AI Incident Response Field Guide — Using UAIF™ and AI-IRF™ for Real-World AI Incidents
AI incidents rarely arrive as cleanly classified security tickets. A responder may first see an anomalous model output, an unexpected tool call, a retrieval result containing restricted information, a vendor model behaviour change, a physical-AI safety event, or an alert whose relationship to AI is not yet established. The operational problem is therefore twofold:
Purpose
AI incidents rarely arrive as cleanly classified security tickets. A responder may first see an anomalous model output, an unexpected tool call, a retrieval result containing restricted information, a vendor model behaviour change, a physical-AI safety event, or an alert whose relationship to AI is not yet established. The operational problem is therefore twofold:
Methodology Note
This guide is a practitioner-oriented implementation document derived from the controlled/current ODA3 source set for UAIF™ and AI-IRF™, together with the established GAISSF Ecosystem architecture.
Public framework-state check - 14 August 2026: ODA3 Institute’s current Framework Comparison page identifies UAIF™ v1.0 as Final Publication and AI-IRF™ v1.0 as Final Publication. A final practitioner guide does not change either underlying framework state, and Final Publication status does not by itself establish completed sector calibration, production-scale validation, legal recognition, or empirical reliability claims. ODA3 Institute was founded in March 2026. This guide therefore contains no claim of long-term client outcome data, proprietary incident telemetry, incident-frequency statistics, sector-wide effectiveness data, or mature certification-program operating history. Examples are hypothetical unless explicitly identified otherwise. The guide distinguishes: observed facts from causal hypotheses;
provider representations from enterprise-observed evidence;
classification from legal determination;
containment from proof of root cause;
recovery from proof of safety or effectiveness;
evidence presence from evidence sufficiency;
framework correspondence from regulatory equivalence.
AI-IRF™ v1.0 is verified in the corrected technical source and current AIIS v1.0.0 machine-readable companion as a 19-domain, 79-control framework. AIIS defines structured control records, playbook definitions and severity structures for that inventory. Its optional conformance_test structure is reserved for a future AIIS release and is not populated in v1.0.0. Organizations should therefore treat automation as implementation support, not as a substitute for human incident judgment or independent evidence review.
Use this guide when…
Use this guide when you need practitioner-oriented guidance within the stated UAIF / AI-IRF scope, while retaining the underlying framework, standard and evidence boundaries.
Intended audience: SOC/CSIRT leaders, incident responders, CISOs, Security Architects and AI security teams
What this guide supports
Structured practitioner understanding and preparation within its stated scope. It should be read with the relevant normative framework and current ODA3 documentation.
Incident sequence: Recognize → Classify with UAIF → Respond through AI-IRF.
What it does not establish
Use of this guide does not by itself establish implementation completeness, control effectiveness, conformity, certification, independent assurance, regulatory approval, legal compliance, ODA3 approval or authorization to use controlled marks.
Download
Notably Absent
47. What This Field Guide Does Not Establish This guide does not, by itself: establish that an observed AI event is a legally defined incident; determine legal reportability or notification deadlines; establish negligence, liability, malicious intent, or actor attribution; establish root cause from temporal correlation; establish that a provider caused an incident; establish that a model is safe or unsafe; establish…