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After an AI-related loss, treat the event as a combined safety, security, privacy, operational and governance incident. Activate a named incident commander and the AI system owner, stop or isolate harmful processing when it is safe to do so, preserve model and system evidence, establish what happened and who may be affected, assess reporting duties, communicate verified facts, then remediate and validate the system before restoring service. Keep a non-AI fallback available throughout. This approach works for unsafe outputs, data exposure, prompt injection, model theft, discriminatory decisions, outages and downstream physical or financial harm.
What counts as an AI-related loss?
An AI incident is any event in which an AI system, its data, its suppliers or its outputs causes or materially contributes to harm, a legal violation, a security compromise or a loss of control. The same event can fit several categories, so classify all plausible impacts rather than forcing it into a single ticket type.
- Unsafe, biased or discriminatory output: recommendations or decisions that create safety, employment, housing, lending, healthcare or access-to-service harm.
- Hallucinated or fabricated content: invented citations, records, instructions or claims that are acted on as if verified.
- Prompt injection and abuse: instructions that override safeguards, exfiltrate connected data or cause an agent to take unauthorized actions.
- Unauthorized access or privacy exposure: disclosure of personal, confidential, regulated or customer data through prompts, logs, retrieval systems or model responses.
- Intellectual-property loss: leakage of proprietary code, training material, model weights, prompts or trade secrets.
- Cyberattack or model compromise: stolen credentials, poisoned data, parameter exfiltration, malicious fine-tuning or attacks on an AI supplier.
- Availability and operational failure: an outage, runaway automation, incorrect batch process or an integration failure that interrupts a critical service.
- Downstream physical, financial or rights impact: injury, monetary loss, denial of a right or a harmful action taken by a person or connected system.
Record whether the effect is reversible, whether it is still propagating and whether the AI system made the final decision or only supplied information to a human or another application. That distinction affects containment, notification and remediation.
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Open a time-stamped incident record, appoint one person to direct decisions and stabilize the situation without erasing evidence. If anyone faces immediate danger, safety takes priority over normal forensic procedure.
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- Declare the incident. Create a unique case number, record the detection time and appoint an incident commander with authority to pause processing. Name the AI or system owner as a technical decision-maker.
- Assemble the response group. Bring in security, privacy, legal or compliance, communications, records management, business continuity, relevant product owners and the model or cloud provider. Add safety, clinical, employment or other specialists when the use case requires them.
- Stop active harm. Use a controlled shutdown, human override, rate limit, connector disablement or non-AI fallback. NIST’s AI Risk Management Framework calls for mechanisms to supersede, disengage or deactivate systems that produce inconsistent outcomes.
- Protect the response channel. Move coordination to an access-controlled workspace, restrict who can change the affected system and preserve an approval trail for every emergency action.
- Set an initial operating picture. Write down what is known, what is suspected, what is unknown, who may be at risk and when the next status review will occur. Mark assumptions clearly so they are not mistaken for findings.
NIST describes its AI RMF as voluntary, rights-preserving, non-sector specific and use-case agnostic. Its Manage function requires plans to respond to, recover from and communicate about incidents, including procedures for previously unknown risks and authority to deactivate systems that produce inconsistent outcomes.
How do we contain an AI failure without destroying evidence?
Contain the accounts, connectors, data flows, endpoints or network paths that are causing harm, but coordinate changes with the forensic lead. The objective is to stop escalation while retaining enough information to determine cause, scope and responsibility.
Preserve volatile evidence first
Before routine cleanup, model replacement or log rotation, capture the information most likely to disappear. CISA guidance identifies system images, memory captures, logs, malware samples and volatile data such as memory and firewall buffers as important evidence. Use a documented chain of custody, synchronized timestamps and read-only copies where practical.
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| Evidence | What to capture | Why it matters |
|---|---|---|
| Model and configuration | Model name and version, weights or immutable identifier, system prompts, safety policies, fine-tuning or retrieval settings, tools and connectors | Shows which behavior and controls were actually deployed |
| Inputs and outputs | Prompts, retrieved documents, tool calls, model responses, human edits, confidence or refusal signals and timestamps | Allows reproduction of the harmful path and separates model behavior from user action |
| Identity and access | User and service accounts, tokens, roles, authentication events, API keys and privilege changes | Reveals unauthorized use and the accounts that must be contained |
| Infrastructure | Host and container images, memory, network captures, firewall buffers, application and database logs, deployment manifests | Supports investigation of compromise, lateral movement and data access |
| Business record | Tickets, customer complaints, screenshots, decisions, approvals, notifications and vendor correspondence | Establishes impact, chronology and what the organization represented to others |
Make containment changes deliberately
Disable a compromised connector, revoke a token, quarantine a data source or route requests to a fallback service when that stops harm. Note the exact time, operator, command or UI action and expected side effect. Australian incident-response guidance emphasizes minimizing damage while preventing an attacker from destroying evidence, and calls for collecting images, packet captures, IP addresses, logs, configurations, databases, notes and screenshots.
