Stop the harmful capability first, protect people and data, preserve the evidence, and put a named incident lead in charge. Then classify the event across customer harm, privacy, safety, security and fairness; choose a tested fallback; meet any notification duties; and restore service only after the fix has been reproduced, tested and assigned an owner.
The first hour: declare the incident and stabilize the system
An AI incident is any event in which an AI-enabled product, decision, recommendation, agent or connected tool creates actual or credible risk to customers, confidential information, physical safety, security, rights or fair treatment. Treat a harmful answer, an unauthorized disclosure and an unsafe autonomous action as operational incidents, not merely model-quality bugs.
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1. Name one incident lead
Appoint a person with authority to pause features, allocate staff and make time-critical decisions. Bring in the functions that match the event: security, privacy and legal, product and engineering, customer support, communications, safety or clinical specialists, and the owners of any model, cloud, data or tool provider involved. Record who is incident lead, who can approve containment, who owns customer contact and who can authorize restoration.
2. Protect people before availability
Use the least risky action that stops further harm. Depending on the failure, pause the feature, rate-limit traffic, roll back a release, remove a tool permission, restrict the data source, or place every response behind human review. Stop autonomous or hazardous actions immediately. If a customer could act on dangerous advice, provide a visible human escalation route and a safe alternative rather than leaving a broken chatbot silently unavailable.
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Do not keep the system online simply to gather more examples. If continued operation is necessary to support people, narrow the scope, disable high-risk workflows and monitor every transaction. Write down the exact time and reason for each control change.
Preserve evidence before changing the system
An investigation is only reproducible if the organization can show what the system received, which versions and policies were active, what it returned, what tools it called and what people did next. Preserve evidence before “cleaning up” prompts, deleting logs or deploying a fix that overwrites the failure.
Evidence to freeze
- Full prompts, responses, refusal messages, system and developer instructions, and relevant conversation context.
- Model name and version, routing decisions, safety-policy version, classifier or guardrail version, embeddings and retrieval configuration.
- Retrieved documents, database records, tool calls, parameters, tool responses and actions taken in external systems.
- Configuration, feature flags, code and deployment versions, access-control settings, secrets or credentials that may have been exposed, and infrastructure changes.
- Timestamps with a stated time zone, user or case identifiers, affected geographies, support tickets, complaints, screenshots and reports from staff or customers.
- Authentication and access logs showing who viewed, exported, changed or shared relevant information.
- Human decisions made during triage, communications sent, approvals, overrides, rollbacks and restoration checks.
Maintain a defensible chain of custody
Copy volatile logs to protected, access-controlled storage; record who collected each item, when, from which system, and whether it was transformed or redacted. Preserve the original and work from a documented copy. Limit access to sensitive prompts and personal data, and involve privacy or legal counsel when retention, privilege, employment records or law-enforcement requests may matter. Never include live secrets in an incident channel or paste personal data into an unapproved analysis tool.
Classify severity with a multi-dimensional triage
Do not reduce severity to the number of bad answers. A single disclosure of highly sensitive data or one unsafe physical action can outweigh thousands of low-impact errors. Assess both what happened and what could plausibly happen if the capability remains available.
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| Dimension | Questions to answer | Evidence to record |
|---|---|---|
| Customer harm | Was a person denied service, charged, misled, distressed or given an erroneous decision? Is correction or compensation needed? | Affected cases, decisions, losses, complaints and vulnerability factors |
| Privacy and confidentiality | What data categories were exposed, to whom, through which systems, and for how long? | Subjects, recipients, jurisdictions, time window, access logs and downstream copies |
| Safety | Could advice, recommendation or automation cause physical injury, health harm or an unsafe operating condition? | Hazards, telemetry, safeguards that failed and qualified safety assessment |
| Security | Was there prompt injection, unauthorized access, credential exposure, data exfiltration or manipulation of a connected tool? | Indicators of compromise, accounts, permissions, commands and network evidence |
| Fairness and rights | Did an error or policy affect a protected group, accessibility need, appeal right or other legal interest disproportionately? | Outcome distribution, affected populations, explanations and appeal records |
| Scope and duration | How many people, products, markets and languages were affected, and for how long? | Start and end estimates, versions, regions, traffic and dependency map |
| Reversibility | Can records, decisions or physical conditions be corrected, or is harm permanent? | Correction time, recovery options and residual impact |
| Third-party dependence | Did a model, cloud, data, identity or tool provider contribute to the event? | Provider notifications, contracts, service logs and shared responsibilities |
Use the assessment to set an incident tier and response cadence. A high-severity event normally means continuous coordination, executive visibility, legal review and a restoration gate; lower-severity events still require a record, trend analysis and corrective action. NIST’s trustworthiness characteristics provide a useful lens: validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and fairness with harmful bias managed.
