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Sentinel Foundry - MCP Server (Preview) (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.Security Copilot Integration with Microsoft Sentinel - Why Automation matters now
Security Operations Centers face a relentless challenge - the volume of security alerts far exceeds the capacity of human analysts. On average, a mid-sized SOC receives thousands of alerts per day, and analysts spend up to 80% of their time on initial triage. That means determining whether an alert is a true positive, understanding its scope, and deciding on next steps. With Microsoft Security Copilot now deeply integrated into Microsoft Sentinel, there is finally a practical path to automating the most time-consuming parts of this workflow. So I decided to walk you through how to combine Security Copilot with Sentinel to build an automated incident triage pipeline - complete with KQL queries, automation rule patterns, and practical scenarios drawn from common enterprise deployments. Traditional triage workflows rely on analysts manually reviewing each incident - reading alert details, correlating entities across data sources, checking threat intelligence, and making a severity assessment. This is slow, inconsistent, and does not scale. Security Copilot changes this equation by providing: Natural language incident summarization - turning complex, multi-alert incidents into analyst-readable narratives Automated entity enrichment - pulling threat intelligence, user risk scores, and device compliance state without manual lookups Guided response recommendations - suggesting containment and remediation steps based on the incident type and organizational context The key insight is that Copilot does not replace analysts - it handles the repetitive first-pass triage so analysts can focus on decision-making and complex investigations. Architecture - How the Pieces Fit Together The automated triage pipeline consists of four layers: Detection Layer - Sentinel analytics rules generate incidents from log data Enrichment Layer - Automation rules trigger Logic Apps that call Security Copilot Triage Layer - Copilot analyzes the incident, enriches entities, and produces a triage summary Routing Layer - Based on Copilot's assessment, incidents are routed, re-prioritized, or auto-closed (Forgive my AI-painted illustration here, but I find it a nice way to display dependencies.) +-----------------------------------------------------------+ | Microsoft Sentinel | | | | Analytics Rules --> Incidents --> Automation Rules | | | | | v | | Logic App / Playbook | | | | | v | | Security Copilot API | | +-----------------+ | | | Summarize | | | | Enrich Entities | | | | Assess Risk | | | | Recommend Action| | | +--------+--------+ | | | | | v | | +-----------------------------+ | | | Update Incident | | | | - Add triage summary tag | | | | - Adjust severity | | | | - Assign to analyst/team | | | | - Auto-close false positive| | | +-----------------------------+ | +-----------------------------------------------------------+ Step 1 - Identify High-Volume Triage Candidates Not every incident type benefits equally from automated triage. Start with alert types that are high in volume but often turn out to be false positives or low severity. Use this KQL query to identify your top candidates: SecurityIncident | where TimeGenerated > ago(30d) | summarize TotalIncidents = count(), AutoClosed = countif(Classification == "FalsePositive" or Classification == "BenignPositive"), AvgTimeToTriageMinutes = avg(datetime_diff('minute', FirstActivityTime, CreatedTime)) by Title | extend FalsePositiveRate = round(AutoClosed * 100.0 / TotalIncidents, 1) | where TotalIncidents > 10 | order by TotalIncidents desc | take 20 This query surfaces the incident types where automation will deliver the highest ROI. Based on publicly available data and community reports, the following categories consistently appear at the top: Impossible travel alerts (high volume, around 60% false positive rate) Suspicious sign-in activity from unfamiliar locations Mass file download and share events Mailbox forwarding rule creation Step 2 - Build the Copilot-Powered Triage Playbook Create a Logic App playbook that triggers on incident creation and leverages the Security Copilot connector. The core flow looks like this: Trigger: Microsoft Sentinel Incident - When an incident is created Action 1 - Get incident entities: let incidentEntities = SecurityIncident | where IncidentNumber == <IncidentNumber> | mv-expand AlertIds | join kind=inner (SecurityAlert | extend AlertId = SystemAlertId) on $left.AlertIds == $right.AlertId | mv-expand Entities | extend EntityData = parse_json(Entities) | project EntityType = tostring(EntityData.Type), EntityValue = coalesce( tostring(EntityData.HostName), tostring(EntityData.Address), tostring(EntityData.Name), tostring(EntityData.DnsDomain) ); incidentEntities Note: The <IncidentNumber> placeholder above is a Logic App dynamic content variable. When building your playbook, select the incident number from the trigger output rather than hardcoding a value. Action 2 - Copilot prompt session: Send a structured prompt to Security Copilot that requests: Analyze this Microsoft Sentinel incident and provide a triage assessment: Incident Title: {IncidentTitle} Severity: {Severity} Description: {Description} Entities involved: {EntityList} Alert count: {AlertCount} Please provide: 1. A concise summary of what happened (2-3 sentences) 2. Entity risk assessment for each IP, user, and host 3. Whether this appears to be a true positive, benign positive, or false positive 4. Recommended next steps 5. Suggested severity adjustment (if any) Action 3 - Parse and route: Use the Copilot response to update the incident. The Logic App parses the structured output and: Adds the triage summary as an incident comment Tags the incident with copilot-triaged Adjusts severity if Copilot recommends it Routes to the appropriate analyst tier based on the assessment Step 3 - Enrich with Contextual KQL Lookups Security Copilot's assessment improves dramatically when you feed it contextual data. Before sending the prompt, enrich the incident with organization-specific signals: // Check if the user has a history of similar alerts (repeat offender vs. first time) let userAlertHistory = SecurityAlert | where TimeGenerated > ago(90d) | mv-expand Entities | extend EntityData = parse_json(Entities) | where EntityData.Type == "account" | where tostring(EntityData.Name) == "<UserPrincipalName>" | summarize PriorAlertCount = count(), DistinctAlertTypes = dcount(AlertName), LastAlertTime = max(TimeGenerated) | extend IsRepeatOffender = PriorAlertCount > 5; userAlertHistory // Check user risk level from Entra ID Protection AADUserRiskEvents | where TimeGenerated > ago(7d) | where UserPrincipalName == "<UserPrincipalName>" | summarize arg_max(TimeGenerated, RiskLevel), RecentRiskEvents = count() | project RiskLevel, RecentRiskEvents Including this context in the Copilot prompt transforms generic assessments into organization-aware triage decisions. A "suspicious sign-in" for a user who travels internationally every week is very different from the same alert for a user who has never left their home country. Step 4 - Implement Feedback Loops Automated triage is only as good as its accuracy over time. Build a feedback mechanism by tracking Copilot's assessments against analyst final classifications: SecurityIncident | where Tags has "copilot-triaged" | where TimeGenerated > ago(30d) | where Classification != "" | mv-expand Comments | extend CopilotAssessment = extract("Assessment: (True Positive|False Positive|Benign Positive)", 1, tostring(Comments)) | where isnotempty(CopilotAssessment) | summarize Total = dcount(IncidentNumber), Correct = dcountif(IncidentNumber, (CopilotAssessment == "False Positive" and Classification == "FalsePositive") or (CopilotAssessment == "True Positive" and Classification == "TruePositive") or (CopilotAssessment == "Benign Positive" and Classification == "BenignPositive") ) by bin(TimeGenerated, 7d) | extend AccuracyPercent = round(Correct * 100.0 / Total, 1) | order by TimeGenerated asc For this query to work reliably, the automation playbook must write the assessment in a consistent format within the incident comments. Use a structured prefix such as Assessment: True Positive so the regex extraction remains stable. According to Microsoft's published benchmarks and community feedback, Copilot-assisted triage typically achieves 85-92% agreement with senior analyst classifications after prompt tuning - significantly reducing the manual triage burden. A Note on Licensing and Compute Units Security Copilot is licensed through Security Compute Units (SCUs), which are provisioned in Azure. Each prompt session consumes SCUs based on the complexity of the request. For automated triage at scale, plan your SCU capacity carefully - high-volume playbooks can accumulate significant usage. Start with a conservative allocation, monitor consumption through the Security Copilot usage dashboard, and scale up as you validate ROI. Microsoft provides detailed guidance on SCU sizing in the official Security Copilot documentation. Example Scenario - Impossible Travel at Scale Consider a typical enterprise that generates over 200 impossible travel alerts per week. The SOC team spends roughly 15 hours weekly just triaging these. Here is how automated triage addresses this: Detection - Sentinel's built-in impossible travel analytics rule flags the incidents Enrichment - The playbook pulls each user's typical travel patterns from sign-in logs over the past 90 days, VPN usage, and whether the "impossible" location matches any known corporate office or VPN egress point Copilot Analysis - Security Copilot receives the enriched context and classifies each incident Expected Result - Based on common deployment patterns, around 70-75% of impossible travel incidents are auto-closed as benign (VPN, known travel patterns), roughly 20% are downgraded to informational with a triage note, and only about 5% are escalated to analysts as genuine suspicious activity This type of automation can reclaim over 10 hours per week - time that analysts can redirect to proactive threat hunting. Getting Started - Practical Recommendations For teams ready to implement automated triage with Security Copilot and Sentinel, here is a recommended approach: Start small. Pick one high-volume, high-false-positive incident type. Do not try to automate everything at once. Run in shadow mode first. Have the playbook add triage comments but do not auto-close or re-route. Let analysts compare Copilot's assessment with their own for two to four weeks. Tune your prompts. Generic prompts produce generic results. Include organization-specific context - naming conventions, known infrastructure, typical user behavior patterns. Monitor accuracy continuously. Use the feedback loop KQL above. If accuracy drops below 80%, pause automation and investigate. Maintain human oversight. Even at 90%+ accuracy, keep a human review step for high-severity incidents. Automation handles volume - analysts handle judgment. The combination of Security Copilot and Microsoft Sentinel represents a genuine step forward for SOC efficiency. By automating the initial triage pass - summarizing incidents, enriching entities, and providing classification recommendations - analysts are freed to focus on what humans do best: making nuanced security decisions under uncertainty. Feel free to like or/and connect :)Webinar Cancellation
Hi everyone! The webinar originally scheduled for April 14th on "Using distributed content to manage your multi-tenant SecOps" has unfortunately been cancelled for now. We apologize for the inconvenience and hope to reschedule it in the future. Please find other available webinars at: http://aka.ms/securitycommunity All the best, The Microsoft Security Community TeamWebinar Rescheduled: AI-Powered Entity Analysis in Sentinel's MCP Server
Hi folks! The webinar: AI-Powered Entity Analysis in Sentinel's MCP Server which was previously scheduled for: January 13th, 2026, has been rescheduled to: January 27th, 2026, at 9:00 AM PT. Please delete the old invite from your calendar and find the new one at aka.ms/securitycommunity. We apologize for the inconvenience and hope to see you there!Sentinel to Defender webinar series CANCELLED, will be rescheduled at a later date.
