Topic

Near-Miss Disclosure

Near-miss disclosure refers to the reporting and documentation of potential AI failures or governance breaches that were identified and corrected before causing actual harm or compliance violations. This practice is critical for enterprise AI governance because it creates visibility into systemic risks, process weaknesses, and emerging issues that might otherwise remain hidden until they result in serious incidents. Organizations that implement near-miss disclosure programs can proactively strengthen their AI controls, improve model safety practices, and demonstrate to regulators and stakeholders that they maintain robust internal monitoring and continuous improvement mechanisms.

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StandardsGlobal2026-06-30

Academic Framework Proposes 7-Day Public Reporting Window for Tier 3 Agentic AI Incidents, Raising the Bar for Enterprise Anomaly Detection

A paper published on SSRN titled 'Transparent Real-Time Governance of Agentic AI Systems' proposes a tiered incident governance framework that would require AI Offices and National Authorities to publish public summaries of significant agentic AI events, including near-misses and blocked misuse attempts, within seven days of a Tier 3 classification. The framework targets agentic AI systems operating with meaningful autonomy and sets specific detection and reporting expectations for enterprise operators. Compliance teams deploying agentic AI should treat this as an early signal of the reporting granularity regulators may soon demand.

agentic AI incident reporting real-time governance anomaly detection near-miss disclosure