Building resilient AI programmes requires a feedback loop between offensive testing, runtime detection, and governance.
Establish the telemetry backbone
Start with signal collection across inference, training, and prompt orchestration layers. Normalize traces so threat-hunting queries can correlate user identities with model responses and retrieval context.
- Capture prompt/response pairs with secrets filtered.
- Retain retrieval metadata (vector hits, knowledge articles).
- Emit structured events for abuse heuristics (prompt injection, data exfil attempts).
Run continuous adversarial testing
Red teams should automate exploit discovery against the telemetry pipeline.
Treat your red/blue programme like an SRE discipline. Every finding must have an attached detection or block.
Close the loop with response playbooks
Feed detections into pre-approved guardrail actions:
- Flag high-risk interactions for human review in near real-time.
- Trigger sensitivity aware fallback answers.
- Record investigator notes for audit readiness.
Over time, this creates an asset library of guardrails, detections, and runbooks that can be tailored to product teams.