Integration

AI Integration for Microsoft Sentinel with Teams

Bridge the gap between your SIEM and collaboration platform. Automate incident summarization, tag relevant experts, and create Teams meetings for war rooms directly from Microsoft Sentinel using AI.

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ARCHITECTURE AND ROLLOUT

Where AI Connects Sentinel to Teams for Faster Response

A practical blueprint for integrating Microsoft Sentinel with Microsoft Teams to accelerate incident response through AI-powered collaboration.

This integration connects the Microsoft Sentinel Incidents API and Automation Rules to the Microsoft Graph API for Teams. The core workflow is event-driven: when a new, high-severity incident is created or updated in Sentinel, an Azure Logic App or Azure Function is triggered. This serverless component uses an AI model (like GPT-4 via Azure OpenAI Service) to analyze the incident's raw alert data, entities (users, hosts, IPs), and investigation graph. The AI generates a concise, plain-language summary and identifies the relevant security owners, on-call engineers, or application teams based on the affected assets or attack patterns. This enriched payload is then posted as an adaptive card to a designated Teams channel (e.g., #soc-war-room) via an incoming webhook connector or the Graph API, automatically tagging the identified experts using @mentions.

The AI's role is critical for moving beyond simple alert forwarding. It transforms technical log data into an actionable narrative, answering: What happened? Which systems/users are impacted? What's the likely attacker intent (MITRE ATT&CK)? This allows the SOC analyst to bypass manual triage and immediately engage the right system owner or network engineer in Teams. For complex incidents, the same automation can create a Teams meeting in the channel with a pre-populated agenda based on the AI's analysis, instantly convening a war room. This cuts the traditional 'alert -> ticket -> assignment -> discovery' cycle from hours to minutes, as the collaborative investigation begins in the tool where technical teams already operate daily.

Rollout should start with a pilot for a single, high-volume alert type (e.g., Impossible Travel or Malicious Power Shell). Governance is essential: implement approval steps in the Logic App for incidents above a certain severity before posting to Teams, and maintain a clear audit trail of all AI-generated summaries and actions taken. Use Role-Based Access Control (RBAC) on the Sentinel Automation Account and limit the Teams posting permissions to specific channels. This ensures the integration augments—not disrupts—existing SOC processes, providing a force multiplier for faster, more informed collective response.

ARCHITECTURE BLUEPRINT

Key Integration Touchpoints in Sentinel and Teams

Automating Alert-to-Channel Workflows

This integration surface connects Microsoft Sentinel's Incidents queue and Automation Rules to Microsoft Teams via the Graph API or Incoming Webhooks. When a high-severity incident is created or updated, an AI agent analyzes the raw alerts, entities (hosts, users, IPs), and related log context to generate a concise, actionable summary.

The summary is then posted to a designated Teams channel. The post can:

Tag relevant security team members or subject matter experts based on the incident's MITRE ATT&CK tactics or affected assets.
Include interactive Adaptive Cards with buttons to Acknowledge, Escalate, or View in Sentinel.
Dynamically create a new Teams meeting (a "war room") for critical incidents, adding the tagged experts and posting the meeting link back to the channel.

Key APIs: Microsoft.Graph.Chats, Microsoft.Graph.Teams, Microsoft.SecurityInsights/incidents.

MICROSOFT SENTINEL + TEAMS

High-Value Use Cases for AI-Driven Collaboration

Integrating AI between Microsoft Sentinel and Microsoft Teams transforms isolated security alerts into collaborative, actionable incidents. This bridges the gap between SOC workflows and expert teams, accelerating response by embedding intelligence directly into the communication channels where decisions are made.

Automated Incident Summaries in Teams Channels

AI analyzes a new Microsoft Sentinel incident—synthesizing alerts, entities, and log context—and posts a concise, actionable summary to a designated Teams channel. The summary includes severity, affected users/hosts, and the likely ATT&CK tactic, enabling the entire team to grasp the situation in seconds without logging into Sentinel.

Minutes -> Seconds

Team awareness

Dynamic Expert Tagging & War Room Creation

Based on the incident's entities (e.g., a specific server, application, or user role), AI identifies and @mentions relevant subject-matter experts directly in the Teams post. For high-severity incidents, it can automatically create a dedicated 'War Room' Teams meeting with a pre-populated agenda and link it in the channel, ensuring immediate, focused collaboration.

Same day

Expert mobilization

Interactive Triage & Response from Teams

The AI-powered Teams post includes adaptive card buttons for common response actions (e.g., 'Acknowledge', 'Escalate', 'Run Containment Playbook'). Analysts can trigger Sentinel automation rules or SOAR playbooks directly from Teams, with status updates posted back to the thread. This keeps the workflow and audit trail centralized within the collaboration space.

Click -> Action

Response execution

Contextual Evidence Fetch for Discussion

During a Teams discussion, participants can ask natural language questions (e.g., "What were this user's logins in the last 24 hours?"). An AI agent, with secure access to Sentinel, translates the query into KQL, executes it, and posts a summarized result back into the thread. This provides on-demand evidence without context-switching.

