How is an AI incident different from a normal software bug?

AI incidents often involve probabilistic behaviour — a harmful output, a biased decision, a hallucination, or an unsafe refusal — that may not reproduce deterministically. Logging the prompt, model version, and context is essential because you cannot always re-run the exact failure.

Who should own the incident log?

Typically a named responsible-AI or risk owner maintains it, with engineering and product feeding entries. For regulated or public-sector use, the log often feeds an audit trail and may be disclosable, so keep entries factual and dated.

Does this tool store my incident data?

No. It only generates a blank template structure in your browser. Nothing you type and nothing you log later is uploaded or stored by this tool.

AI Safety Incident Log Template

Q: What should an AI safety incident log capture?

At minimum it should record what happened, when, severity, who or how many were affected, the suspected root cause, the remediation taken, and a recurrence-prevention action. Consistent fields make patterns visible across many incidents.

AI safety incident log template

When an AI system produces a harmful, biased, or unsafe output, the difference between a one-off embarrassment and a systemic risk is whether you logged it consistently. This tool generates a structured incident log template — a set of columns and example rows — that you copy into your own tracker so every incident is captured the same way, making trends and repeat failures visible over time.

How it works

You select your organization type and list the AI systems you monitor. The tool assembles a Markdown table (and a matching CSV header) with the core columns of a mature incident log: a unique ID, date detected, the system and model version, incident type, severity, number of users affected, a short description, the suspected root cause, the remediation taken, and a recurrence-prevention action. You can toggle optional columns on or off so the template matches your existing governance process rather than forcing unused fields. The output is plain text you can paste straight into a spreadsheet, wiki, or ticketing system.

Tips and notes

Log near-misses, not just live failures. An unsafe output caught in staging is a free lesson — record it with the same rigour as a production incident.
Always capture model version and prompt context. AI failures are often non-deterministic; without the exact inputs you cannot reproduce or fix them.
Use a fixed severity scale. A consistent 1–4 or low/medium/high/critical scale lets you sort and prioritise across dozens of entries.
Close the loop with a prevention action. An incident without a “what stops this recurring” line is a story, not a control.