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An AI incident response plan is a written, tested way to detect harmful model behavior, decide who can act, contain the problem, protect affected people, restore service safely, and learn from the failure. Build it as an extension of your product incident process, but give it an AI-specific harm taxonomy, context-based severity, output and drift monitoring, reversible shutdown controls, and communication duties. The practical sequence is: inventory the AI system, assign decision rights, define incident classes and severity, instrument detection, write first-hour playbooks, rehearse containment and recovery, and keep the plan current after every change or exercise.
What should an AI incident response plan cover?
The plan should cover the complete AI-enabled product rather than only the model endpoint. Record every model, prompt or policy layer, training and retrieval data source, tool or agent action, vendor, user group, geography, deployment environment, and dependency. Include pre-production gates if a failure there could reach customers or contaminate production data.
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Define an AI incident broadly. It can involve unsafe or discriminatory output, privacy leakage, prompt injection, model or data compromise, unacceptable accuracy or drift, harmful automation, abuse at scale, a third-party outage, or a failure of human oversight. An incident is not limited to a security breach or downtime: a fluent answer that causes physical, financial, legal, or social harm belongs in the same response system.
Document intended use and foreseeable misuse
For each feature, state what the system is allowed to do, what it must never do, who is affected, and which human decisions remain mandatory. List foreseeable misuse such as automated bulk prompting, attempts to extract confidential context, adversarial documents in retrieval, or users treating a draft as professional advice. Map regulatory, contractual, safety, accessibility, and data-residency obligations to the feature and the team responsible for meeting them.
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Set boundaries with neighboring processes
Explain when an AI event is also a security incident, privacy breach, safety event, availability outage, or customer-support escalation. One incident record should link the parallel tickets and preserve a single timeline; separate processes must not create conflicting instructions about containment or notification.
Who owns an AI incident and who can make decisions?
Name an incident commander and at least one backup for every operating period. The commander coordinates facts, priorities, and the decision log; they do not need to be the person who fixes the model. Publish a current contact list, on-call route, time zone coverage, and a backup for every critical role.
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| Role | Primary responsibilities | Decision rights to document |
|---|---|---|
| Incident commander | Open the incident, set severity, assign work, run updates, and close or hand off the response. | Declare and escalate; convene the response; approve movement between phases. |
| Product owner | Explain user impact, intended behavior, launch commitments, and acceptable service trade-offs. | Prioritize feature restrictions and approve product-level workarounds. |
| Engineering and ML | Analyze model, prompt, retrieval, tool, data, and deployment changes; implement rollback or fixes. | Change prompts or policies, roll back a model, disable a tool, and restore a tested version. |
| Security | Investigate account abuse, prompt injection, exfiltration, compromised infrastructure, and evidence preservation. | Block traffic, revoke access, isolate systems, and set security escalation. |
| Privacy and legal | Assess personal data, intellectual property, contractual duties, and notification requirements. | Approve disclosures, regulator or customer notices, and legally required preservation. |
| Trust and safety | Assess harmful content, vulnerable groups, abuse patterns, and human-review capacity. | Set content controls, review queues, and user-protection measures. |
| Communications and support | Prepare status messages, support guidance, and direct outreach to affected users. | Publish approved messages and set the next update time. |
| Executive sponsor | Resolve cross-team trade-offs, risk acceptance, and prolonged business impact. | Approve extended shutdown, exceptional risk acceptance, or major customer commitments. |
Write the authority for each consequential action in plain language: who may pause traffic, disable a model or feature, alter prompts or policies, contact customers, notify regulators, approve restoration, and close the incident. NIST SP 800-53 incident-response control IR-08 calls for designated responsibility, definitions of reportable incidents, metrics, information sharing, approval, and updates after changes or testing; your plan should make those requirements operational. See the FedRAMP and NIST incident-response controls.
How do you classify AI incidents and set severity?