Hours -> Minutes

Evidence retrieval

Automated Post-Incident Reporting to Channels

When an incident is closed in Sentinel, AI generates a closure summary and root cause analysis. It then posts this report to the relevant Teams channel and tags the participants, creating a seamless record for lessons learned. The report can be saved as a Wiki page in the Team for future reference and compliance.

1 sprint

Knowledge capture

Governed Broadcasts for Threat Intelligence

AI monitors threat intelligence indicators and high-fidelity alerts in Sentinel. When a critical threat is confirmed to be relevant to the environment (e.g., a new vulnerability affecting deployed software), it drafts and posts a structured advisory to a broad 'Security Broadcast' Teams channel, ensuring rapid, policy-controlled organizational awareness.

Batch -> Real-time

Threat comms

PRACTICAL AUTOMATION PATTERNS

Example AI-Powered Workflows from Sentinel to Teams

These workflows demonstrate how AI can bridge Microsoft Sentinel incidents with Microsoft Teams collaboration, moving from detection to coordinated response without manual handoffs. Each pattern is built using Sentinel automation rules, Logic Apps, or Azure Functions, with AI agents handling summarization, routing, and action initiation.

Trigger: A Microsoft Sentinel incident is created or updated with a severity of High or Critical.

AI Agent Action:

The agent calls the Sentinel API to retrieve the incident details, including related alerts, entities (users, hosts, IPs), and the incident description.
Using a large language model (LLM), it generates a concise, non-technical summary for leadership and a detailed technical summary for analysts.
The agent uses the Microsoft Graph API to:
- Create a new Teams channel named using a convention (e.g., INC-12345-Suspected-BEC).
- Post the incident summaries as the first message.
- @mention the SOC-Leads team and any specific individuals tagged as incident-commander in the Sentinel incident.
- Create a new Teams meeting titled "War Room: [Incident Title]" for immediate collaboration, add the SOC team as required attendees, and post the join link to the new channel.

System Update: The Sentinel incident is tagged with the newly created Teams channel ID and meeting link for easy reference. An automation rule updates the incident status to Active.

CONNECTING SENTINEL INCIDENTS TO TEAMS COLLABORATION

Typical Implementation Architecture and Data Flow

A practical architecture for routing critical Microsoft Sentinel incidents into Microsoft Teams channels, using AI to summarize context and trigger war room collaboration.

The integration is typically anchored by a secure, event-driven workflow. A Logic App or Azure Function is triggered by a Microsoft Sentinel Automation Rule when a high-severity incident is created or updated. This serverless function acts as the orchestrator, calling the Microsoft Sentinel API to fetch the full incident details, including related alerts, entities (users, hosts, IPs), and comments. It then packages this raw data and sends it to an AI processing layer—often an Azure OpenAI Service deployment or a custom model endpoint—which generates a concise, plain-language summary of the incident's root cause, impacted assets, and immediate risks.

The processed output is then posted to a designated Microsoft Teams channel via the Graph API or an Incoming Webhook connector. The message is structured to include the AI-generated summary, key indicators, and deep links back to the Sentinel incident for full context. Crucially, the integration can use the Teams Adaptive Card schema to create interactive messages with buttons for common actions like Acknowledge, Escalate, or Create War Room. Based on the entities involved (e.g., a compromised server name), the system can also @mention pre-defined security group tags or specific expert roles within the Teams channel to ensure the right personnel are notified immediately.

For war room activation, the flow can automatically create a new Teams meeting via the Graph API, populate the agenda with the incident summary, and post the join link in the channel. Governance is maintained by logging all automation steps—API calls, AI prompts, and post actions—to a dedicated Log Analytics workspace for audit. Rollout follows a phased approach: starting with a single, low-severity alert type to validate summarization accuracy and user notifications, then expanding to broader incident categories with approval gates managed through a Sentinel Playbook or conditional logic in the Azure Function before any automated meeting creation or user tagging is enabled.

IMPLEMENTATION PATTERNS

Code and Payload Examples

Triggering Teams Notifications from Sentinel

When a high-severity incident is created in Microsoft Sentinel, an Azure Logic App is triggered via a webhook. This example shows the HTTP request payload sent from Sentinel to the Logic App, containing the core incident context needed for AI processing and Teams posting.

json
{
  "schemaId": "Microsoft.Insights/alert",
  "data": {
    "essentials": {
      "alertId": "inc-2024-12345",
      "alertRule": "Suspicious PowerShell Execution",
      "severity": "High",
      "monitoringCondition": "Fired",
      "startDateTime": "2024-05-15T14:30:00Z"
    },
    "alertContext": {
      "IncidentNumber": 12345,
      "Tactics": ["Execution"],
      "Entities": [
        { "Type": "Host", "HostName": "workstation-01" },
        { "Type": "Account", "Name": "jdoe" }
      ],
      "IncidentUrl": "https://portal.azure.com/#@tenant.com/resource/subscriptions/..."
    }
  }
}

The Logic App receives this payload, extracts key fields, and passes the alertContext to an Azure Function for AI summarization before posting to Teams.

