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Sprint projectSep 12, 2026Japan

Cross Organization AI Incident Record

Veniwol · Team Lilambd

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Cross Organization AI Incident Record

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A minimum evidence and handoff protocol for autonomous-agent incidents that cross organisational boundaries. The report defines four clocks, claim-level evidence states, disclosure tiers and a compact handoff record so an affected third party and an originating lab can preserve uncertainty while coordinating containment, attribution and reporting. It is deliberately defensive: no exploit code, live indicators, credentials or reproduction detail are included.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. I think the most valuable takeaway is the proposal to conduct a tabletop. The most perfectly defined schema to share data is useless if orgs/teams don't use it, and a tabletop is a great way to test that.

  2. This paper tackles a real problem. When an AI agent causes an incident, different organizations each see only part of the picture, at different times, with different evidence. The authors propose a simple shared record that lets each party pass along what it knows without forcing everyone into one story too early.

    The design is useful. It tracks four "clocks" (when the event happened, when it was noticed, when an organization learned of it, and when it was disclosed), labels each claim by how well it's supported, and includes clear fields for authorization and tiered disclosure. The authors also don't oversell it as a replacement for forensics, legal processes, or regulatory reporting. The main weakness is that the idea hasn't been tested. The record was designed from public accounts of one incident, then checked against that same incident. That shows it can hold the information, but not that it makes real incident response any better. There's no comparison with other record formats, no live or simulated exercise with multiple organizations, and no measurement of whether it speeds up notification, reduces unsupported assumptions, or gets the right person to act sooner. The authors list these as future work, and a multi-party tabletop exercise would be the most valuable next step. Overall, this is a clear, practical coordination tool, but closer to an operating procedure than a proven new AI safety mechanism. A realistic multi party exercise, a machine readable version, and measured coordination outcomes would make it much stronger.

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Cite this project

@misc{veniwol2026cross,
  title = {{Cross Organization AI Incident Record}},
  author = {Veniwol},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/cross-organization-ai-incident-record-09md}},
  url = {https://apartresearch.com/sprints/projects/cross-organization-ai-incident-record-09md}
}

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