GAFAIG-S-002 — AI Incident Disclosure & Reporting Standard

Scope

This standard applies to AI incidents that may materially affect individuals, groups, systems, or public trust. Covered incidents include unintended outcomes, misuse, failures, near-misses, and emergent behaviors identified during operation or post-deployment review.

Incident classification

Incidents should be classified using defined severity tiers and confidence indicators. Classification may evolve as investigations proceed and new evidence becomes available.

• Severity: degree of actual or potential harm.
• Confidence: level of certainty regarding facts and causation.
• Scope: systems, users, or populations affected.

Disclosure expectations

Organizations are expected to disclose incidents in a manner proportionate to severity and confidence. Initial disclosures may be high-level and should be updated as facts are verified. GAFAIG discourages premature conclusions while emphasizing timely acknowledgment.

Ongoing updates

Incident records are not static. Updates should reflect investigative findings, corrective actions, and resolution status. Where public disclosure thresholds apply, GAFAIG policies govern what information is published and when.

Evidence and traceability

Incident disclosures should be supported by internal records such as logs, timelines, investigation notes, and remediation actions. GAFAIG may review evidence as part of certification assessment or follow-up processes.

Interpretation posture

GAFAIG interprets this standard to prioritize clarity, proportionality, and procedural fairness. The objective is to improve governance outcomes, not to penalize good-faith reporting or evolving understanding.