Frameworks // an overview of existing methodologies
An overview of existing frameworks and methodologies relevant to AI incident work: reporting regimes, harm and failure taxonomies, causal-factor analysis, red-team studies of agent behavior, and adjacent practice from insider-threat research. Each is a useful starting point for part of the job and worth reading further; none yet describes how to conduct an AI incident investigation end to end, which is the gap the playbooks aim to fill. Full annotations are on the repository page.
| Framework | Covers | Does not cover |
|---|---|---|
| Ezell, Roberts-Gaal & Chan (2025), Incident Analysis for AI Agents | Causal-factor analysis; the data categories an analysis needs | Goal-directed (intentional-analog) cases; competing-hypothesis work |
| Microsoft AI Red Team taxonomy (2025) | Misalignment / misuse / operational-failure vocabulary | Any investigative procedure; categories often inseparable in practice |
| OECD common reporting framework (2025) | Baseline definitions; 29 reporting criteria | How to establish the facts being reported |
| EU AI Act Art. 73 + draft guidance (2025) | What to report, to whom, by when; template | How to conduct the investigation it mandates |
| Anthropic (Lynch et al., 2025); Apollo (Meinke et al., 2024); CLTR (2026) | Red-team evidence and at-scale detection of scheming behavior | What happens after detection |
| MITRE ATLAS; GenAI-IRF (Jakoby, 2026) | Adversarial techniques; cyber-IR bridging | Agent-initiated behavior; investigation depth |
| CERT insider-threat corpus (Cappelli et al., 2012); Shaw & Sellers (2015) | The intentional/unintentional asymmetry: the closest existing model | AI systems; needs adaptation, which is the open problem |