Investigating an AI security incident starts from the same discipline as any security investigation, establish what happened, when, and how far it spread, applied with attention to the specific artifacts an AI system produces: model decisions, retrieved content, tool calls, policy evaluations. Having the traceability and audit trail infrastructure running throughout this content series in place before an incident occurs is what makes this investigation tractable rather than a slow reconstruction from fragments.
Establish the specific timeline and trigger first
Start by establishing exactly when the incident began and what specific event triggered it: a specific request, a specific piece of content, a specific model or configuration change. Use the audit ledger covered in architecture guidance to pull the specific records around the suspected trigger point, rather than starting from a broad, unscoped search across all activity, which is slower and less likely to converge on the actual starting point efficiently.
Reconstruct the specific decision sequence
For the specific interaction where the incident happened, reconstruct the full sequence: what content reached the model, what the model decided, what policy evaluation applied to any resulting action, and what actually executed. This reconstruction, supported by the traceability covered in governance guidance, is what turns "something went wrong" into a specific, understood causal chain, distinguishing whether the root cause was a successful manipulation of the model, a gap in policy enforcement, or a combination of both.
Determine whether the enforcement layer engaged as intended
A key investigative question is whether the policy enforcement layer, if one applies to the affected action, actually evaluated the request and what it decided, covered throughout policy enforcement guidance. If the enforcement layer correctly denied the request but the incident still occurred through some other path, that points to a gap elsewhere in the architecture. If the enforcement layer should have evaluated the request but didn't, that points to an integration or wiring gap in the enforcement layer's own deployment.
Search for similar patterns across other sessions and identities
Once the specific triggering pattern is understood, search the broader audit ledger for similar patterns across other sessions and identities, following the same content or technique. This determines the actual scope of the incident, whether it affected only the specific interaction that first surfaced it or a broader set of interactions nobody had yet individually noticed, which matters for accurately communicating impact to affected parties and stakeholders.
Check whether the same gap exists in other, similar systems
If the incident traces back to a specific architectural gap, a missing authorization check, an unfiltered retrieval source, check whether other AI systems in the organization share the same gap, using the inventory covered in governance guidance to identify similar systems that might be equally exposed. An incident investigation that only fixes the specific system where the issue first surfaced, with no check for the same gap elsewhere, risks leaving the same vulnerability active in a sibling system.
Preserve evidence appropriately for the incident's severity
For significant incidents, particularly ones that might carry legal, regulatory, or customer notification implications, preserve the relevant audit records and any other supporting evidence with appropriate care, coordinating with legal counsel on preservation requirements before any routine data retention or cleanup process might otherwise remove relevant records.
Distinguish root cause from proximate cause
An incident's proximate cause might be a specific successful prompt injection, while its root cause might be a missing authorization check that would have contained that injection's consequence regardless of whether the injection itself succeeded. Address both: fixing only the proximate cause, hardening against that specific injection technique, leaves the root cause, the missing containment, wide open for the next technique that succeeds at the language level.
Document findings for both immediate response and long-term learning
Document the investigation's findings in enough detail to support the immediate response, communication, remediation, and to inform longer-term learning, feeding into the incident pattern tracking covered in incident reporting guidance and potentially into updated requirements or design guidance for future features, following the lifecycle feedback loop covered in secure development guidance.
Where TELEON fits
TELEON's audit trail, applied through the gateway or supported middleware, provides the reconstructable record of content, decisions, and outcomes that this investigation process depends on, and its policy enforcement records directly answer the question of whether enforcement engaged as intended for the specific incident under investigation.
Establishing the timeline, reconstructing the full causal chain, and determining the appropriate remediation and communication response remain investigation work that has to be performed directly by the team responding to a specific incident.
A short checklist
- Establish the specific timeline and triggering event first.
- Reconstruct the full decision sequence for the affected interaction.
- Determine whether the policy enforcement layer engaged as intended.
- Search for similar patterns across other sessions and identities to
determine full scope.
- Check whether the same architectural gap exists in similar systems.
- Preserve evidence appropriately, coordinating with legal counsel for
significant incidents.
- Distinguish and address both root cause and proximate cause.
- Document findings for both immediate response and long-term learning.
Investigating an AI security incident well depends on the traceability and audit infrastructure being in place before the incident occurs, since reconstructing a specific decision sequence after the fact is only possible if the relevant content, decisions, and outcomes were actually recorded in a connected, reconstructable way.