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Do not fine-tune, delete, rotate or otherwise alter the affected model or system in a way that could compromise causal evaluation before required authorities are informed. EU AI Act Article 73 specifically requires providers to avoid such alteration before informing authorities.
How do we determine the scope and cause?
Build a timeline and an affected-population map before deciding that the incident is closed. HHS guidance recommends analyzing scope, origin, whether the event is ongoing or has propagated and how the attack occurred before prioritizing later response actions.
Build the timeline
- When was the model, prompt, data source, integration or credential changed?
- When did the first harmful output or unauthorized access occur?
- When was it detected, reported, contained and disabled?
- Which versions, regions, tenants, channels and downstream systems were involved?
Map people, data and decisions
Identify data categories (such as health, financial, employee, child, biometric or proprietary data), the people or communities affected, the number of records or decisions and whether the harm reached a third party. Check cached responses, exported files, email, logs, vector indexes and backups; an AI response may have copied data into places outside the model itself.
Test competing causal hypotheses
Keep several explanations open until evidence separates them: a model defect, unsafe system prompt, poisoned retrieval source, compromised identity, vulnerable integration, operator error or a provider-side change. Reproduce the behavior in an isolated environment using preserved versions and synthetic data. Record confidence and disconfirming evidence for each hypothesis rather than declaring a root cause from one example.
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Do we have to report an AI incident?
There is no universal legal category called an “AI incident.” Make a written decision for every potentially applicable AI regulation, data-protection and breach-notification rule, sector requirement, employment or safety law, contract, insurer condition and law-enforcement request. The responsible party may be a provider, deployer or both.
| Regime or duty | When it may apply | Timing or action |
|---|---|---|
| EU high-risk AI systems | A serious incident with a causal link, or a reasonable likelihood of one, involving a high-risk system | EU AI Act Article 73 generally requires reporting no later than 15 days after awareness. Widespread infringements or specified serious incidents require immediate reporting and no later than two days; a death requires immediate reporting and no later than 10 days. Providers must investigate, risk-assess, take corrective action and cooperate with authorities. |
| EU general-purpose AI models with systemic risk | A serious incident involving the model, including a serious cybersecurity breach | Providers must track, document and report serious incidents and possible corrective measures without undue delay. The European Commission identifies model-parameter exfiltration and cyberattacks as examples of serious breaches. |
| NIS2 | A significant incident affecting an essential or important entity or its network and information systems | ENISA implementation guidance says to report without undue delay to the relevant CSIRT or competent authority. Users may also need notification when service delivery is likely to be adversely affected. |
| HIPAA and U.S. healthcare | Ransomware or another security incident involving protected health information | Assess breach-notification obligations, document the analysis and include lessons learned. HHS recommends examining notification duties as part of the initial response. |
| Contracts, insurance and sector rules | Customer data, regulated services, critical operations or a policy with notice conditions | Check definitions, notice addresses, service-level commitments, cooperation clauses and deadlines. Notify an insurer or response-retainer provider early enough to preserve coverage and access assistance. |
Keep the legal analysis, assumptions, approvals and submission receipts in the incident record. If facts change, update the decision and explain why; do not silently replace an earlier assessment.
How should we notify affected people and customers?
Communications should be accurate, useful and proportionate to the risk. NIST calls for communication, feedback and recourse mechanisms for impacted individuals and communities.
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- Separate unknowns: state what remains under investigation and when the next update will appear.
- Explain protective action: describe shutdowns, credential resets, monitoring, corrected decisions or alternative service paths without exposing attack details that would increase risk.
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Use one approved fact sheet for customers, employees, regulators and support teams, then adapt the level of detail to each audience. Maintain a decision log showing who approved each statement and when it was sent.
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How do we remediate and safely bring the AI system back online?
Recovery is not a simple restart. Remove the enabling weakness, prove that the corrected system meets safety, privacy, security and accuracy criteria, and define who can authorize service restoration.
- Eradicate the cause. Revoke or rotate compromised credentials, remove malicious instructions or data, patch vulnerable integrations, correct access policies and replace tampered artifacts.
- Restore known-clean state. Recover data, prompts, indexes and deployment artifacts from verified backups. Record integrity checks and the point in time used for restoration.
- Run acceptance tests. Test the triggering scenario, abuse and prompt-injection cases, privacy leakage, authorization boundaries, unsafe outputs, refusal behavior, bias-relevant cases and failure handling. Include representative real-world inputs, not only a small demo set.
