Mandatory AI incident reporting creates a disclosure and response category

The category is early, not empty, and each of the three ideas is at a different stage. The incident system of record looks the most open. Current tools fall into three separate groups. Governance platforms like Credo AI and RadarFirst handle documents, workflow and regulator filings but take no runtime data from agents. Agent security and egress-logging vendors capture tool calls and network traffic but do not map events to legal definitions. Operational incident tools like incident.io handle response workflow but not compliance. I found nothing that joins all three: capturing agent actions and egress, classifying events against the definitions in California SB 53 ("critical safety incident") and EU AI Act Article 73 ("serious incident"), drafting the notification within the deadline, and tracking the fix until it is closed. Sources disagree on the Article 73 deadlines, so check the regulation text before building anything around them. Article 73 is reported to take effect from August 2026, which gives a hard timing trigger. The risk is that governance platforms and observability vendors bolt this on, so a new entrant would need to win on deep runtime integration and the regulatory definitions before they do. Independent incident attestation is mostly unclaimed as a product. Academic proposals for frontier AI auditing exist, ISO 42001 certification bodies exist, and labs are reported to be bringing in embedded evaluators. Big audit firms are the natural owners, though, so a startup would need to supply the technical evidence tooling that auditors rely on rather than compete with them on the sign-off. Agent loss data for insurers is the most contested. AIUC, Armilla and Klaimee each build their own evidence from testing and incident databases, and the free OECD and AIID databases set a baseline. A neutral, cost-normalised, cross-carrier dataset still seems missing, and research such as the governance.ai insurance-stack paper says this needs industry-wide coordination. But the insurers have little reason to share data, and the best source of normalised data would probably be the system-of-record product itself. That makes the dataset a strong second product to build on top of the first idea, rather than a business to start from scratch.

• RadarFirst (AI Incident Management)

  This is the closest match I found to the incident system of record. RadarFirst sells privacy-incident software and has added an AI incident management module that covers triage, investigation workflows and documenting decisions for regulators. It is built for privacy and governance teams. It does not capture agent actions or egress at runtime, and it is not aimed at model labs.

• Credo AI

  An AI governance platform with an AI registry, control and mitigation tracking, evidence storage and reporting. Reviews say its monitoring evidence comes from outside observability and security tools. I found no dedicated module for classifying incidents or drafting regulatory notifications, but it is an obvious company to add one.

• incident.io

  An operational incident management tool that publishes material on EU AI Act incident handling. It could extend its existing workflow into regulatory AI incident filing, though today it is aimed at SRE incident response.

• AIUC (Artificial Intelligence Underwriting Company)

  Founded by a former Anthropic executive and backed by a $15M seed round. It insures AI agents and runs the AIUC-1 standard, which uses audit evidence and adversarial simulation in place of loss history. That puts it close to both the attestation idea and the insurer data idea.

• Armilla AI

  A Lloyd's coverholder selling AI liability policies, now with limits up to $25M, that cover agent failures and regulatory defence. Its underwriting draws on its own AI incident and litigation databases and on model testing data, so it is already building insurer-side incident data for its own use.

• Klaimee

  A YC-backed insurtech that raised $5.5M. It sells liability cover for autonomous agents and underwrites each one with a public-data scan, a governance questionnaire and adversarial probes. Like Armilla, it would be a buyer of agent loss data or a competitor in producing it.

• OECD AI Incidents and Hazards Monitor

  A free public database built from news reports, with thousands of AI incidents and hazards. It is not normalised for cost or specific to agents, but it sets the free baseline that a paid loss dataset has to beat.

• Cyera (AI incident analysis)

  This is a secondary report, not Cyera's own page. It says Cyera analysed 7,246 public AI incidents and verified 188 enterprise-relevant cases where autonomous systems caused harm in production. That suggests security vendors are already compiling agent incident datasets.

• ISO 42001 certification bodies (BSI, Schellman, DNV, KPMG)

  Accredited auditors already certify AI management systems. Of all the parties here, they are the most likely to extend into attesting that an incident was contained and fixed. I found no firm that sells this as a dedicated product yet.

Checked 2026-10-12