Contain the failure and choose a safe fallback
Containment should remove the dangerous path, not merely hide its symptoms. Select controls that match the failure mode and test that they actually prevent recurrence.
When the model gives harmful or unreliable advice
- Disable the affected intent or route it to a deterministic, reviewed answer.
- Require human approval for advice involving health, finance, legal rights, account changes or other high-consequence decisions.
- Block known unsafe prompts and outputs while keeping an appeal or escalation path for legitimate users.
- Review conversations for systematic impact rather than correcting only the customer who complained.
When data is exposed
- Restrict access to the index, prompt store, transcript repository or tool that revealed the data.
- Stop downstream sharing and revoke links or exports; rotate exposed credentials, tokens and secrets.
- Remove or quarantine compromised retrieval sources and invalidate cached responses where possible.
- Preserve evidence before deletion, then follow an approved retention and eradication plan.
When an agent or tool takes unsafe action
- Revoke the action permission or disconnect the tool; stop queued jobs and autonomous loops.
- Move to read-only or simulation mode and require two-person or qualified-human approval for consequential actions.
- Inspect external systems for changes, cancel or reverse them where safe, and preserve safety telemetry.
- Use a conservative manual procedure until a hazard review validates the replacement.
Coordinate containment with the model, cloud, data and tool providers when their systems are involved. Record the request, provider response, timing and any limitation on the evidence you can obtain.
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Customer-impact incident
Give affected people a human route that does not depend on the failing AI. Identify the cases in which the system produced an erroneous answer, recommendation or decision, and have a qualified person review them. Correct records and decisions, refund or otherwise remediate where appropriate, and monitor complaint volume after the correction. Preserve enough context to explain what the system did, what rule or data was involved and what a human decided afterward. Do not imply that a customer caused the failure by phrasing a prompt in an unexpected way.
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Privacy or confidentiality incident
Map the data flow: categories of information, people concerned, recipients, systems, time window, countries and any onward transfers. Restrict access and downstream sharing, rotate exposed credentials or secrets, and determine whether copies remain in logs, caches, training pipelines, tickets or provider systems. Obtain privacy and legal review before deciding that an event is reportable or not reportable.
Notification thresholds and deadlines depend on the applicable privacy, sectoral, contractual and security rules; there is no single global deadline. Document the analysis even when no notice is required. NIST SP 800-63-4 says organizations using AI or machine learning in identity systems “SHALL perform and document privacy risk assessments for personal information and data processed by such systems.”
Safety incident
Protect people immediately, stop autonomous or hazardous actions and move to a conservative fallback. Involve qualified safety personnel who can assess the hazard rather than relying only on the model team. Preserve safety-relevant telemetry, operator actions and environmental conditions. Notify responsible authorities, customers, partners or site operators when required. Before service returns, complete a documented hazard review and validation against the original failure and foreseeable variants.
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Separate verified facts from hypotheses. A first notice should state what service or decision was involved, the period currently known, the kinds of information or actions involved, what has been stopped, what the person should do now and how to obtain help. Say what remains unknown and when the next update will arrive. Do not speculate about root cause, blame a provider or promise that no further impact exists before the evidence supports those statements.
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Internal communications
Use one incident record and a controlled distribution list. Give staff a short script, an escalation address and instructions not to copy sensitive customer data into informal channels. Communications, support and sales teams should use the same confirmed timeline. Keep decision authority explicit: who can approve customer notice, regulator contact, compensation, public statements and restoration.
External notices and regulators
Check privacy, breach, product-safety, cybersecurity, consumer-protection, employment and contractual duties for every affected jurisdiction and sector. Consider notice to customers, regulators, partners, insurers, law enforcement or a safety authority separately; one notice does not automatically satisfy another obligation. Meet the shortest applicable deadline, but avoid sending an inaccurate account merely to appear definitive. Counsel should document the legal basis, trigger date, recipients, content and delivery evidence.
The Federal Trade Commission’s Safeguards Rule guidance says an incident plan should cover the plan’s goals, internal processes, roles and decision authority, communications, remediation, documentation and reporting, and a post-mortem with plan revision.
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Investigate without making the failure worse
Reconstruct the timeline
- Set a common clock and identify the earliest reliable signal, such as a customer report, alert or access-log event.
- Map deployments, configuration changes, model and policy versions, retrieval updates, provider changes and human interventions before and during the window.
- Reproduce the behavior in an isolated environment using sanitized or synthetic data. Do not probe production with real personal information or dangerous commands.
- Compare expected controls with actual behavior: input filtering, retrieval permissions, output checks, tool authorization, human review, monitoring and rollback.
