Overview
Microsoft has published a new investigator playbook designed to help security teams reconstruct activity involving Microsoft 365 Copilot and Azure AI services. The release, authored by Phillip Misner and the Microsoft AI Red Team, responds to a practical gap that has emerged as AI systems become routine components of enterprise infrastructure: security teams are generating telemetry from AI interactions but lack a structured methodology to convert those signals into coherent incident accounts.
The playbook arrives as Microsoft acknowledges that real investigations involving AI systems are already underway — including prompt injection attempts and anomalous data access events — making the absence of structured IR guidance a measurable operational risk.
Technical Analysis
The playbook introduces a scope–context–signal investigation sequence:
- Scope: Identify who interacted with AI systems, when activity occurred, and which services were involved.
- Context: Expand to resource-level detail — what data was accessed, what the system returned, and whether behaviour aligns with baseline usage profiles.
- Signal: Evaluate detection alerts — prompt injection indicators, anomalous usage patterns, credential exposure — within the established chain of activity.
Telemetry is described as metadata-first, providing identity, timestamp, and resource context across interactions. This structure is drawn from Microsoft Purview (data governance and audit logs), Microsoft Defender (threat detection signals), and Microsoft Sentinel (SIEM correlation and investigation tooling).
The approach enables investigators to move from isolated alerts — such as a single prompt injection detection — to a full account that includes what data was exposed, which user or service principal initiated the chain, and whether the pattern constitutes normal usage, a policy violation, or an indicator of compromise.
Framework Mapping
- AML.T0051 (LLM Prompt Injection): Explicitly named as a detection scenario within the playbook.
- AML.T0057 (LLM Data Leakage): Addressed through resource context analysis — identifying what data may have been exposed during AI interactions.
- AML.T0040 (ML Model Inference API Access): Relevant to Azure AI service investigation paths covering API-level access patterns.
- AML.T0012 (Valid Accounts): Identity context is foundational to the scope phase, covering both user and service principal attribution.
- LLM01 (Prompt Injection) and LLM06 (Sensitive Information Disclosure): Both are core threat categories the playbook operationalises detection and response for.
Impact Assessment
This guidance is primarily relevant to enterprise environments running Microsoft 365 Copilot or Azure AI services at scale. Without structured IR methodology, security teams risk misclassifying AI-related incidents, underestimating data exposure, or failing to attribute activity to the correct identity. The playbook does not address a new vulnerability but fills a procedural gap that currently leaves many organisations under-prepared to handle AI-specific incidents with the same rigour applied to traditional endpoint or identity investigations.
Mitigation & Recommendations
- Adopt the playbook methodology as the baseline framework for all AI-related incident investigations in Microsoft environments.
- Validate telemetry completeness: Confirm that Purview audit logging, Defender for Cloud Apps signals, and Sentinel AI workbooks are fully configured before an incident occurs.
- Establish AI usage baselines: Anomaly detection requires known-good behaviour profiles. Document expected interaction patterns for Copilot and Azure AI workloads.
- Test prompt injection detection controls against the playbook’s signal criteria to confirm alert fidelity.
- Integrate AI incident workflows into existing SOC runbooks, treating AI system events as first-class investigation subjects rather than secondary signals.