Document the evidence behind a generated incident timeline

You have just received an incident timeline generated by Microsoft Security Copilot. It is concise, it tells a story, and it is ready for your post-incident review. The first question is not whether the timeline is correct, but how you can demonstrate that it is grounded in your environment’s actual data. A generated narrative is only useful as evidence if you can trace every event back to a specific data source and a specific query.

Security Copilot is a generative AI assistant that integrates with your existing security infrastructure. It does not operate in a vacuum. It relies on connectors to ingest data and plugins to extend its analytical capabilities. The Security Copilot Community README highlights the modular nature of these components, noting that plugins can be added to extend functionality. This modularity is key to your verification process: if a plugin is missing or a connector is stale, the timeline will have gaps or inaccuracies.

Verify the data sources before accepting the narrative

The timeline is generated based on the data available to the service at the time of the query. If your data sources are not configured correctly, the AI cannot retrieve the events it needs. You must confirm that the connectors feeding the timeline are active and healthy.

1. Identify the enabled connectors: Open your Security Copilot environment and review the list of connected data sources. These are the bridges between the AI and your logs. If a connector is disabled or misconfigured, the timeline will reflect only a partial view of the incident.
2. Check plugin status: Plugins provide specific analytical functions. Ensure that the relevant plugins are active. The community repository structure suggests that plugins are modular components that can be added or removed. If a plugin required for a specific analysis (such as a KQL-based plugin) is missing, the timeline may lack the necessary detail.
3. Validate data freshness: Stale data leads to outdated timelines. Check the last successful sync time for each connector. If the data is days old, the timeline may miss critical events that occurred in the interim.

Trace events to their origins

Each event in the timeline should be traceable to a specific log entry or alert in your source system. This is where the “evidence” part comes in. You need to verify that the AI has not hallucinated or synthesized an event that does not exist in your logs.

• Cross-reference with source systems: Manually check the source system (e.g., Microsoft Sentinel, Defender for Cloud) for the event ID or timestamp mentioned in the timeline. If the timeline mentions a specific alert, find that alert in the source system and confirm its details match.
• Document discrepancies: If an event is missing or incorrect, note it. This is critical for post-incident review. A discrepancy between the AI-generated timeline and the raw logs is a sign that the connector or plugin needs adjustment.
• Record the prompt used: Security Copilot generates responses based on the prompts you provide. If you used specific KQL queries or prompts to generate parts of the timeline, save them. This is a user-side best practice for reproducibility, not a feature guaranteed by the tool. Without this record, it is difficult to reproduce the analysis later.

Document the process

Documentation is not just about recording the final timeline. It is about recording the process that led to it. This includes:

• The plugins and connectors used: List them. This helps in reproducing the analysis later.
• The queries or prompts used: If you used specific KQL queries or prompts to generate parts of the timeline, save them.
• The limitations: Note any data sources that were unavailable or incomplete. If a connector was down during the incident, the timeline will have a gap. Documenting this gap is as important as documenting the events that were found.

Use the community for best practices

The Security Copilot Community is a resource for learning how others are using the tool. While the README is a guide for contributors, it reflects the active development and community engagement around the product. Engaging with the community can provide insights into best practices for evidence documentation, such as how to structure plugins for maximum traceability.

Next steps

Once you have verified the timeline and documented the process, you can use this evidence for your post-incident review. If you find gaps or inaccuracies, use the community resources to improve your plugins and connectors. The goal is to ensure that future timelines are not only generated quickly but are also verifiable and trustworthy.

By systematically checking data sources, tracing events, and documenting the process, you turn a generated summary into actionable evidence. This approach ensures that your incident response is based on facts, not just AI-generated narratives.