AI Investigation Workflows

When an AI alert escalates from triage to investigation, the analyst needs a structured workflow: what logs to pull, what patterns to look for, what constitutes confirmation versus false positive, and what containment action to take if confirmed. AI investigations follow the same general framework as traditional investigations — detect, triage, investigate, contain, remediate — but the evidence sources and indicators of compromise are different.

1. Pull the Context Retrieve the full interaction log for the flagged session: all inputs, all outputs, the context window composition, and all tool invocations. The context tells you what the model saw, what it did, and whether the flagged behavior was caused by an attack or a legitimate edge case.
2. Correlate Across Agents Check whether other agents show unusual behavior in the same time window. A prompt injection attempt against one agent may be part of a broader campaign targeting multiple agents simultaneously. Correlation across agents reveals the scope of the threat.
3. Determine Impact If the investigation confirms a successful attack: what data was in the context window? What tool actions were executed? What outputs were sent to the user? The impact assessment determines the severity, the notification obligations, and the containment scope.