Autonomous Investigation and Containment of AI Threats

AI-scale threats move at machine speed, and a human in the loop for every step is a bottleneck that attackers exploit. This series builds the discipline of autonomous investigation and containment for AI threats from the ground up: why machine-speed threats demand machine-speed containment and where the human belongs instead, how to structure investigation as a pipeline of signal triage, evidence gathering, and hypothesis testing, the containment primitives an agent responder actually needs — quarantine, capability revocation, and safe rollback, how to put guardrails on the responder itself so the defender is never weaponized, and how to measure response with MTTD, MTTR, and the false-containment cost. Grounded in the NIST incident-handling and Zero Trust guidance, the NIST AI RMF, the OWASP Agentic Security Initiative and LLM Top 10, MITRE ATLAS and ATT&CK, and the peer-reviewed literature on anomaly detection and detection-response measurement.