- Test whether the issue is deterministic, probabilistic, prompt-specific, language-specific, tenant-specific or dependent on a third-party service.
Find contributing causes, not a convenient culprit
A model error may coexist with excessive permissions, incomplete evaluations, stale retrieval data, an unsafe prompt template, weak identity controls, a missing human override or a process that ignored alerts. Use a structured causal analysis and distinguish direct trigger, enabling conditions and missed detection. Record uncertainty and competing explanations until tests resolve them. Do not train on, delete or publicly publish sensitive incident material without a privacy and security review.
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Remediate, test and restore in stages
Remediation can involve data correction, prompt or policy changes, model routing, access controls, evaluation sets, tool restrictions, staff procedures or vendor terms. Fix every control that allowed the event, not only the visible output.
Use an incident reproduction as a release test
- Capture the original triggering sequence in a protected test case.
- Add foreseeable variants: paraphrases, languages, edge cases, adversarial prompts, missing context and unusual tool responses.
- Test refusal, escalation, privacy filtering, authorization and logging behavior, not just answer quality.
- Check for regressions in legitimate use and for disparate effects on affected groups.
- Have an independent reviewer sign off on high-consequence fixes and safety controls.
Restore gradually
- Define measurable release gates, monitoring signals, acceptable error thresholds and a named rollback owner.
- Start with a small, low-risk cohort or read-only mode; keep human review in place.
- Watch real-time alerts, support contacts, privacy signals, safety telemetry and provider status.
- Expand only when evidence meets the gates. Roll back immediately when a threshold or known failure pattern returns.
- Close the incident only after residual risk, customer remediation, required notices and open corrective actions have owners and due dates.
Turn the post-incident review into a control change
Write a factual timeline covering impact, decisions, communications, evidence, root and contributing causes, corrective actions, owners and due dates. Include what worked, what delayed detection or response, and which assumptions proved false. Share an appropriate version with affected communities and oversight bodies without exposing personal information or security-sensitive details.
Update monitoring, appeal and override paths, recovery procedures, decommissioning criteria, change management and provider coordination. Add an explicit trigger for reassessing the system when its data, model, tools, users, geography or risk profile changes. Re-test the incident scenario after controls are changed and schedule a review date rather than treating the report as the end of the work.
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Governance frameworks that support response
NIST describes the AI Risk Management Framework as a voluntary, rights-preserving and use-case-agnostic resource for organizations designing, developing, deploying or using AI. Its Playbook organizes implementation into Govern, Map, Measure and Manage. These functions help an organization know who owns a system, map affected people and harms, measure controls and execute risk responses; they do not replace incident-specific legal, safety or security duties.
ISO/IEC 42001:2023 specifies requirements and guidance for establishing, implementing, maintaining and continually improving an AI management system. It can provide documented roles, risk treatment, monitoring and continual improvement, but certification or a management-system process does not by itself satisfy a breach notice, product-safety investigation or customer remedy.
Choosing outside help during or after an incident
| Need | What to compare | Questions before engaging |
|---|---|---|
| AI management-system formalization | Scope, evidence and auditability, continual-improvement process, implementation effort and independent assurance | Which systems and jurisdictions are covered? What deliverables and assurance are actually provided? |
| Live breach or security response | Forensic capability, logging and evidence handling, privacy expertise, response speed and geographic coverage | Can the team preserve chain of custody, coordinate providers and support regulator or customer notices? |
| Safety investigation | Hazard-analysis methods, domain qualifications, telemetry review, validation and authority to stop operations | Who signs the return-to-service decision, and how are foreseeable variants tested? |
| Model or tool provider support | Access to logs and versions, escalation service levels, contractual duties, data handling and recovery testing | Can the provider isolate your tenant, preserve evidence and document changes made on its side? |
Verify competence, legal coverage, geography, confidentiality terms, conflicts of interest and any referral or commercial terms. An ISO/IEC 42001 service may help formalize governance, while an incident-response specialist may be better suited to a live breach or safety event; they are not interchangeable.
Quick Recap
A practical incident checklist
- Named incident lead and decision authority.
- Affected capability paused, constrained or placed behind human review.
- People protected and a safe customer escalation route available.
- Prompts, outputs, versions, policies, retrieval, tool calls, logs and access records preserved.
- Severity assessed across harm, privacy, safety, security, fairness, scope, duration, reversibility and third parties.
- Fallback selected, tested and documented.
- Legal, contractual, sectoral and geographic notification duties assessed.
- Customers and staff given verified facts, practical steps and a help route.
- Fix tested against the reproduction and foreseeable variants.
- Staged restoration, monitoring thresholds, rollback criteria and an owner in place.
- Post-incident actions recorded with owners, due dates and a scheduled effectiveness review.
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