- Use staged release. Start with a limited audience, reduced permissions, rate limits and human approval for consequential actions. Compare outputs with the pre-incident baseline and monitor for recurrence.
- Authorize recovery. Require sign-off from the system owner, security, privacy or compliance and the relevant business or safety authority. The approval should list residual risks, compensating controls and rollback conditions.
- Monitor after restoration. Watch error rates, refusals, data-access events, user complaints, drift, abnormal tool calls and safety indicators. Keep the non-AI fallback available until monitoring demonstrates stable behavior.
The UK National Cyber Security Centre describes the response cycle as analyse, contain or mitigate, remediate or eradicate, and recover. It recommends confirming remediation before recovery and monitoring systems after restoration. If the system cannot meet acceptance criteria, keep it disabled, replace it or narrow its use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should we choose between response options?
When leaders must choose between a shutdown, partial containment, a fallback service or continued operation under controls, score each option against the same dimensions. A fast option is not automatically the safest if it destroys evidence or leaves people exposed.
| Decision axis | Questions to ask | What a strong option demonstrates |
|---|---|---|
| Harm severity and reversibility | Could this affect safety, rights, finances or a large population? Can the effect be undone? | Stops irreversible or high-severity harm first |
| Containment speed versus evidence preservation | How quickly must processing stop, and will the proposed change overwrite logs, memory or model state? | Uses the least destructive action that still controls danger |
| Regulatory and contractual exposure | What reporting clocks, sector rules, customer commitments and cross-border obligations apply? | Preserves time to notify and meet cooperation duties |
| Technical complexity | Which model, data, identity, cloud, vendor and supply-chain dependencies must change? | Has an executable change plan and rollback path |
| Recovery confidence | Are backups clean? Are tests, monitoring and human review available? | Can prove the restored service is within agreed limits |
| Transparency and recourse | Can the organization explain the decision, notify people and offer correction or appeal? | Provides understandable reasons and a staffed remedy |
What should the incident record contain?
Use one controlled record rather than scattered chat messages. At minimum, capture:
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- Case number, severity, incident commander, system owner and all decision-makers.
- Detection source, first-known occurrence, current status and every containment or restoration time.
- Systems, model and data versions; regions, tenants, integrations and vendors involved.
- Prompts, outputs, tool calls, identities, logs, images, captures and chain-of-custody details.
- Affected data categories, people, decisions, services and estimated scope, with confidence levels.
- Causal hypotheses, tests performed, findings, unresolved questions and legal or regulatory analysis.
- Notifications, approvals, regulator or customer correspondence and commitments for the next update.
- Recovery tests, acceptance results, residual risks, rollback conditions and final authorization.
- Corrective actions with an owner, due date, priority, verification method and completion evidence.
How do we learn from the incident and prevent a repeat?
Hold a short hot debrief after stabilization, followed by a formal review when the incident report is complete. The review should examine both the technical cause and the effectiveness of the response.
- Which governance, supply-chain, data, identity or monitoring control failed or was missing?
- Was evidence collected before it was overwritten, and could investigators reproduce the harmful behavior?
- Did escalation reach the right safety, privacy, legal and business owners quickly enough?
- Were affected people told what they needed to know and given a workable correction or appeal route?
- Which corrective actions have named owners, due dates, funding and a test that proves completion?
NIST requires documented response, recovery, communication and continual-improvement processes. Close the incident only when corrective actions are tracked in normal governance and the organization has tested the fallback, notification contacts and evidence-preservation procedures in a future exercise.
Frequently Asked Questions
Should we shut down the entire AI service or only one feature?
Use the narrowest control that reliably stops harm. Isolate the affected model, tenant, connector or workflow when the blast radius is understood; choose a full shutdown when scope is uncertain, effects are propagating or a partial control cannot be validated.
Who should own an incident involving a third-party model?
Your organization still needs an internal incident commander and system owner, even when the provider investigates its platform. Contract contacts, preservation requests, audit rights and provider timelines should be recorded alongside your own evidence and decisions.
Can we use incident data to retrain the model?
Not until privacy, legal and forensic reviews approve the use. Preserve an immutable copy first, remove or protect sensitive information, and document whether retraining could contaminate evidence or reproduce the harmful behavior.
What if we cannot identify everyone affected?
Document the uncertainty and the method used to estimate scope. Use broad protective measures, provide a way for people to check whether they were affected, and update notices when new evidence narrows or expands the population.
How often should an AI incident plan be exercised?
Exercise it after major model, data, vendor or workflow changes and on a regular schedule set by your risk program. Include a timed evidence-capture drill, a notification decision, a fallback activation and a recovery authorization so the plan is tested under realistic pressure.
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