Microsoft has released a new investigator playbook designed to help security teams reconstruct and analyze AI activity within enterprise environments, addressing a growing need as AI systems like Copilot become embedded in everyday business workflows. The playbook provides a structured methodology for investigating incidents involving Microsoft 365 Copilot and Azure AI services, from prompt injection attempts to unexpected data access patterns.

Why Is Reconstructing AI Activity Becoming a Security Priority?

As AI systems become part of standard business operations, security teams face a new investigative challenge: understanding what happened inside an AI interaction. Unlike traditional endpoint or cloud infrastructure investigations, AI activity generates signals scattered across multiple systems without a clear framework for connecting them into a coherent narrative. Security teams are already investigating incidents involving Microsoft 365 Copilot and Azure AI services, but without structured methodology, isolated signals don't tell the full story.

The core problem is that AI telemetry exists, but it's fragmented. Investigators need to piece together who initiated an interaction, when it occurred, which resources were involved, what data the system accessed, and whether that activity aligns with expected behavior. Microsoft's new playbook operationalizes this investigation process across its security products.

How Does the Investigation Methodology Work?

The playbook follows a three-step investigative sequence: scope, context, and signal. Investigations begin by identifying who interacted with AI systems, when the activity occurred, and which services were involved. From there, investigators expand into resource context, examining what the system accessed, what data may have been exposed, and how that activity aligns with expected behavior. Finally, detection signals, including prompt injection attempts, anomalous usage patterns, or credential exposure alerts, are evaluated within that broader chain of activity.

The methodology leverages telemetry already available across Microsoft Purview, Defender, and Sentinel. By bringing together required configuration, queries, and detection patterns into a single working model, the playbook enables investigators to follow AI activity across tools with fewer ad hoc pivots. It also extends the model to agent-based systems, where the investigative picture expands to include which agents are deployed, how they are configured, what data they are authorized to access, and whether that authorization was used as expected.

Steps to Investigate AI Activity in Your Enterprise

• Establish Scope: Identify who interacted with AI systems, when the activity occurred, and which services were involved to create a baseline understanding of the incident.
• Analyze Resource Context: Determine what data the AI system accessed during interactions, which resources were touched, and whether that access aligns with the user's normal permissions and role.
• Evaluate Detection Signals: Assess whether observed behavior reflects normal usage, policy violations, or indicators of active threat conditions, including prompt injection attempts and anomalous usage patterns.
• Review Agent Configuration: For agent-based systems, examine which agents are deployed, their configuration settings, and whether their authorized data access was used as intended.

What Specific Threats Is the Playbook Designed to Address?

The playbook addresses several emerging threat categories in AI environments. Prompt injection attempts, where attackers manipulate AI inputs to bypass security controls, represent one key threat. Unexpected data access, where AI systems retrieve or process information beyond their intended scope, is another. Credential exposure through AI interactions, where sensitive authentication information is inadvertently revealed or logged, also requires investigation.

The practical outcome is significant: response teams can move from isolated signals to a reconstructed account of observed activity. They can scope AI usage patterns, understand what data was accessed during specific interactions, and assess whether observed behavior is consistent with normal usage, policy violations, or indicators of active threat conditions across Microsoft security services.

As AI becomes part of everyday business workflows, response teams need the same investigative rigor they apply to endpoints, identities, and cloud infrastructure. The ability to determine what happened, what data was involved, and whether activity was authorized is quickly becoming a core incident response capability. Microsoft's playbook provides the structured framework and tooling to answer these questions consistently across enterprise environments.