AI-ENHANCED SOC COLLABORATION

Realistic Time Savings and Operational Impact

How integrating AI between Microsoft Sentinel and Microsoft Teams changes the speed and quality of incident response, focusing on collaboration and war room activation.

Workflow Stage	Before AI Integration	After AI Integration	Operational Notes
Initial Incident Triage	Analyst manually reviews Sentinel incident, then switches to Teams to notify team	AI automatically posts summarized incident to designated Teams channel with tags	Reduces context-switching; ensures immediate, structured visibility
Expert Identification & Tagging	Manual lookup of on-call roster or subject matter experts in directory	AI analyzes incident entities (hosts, users, apps) and tags relevant experts in Teams post	Accelerates war room formation; leverages organizational knowledge
War Room Coordination	Analyst creates meeting manually, copies incident details, sends calendar invites	AI automatically creates a Teams meeting from the channel post with incident context pre-loaded	Meeting link and context are instantly available; reduces administrative overhead
Incident Briefing & Handoff	Lead analyst verbally summarizes or pastes raw log snippets into chat	AI provides a concise, structured narrative in the Teams post: timeline, entities, confidence	Enables faster analyst onboarding; provides consistent briefing quality
Evidence Collection & Sharing	Analysts share screenshots, log files, and notes across multiple chat threads	AI can suggest and pin key evidence (e.g., related alerts, entity graphs) to the Teams channel tab	Centralizes critical data; reduces noise and search time in chat
Status Reporting to Management	Manual compilation of updates from chat into email or slide for stakeholders	AI can generate periodic summary updates in the channel based on analyst comments and new data	Automates stakeholder comms; allows analysts to focus on investigation
Post-Incident Documentation	Manual synthesis of chat history, meeting notes, and Sentinel timeline for report	AI drafts a preliminary incident report from the channel's activity, comments, and resolved actions	Creates a strong first draft; captures tribal knowledge before it dissipates

ARCHITECTING A CONTROLLED DEPLOYMENT

Governance, Security, and Phased Rollout

Integrating AI into a critical security workflow requires a deliberate approach to control, security, and change management.

A production-ready integration between Microsoft Sentinel and Microsoft Teams is built on a secure, event-driven architecture. The core flow typically uses Azure Logic Apps or an Azure Function triggered by a Microsoft Sentinel Automation Rule. This ensures the AI agent only processes incidents that meet specific severity, status, or tag criteria. The agent queries Sentinel via the Graph API for Sentinel to gather the full incident context—alerts, entities, comments—before calling an LLM API (like Azure OpenAI) with a carefully engineered system prompt. The generated summary and expert tagging recommendations are then posted to a designated Teams channel via the Microsoft Graph API or the Teams webhook connector, with strict RBAC ensuring the service principal has only the necessary ChannelMessage.Send and SecurityIncidents.Read.All permissions.

To manage risk, implement a phased rollout. Start with a monitoring-only phase, where summaries are posted to a private SOC channel for analyst review without automated tagging or meeting creation. This validates summary accuracy and workflow fit. Next, introduce expert tagging in a pilot group, using a controlled allow-list of Teams users. Finally, enable automated meeting creation for critical, confirmed incidents, using a predefined Teams template. Throughout, maintain a complete audit trail: log all AI-generated content, API calls, and user interactions to an isolated Log Analytics workspace for performance monitoring, cost tracking, and compliance reviews.

Governance is critical. Establish a prompt management system to version-control and test system prompts that instruct the LLM to avoid hallucinations, maintain a neutral tone, and cite source data. Implement content filtering (e.g., Azure OpenAI's) and a human-in-the-loop approval step for any action that modifies a Sentinel incident or @mentions a user. Define clear rollback procedures, such as disabling the Automation Rule. This controlled, phased approach allows security teams to harness AI for collaboration speed while maintaining the integrity and security of their SOC operations. For related architectural patterns, see our guides on /integrations/security-information-and-event-platforms/ai-integration-for-microsoft-sentinel-incident-summaries and /integrations/unified-communications-platforms/ai-integration-for-microsoft-teams-meeting-summaries.

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AI INTEGRATION FOR MICROSOFT SENTINEL WITH TEAMS

Frequently Asked Questions

Practical questions about integrating AI between Microsoft Sentinel and Microsoft Teams to automate incident collaboration, expert routing, and war room coordination.

The AI uses a combination of static mapping and dynamic analysis to route incidents. The typical logic includes:

Primary Mapping: The Sentinel incident's severity, assigned owner, or custom tags are matched against a configuration table that maps these attributes to specific Teams channels (e.g., High-Severity -> #sentinel-critical-alerts).
Dynamic Context Analysis: For incidents without a clear mapping, the AI analyzes the incident's entities (IPs, users, hosts), alert titles, and descriptions using an LLM to infer the relevant team or technology domain (e.g., "Azure SQL Database" and "SQL Injection" suggests the #appsec-database channel).
Fallback Logic: If confidence is low, the system can post to a general #sentinel-incidents channel and use @here or tag a designated incident commander for manual routing.

This routing logic is defined in a secure configuration file or database, often integrated with Sentinel's Watchlists or Automation Rules for easy SOC management.

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.

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