AI Integration for Microsoft Sentinel Incident Summaries

AI integration targets the Incidents queue and Incident details pane in Microsoft Sentinel. The workflow begins when an incident is created from correlated alerts. An AI agent, triggered via an Automation Rule or Logic App, ingests the incident's raw data: the alert names, descriptions, entities (users, IPs, hosts), and related raw log entries from the Incident Timeline. It synthesizes this disparate data into a concise, plain-language summary that answers the core questions: What happened, to whom, from where, and what was the potential impact? This summary is written back to the incident's Description or a custom field, providing immediate context for the assigned analyst.

Implementation requires secure API calls from Azure to your chosen LLM (e.g., Azure OpenAI Service). The architecture typically involves a serverless function (Azure Function) that uses the Microsoft Sentinel REST API to fetch incident context and the Log Analytics API to retrieve relevant raw logs. The prompt sent to the LLM is engineered with Sentinel-specific context, instructing it to structure the output, avoid hallucination by grounding in provided logs, and highlight high-risk entities. Governance is critical: all summaries should be logged for audit, and a human-in-the-loop approval step can be configured for high-severity incidents before the summary is committed. This reduces manual note-taking from 15-30 minutes per complex incident to near-zero, allowing analysts to focus on investigation and response.

Rollout should be phased. Start with low-severity incidents (Sev 3) to validate summary quality and tune prompts. Use Sentinel Workbooks to monitor metrics like analyst feedback on summary usefulness and time-to-acknowledge. A key success factor is integrating this AI summary into downstream workflows—such as populating the initial post in a Microsoft Teams war room or auto-filling the "Description" field in a connected ServiceNow ticket. This creates a cohesive narrative thread from detection to resolution. For teams managing our related service, see our guide on Generative AI for Microsoft Sentinel Incidents for deeper implementation patterns.

The integration is typically built as a secure, serverless workflow triggered by the Microsoft Sentinel Incidents API. When a new incident is created or reaches a defined severity threshold (e.g., High or Medium), an Azure Logic App or Azure Function is invoked. This function retrieves the full incident context, including the raw alerts, entities (hosts, users, IPs), and related log data from the Azure Log Analytics workspace using Kusto Query Language (KQL). This raw, often disparate, data is then packaged into a structured prompt for a large language model (LLM).

The LLM call—using Azure OpenAI Service for data residency and security compliance—synthesizes the technical details into a concise, plain-language summary. The prompt is engineered to focus on the "who, what, when, and where": the affected user or host, the primary alert type and MITRE ATT&CK tactic, the timeline of key events, and the potential scope or impact. The generated summary, along with key extracted indicators, is then written back to the incident's comments or custom details fields via the Sentinel API, providing immediate value to the investigating analyst without requiring them to leave the console.

Governance and safety are wired into the architecture. All prompts and model outputs are logged to a dedicated audit Log Analytics table for review. A human-in-the-loop approval step can be inserted for critical incidents before the summary is posted. The system is designed for incremental rollout, starting with a subset of alert product names (e.g., "Microsoft Defender for Endpoint") to validate summary quality and analyst feedback before scaling to the entire incident queue.

The integration architecture typically involves a secure, serverless function (like an Azure Function) triggered by a Microsoft Sentinel Automation Rule when a new incident is created. This function calls an AI inference endpoint—hosted in your Azure tenant for data residency—passing only the necessary, anonymized context from the incident's alerts, entities, and related log data. The AI service returns a structured summary, which is appended to the incident's Comments or a custom field via the Sentinel API. All data flows stay within your Azure environment, and API calls are authenticated using Azure Managed Identities, avoiding hard-coded secrets.

A phased rollout is critical for adoption and tuning. Start with a monitoring-only phase: deploy the integration to a single, low-severity incident queue (e.g., Informational or Low). The AI-generated summaries are written to a log for review without modifying live incidents. This allows SOC leads to validate accuracy, refine the prompt templates, and establish a baseline. Next, move to an assistive phase, where summaries are written to a custom incident field visible to analysts, who can choose to use or ignore them. Finally, after confidence is built, enable the automation phase where high-fidelity summaries are automatically posted to all incidents in designated queues, reducing manual work.

Governance is built around the AI model's behavior and data handling. Implement prompt management to version-control and audit the instructions sent to the LLM, ensuring they avoid hallucinations and stick to factual synthesis from the provided context. Establish a human review loop for edge cases by routing low-confidence summaries (based on model scoring or anomaly detection) to a senior analyst for approval. All summary generation activity should be logged to a dedicated Log Analytics workspace for an audit trail, capturing the incident ID, timestamp, prompt version, and any user feedback. This creates a closed-loop system where the integration's performance can be continuously measured and improved, ensuring it remains a reliable force multiplier for the SOC.