically, but it cannot change incident state unless the analyst explicitly approves. That boundary is what makes the workflow usable in real operations. After approval, the tool submits the drafted comment directly to the Sentinel incident so the portal reflects the same evidence-backed narrative. Default: return the draft comment only On approval: acquire an ARM token via Azure CLI and submit via curl.exe (hardened with validation + retries) Why This Is Worth Building Less context switching: investigation happens where you already work More consistency: the same loop runs every time, with deterministic tools Better classification: AzureActivity pivots reduce false “user did X” assumptions Safer automation: drafts are automatic; writes are explicit and auditable Conclusion AI is most useful in a SOC when it is constrained: deterministic tools fetch the evidence, the model synthesizes it, and humans keep control of state changes. A local Copilot + MCP workflow hits that sweet spot—faster triage for the SOC analysts.The Role of Sentence Syntax in Security Copilot: Structured Storytelling for Effective Defense
The Role of Sentence Syntax in Security Copilot: Structured Storytelling for Effective Defense🔒 Much like Mad Libs, creating effective Security Copilot prompts isn’t just about filling in words, it’s about crafting a structured storyline. 🧩 In this upcoming session, Mona Ghadiri explores how sentence syntax plays a critical role in shaping Copilot’s reasoning and outcomes. Together, we’ll look at how the right structure helps transform raw data into actionable defense insights. If you’re curious about how to get started with Copilot for Security prompting, this is your chance to dive into the art and science of structured storytelling for cyber defense. 📅 Don’t miss it, join and discover how syntax shapes smarter security! 🗓️ Date: 25 October 2025 ⏰ Time: 18:00 (CEST) 🎙️ Speaker: Mona Ghadiri 📌 Topic: The Role of Sentence Syntax in Security Copilot: Structured Storytelling for Effective DefenceAutomating Sentinel Triage with Microsoft Security Copilot
Automating Sentinel Triage with Microsoft Security Copilot 🤖🧠🛡️ We’re diving deep into the transformative world of AI-driven automation in cybersecurity. This session will explore how Microsoft Security Copilot, integrated with Logic Apps, can supercharge the triage process in Microsoft Sentinel. 💡 What you’ll take away: ✔️ Practical applications of AI in triage and incident response ✔️ How to reduce manual effort and operational costs ✔️ Innovative strategies to elevate efficiency in your SOC Join us as we explore how cutting-edge AI reshapes security operations and empowers teams to focus on what matters most. 🗓️ Date: 29 September 2025 ⏰ Time: 17:00 (AEST) 🎙️ Speaker: Anthony Porter 📌 Topic: Automating Sentinel Triage with Microsoft Security CopilotAzure Entra Security Copilot: How It’s Changing Identity Protection
Overview Azure Entra Security Copilot is revolutionizing how organizations approach identity protection. By combining the power of generative AI with Microsoft’s deep security insights, it enables faster threat detection, smarter policy recommendations, and simplified incident response. Hands-On Experience After integrating Security Copilot into our Azure Entra environment, here’s what stood out: Natural Language Queries: You can ask things like “Show me risky sign-ins from last week” and get instant, actionable insights. Automated Investigations: It correlates signals across Entra ID, Defender, and Sentinel to surface threats. Policy Recommendations: Based on your environment, it suggests Conditional Access policies to reduce risk. Use Cases 1. Breach Detection Detects anomalies like impossible travel, unfamiliar sign-in patterns, and token theft. Automatically flags high-risk users and suggests remediation steps. 2. Policy Optimization Recommends Conditional Access policies tailored to your org’s risk profile. Helps reduce over-permissive access and enforce least privilege. 3. Incident Response Generates incident summaries and timelines. Suggests next steps and integrates with Microsoft Sentinel for deeper investigation. Comparison with Traditional SIEM Workflows Discussion Starter Have you tried Security Copilot in your environment yet? What use cases have you explored? How does it compare with your existing SIEM or XDR tools? Let’s share insights and build a stronger identity protection strategy together!