The Sentinel to Defender webinar series has been cancelled. Please visit aka.ms/securitycommunity to sign up for upcoming Microsoft Security webinars and to join the mailing list to be notified of future sessions. We apologize for any inconvenience.Modernize security operations to secure agentic AI—Microsoft Sentinel at Ignite 2025
Security is a core focus at Microsoft Ignite this year, with the Security Forum on November 17, deep dive technical sessions, theater talks, and hands-on labs designed for security leaders and practitioners. Join us in San Francisco, November 17–21, or online, November 18–20, to learn what’s new and what’s next across SecOps, data, cloud, and AI—and how to get more from the Microsoft capabilities you already use. This year, Microsoft Sentinel takes center stage with sessions and labs designed to help you unify data, automate response, and leverage AI-powered insights for faster, more effective threat detection. Featured sessions: BRK235: Power agentic defense with Microsoft Sentinel Explore Microsoft Sentinel’s platform architecture, graph intelligence, and agentic workflows to automate, investigate, and respond with speed and precision. BRK246: Blueprint for building the SOC of the future Learn how to architect a modern SOC that anticipates and prevents threats using predictive shielding, agentic AI, and graph-powered reasoning. LAB543: Perform threat hunting in Microsoft Sentinel Dive deep into advanced threat hunting, KQL queries, and proactive investigation workflows to sharpen your security operations. Explore and filter the full security catalog by topic, format, and role: aka.ms/Ignite/SecuritySessions. Why attend: Ignite is your opportunity to see the latest innovations in Microsoft Sentinel, connect with experts, and gain hands-on experience. Sessions will also touch on future directions for agentic AI and unified SOC operations, as outlined in Microsoft’s broader security roadmap. Security Forum (November 17): Kick off with an immersive, in‑person pre‑day focused on strategic security discussions and real‑world guidance from Microsoft leaders and industry experts. Select Security Forum during registration. Connect with peers and security leaders through these signature security experiences: Security Leaders Dinner—CISOs and VPs connect with Microsoft leaders. CISO Roundtable—Gain practical insights on secure AI adoption. Secure the Night Party—Network in a relaxed, fun setting. Register for Microsoft Ignite >
Data Connectors Storage Account and Function App
Several data connectors downloaded via Content Hub has ARM deployment templates which is default OOB experience. If we need to customize we could however I wanted to ask community how do you go about addressing some of the infrastructure issues where these connectors deploy storage accounts with insecure configurations like infrastructure key requirement, vnet intergration, cmk, front door etc... Storage and Function Apps. It appears default configuration basically provisions all required services to get streams going but posture configuration seems to be dismissing security standards around hardening these services.
Single Rule for No logs receiving (Global + Per-device Thresholds)
Hi everyone, I currently maintain one Analytics rule per table to detect when logs stop coming in. Some tables receive data from multiple sources, each with a different expected interval (for example, some sources send every 10 minutes, others every 30 minutes). In other SIEM platforms there’s usually: A global threshold (e.g., 60 minutes) for all sources. Optional per-device/per-table thresholds that override the global value. Is there a recommended way to implement one global rule that uses a default threshold but allows per-source overrides when a particular device or log table has a different expected frequency? Also, if there are other approaches you use to manage “logs not received” detection, I’d love to hear your suggestions as well. This is a sample of my current rule let threshold = 1h; AzureActivity | summarize LastHeartBeat = max(TimeGenerated) | where LastHeartBeat < ago(threshold)