Use an AI-specific taxonomy plus context-based severity. Traditional confidentiality, integrity, and availability labels miss probabilistic output harms, cumulative discrimination, unsafe automation, and impacts that differ sharply by population or use case. Microsoft describes these taxonomy gaps and the need to account for context and affected populations in its AI systems incident-response guidance.
| Incident class | Examples | Questions for triage |
|---|---|---|
| Safety and harmful content | Self-harm encouragement, dangerous instructions, harassment, extremist or sexual content reaching an unintended audience. | Could the output cause immediate injury or coercion? Are minors or other vulnerable people exposed? |
| Bias and discrimination | Unequal eligibility, ranking, moderation, or quality across protected or marginalized groups. | Who receives worse outcomes, how large is the disparity, and is a high-impact decision automated? |
| Privacy and confidentiality | Memorized personal data, cross-tenant leakage, confidential prompt exposure, or retrieval of restricted records. | What data was exposed, to whom, for how long, and can access or deletion stop further disclosure? |
| Security and abuse | Prompt injection, tool abuse, account takeover, malicious automation, or data exfiltration. | Is an attacker still active, and can the system perform external actions without approval? |
| Reliability and availability | High error rates, unsafe latency fallbacks, outages, loops, or a tool failure that leaves users without a safe path. | Is the service unavailable or merely degraded, and does fallback behavior increase harm? |
| Data or model integrity | Poisoned training or retrieval data, unauthorized model or prompt changes, or a corrupted evaluation set. | Which versions are trustworthy, and can you reproduce the change from immutable records? |
| Legal and intellectual property | Infringing output, prohibited advice, contractually restricted processing, or failure to honor a rights request. | Which jurisdiction, contract, license, or right is implicated, and what preservation duty applies? |
| Third-party failure | Provider outage, changed safety behavior, revoked model, compromised integration, or an unannounced policy change. | What control remains available if the supplier is unavailable, and is a contractual notice required? |
Grade severity using the affected population, vulnerability, duration, scale, reversibility, likelihood that harm will continue, and deployment context. A single erroneous answer in an internal experiment is not equivalent to the same answer driving a medical, employment, financial, or public-facing decision. Record both the class and the rationale so a later reviewer can understand why a level was chosen.
Example severity bands
- Critical: credible immediate or ongoing risk of serious physical, privacy, security, or systemic harm; broad exposure; compromised controls; or an AI action affecting high-stakes decisions without human review. Pause the capability while the commander and required specialists respond.
- High: material harm or a significant rights, safety, or security failure affecting multiple users, a vulnerable group, or a sensitive dataset, with a realistic chance of recurrence. Escalate on the on-call clock and apply a containment control promptly.
- Moderate: limited users or duration, measurable quality or fairness degradation, or a contained policy violation with no evidence of continuing severe harm. Correct, monitor, and set a specific review deadline.
- Low: an isolated, low-impact defect or near miss caught before material exposure. Record it, fix the contributing control, and include it in trend analysis.
Do not hard-code these labels as universal legal thresholds. Set numerical triggers, notification clocks, and required approvers for your geography, contracts, and risk class with privacy and legal counsel.
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What telemetry detects an AI incident early?
Monitor the model and the surrounding product. A low error rate can hide a harmful subgroup or a slow shift in confidence, while a sudden report spike can reveal a failure before offline metrics move.
Output and quality signals
- Safety and policy-classifier scores, classifier disagreement, refusal rates, and samples of outputs for human review.
- Accuracy, groundedness, citation or retrieval quality, task success, latency, and fallback rates compared with pre-deployment baselines.
- Confidence distributions and calibration by model version, user segment, language, geography, and high-impact workflow.
- Drift in input data, retrieved documents, prompt templates, tool calls, and output length or style.
Abuse, privacy, and security signals
- Unusual prompt volume, repeated jailbreak patterns, account or IP concentration, automation signatures, and anomalous tool-use sequences.
- Access violations, cross-tenant references, secrets in prompts or outputs, unexpected data egress, and changes to model, policy, or retrieval artifacts.
- User reports, support contacts, appeals, protected-class disparity indicators, and sudden changes in report categories.
Microsoft recommends telemetry for output anomalies, confidence shifts, and report-volume spikes. The UK AI Risk Management Toolkit advises alerts when production performance differs from pre-deployment tests and human review of anomalous outputs; its guidance is available at gov.uk.
Make evidence usable and privacy-aware
Capture an immutable event timeline with alert time, reporter, user and tenant identifiers, model and prompt versions, retrieved sources, tool calls, policy decisions, output samples, reviewer actions, and every mitigation. Minimize or redact personal data in logs, restrict access, set retention by incident and legal need, and preserve a separate evidence copy when deletion or regulatory holds could apply. Ensure timestamps are synchronized and that a responder can reconstruct the exact request path without exposing more customer content than necessary.
What should the first-hour triage playbook say?
Write an executable playbook for each severity level. It should state entry criteria, the first actions, required roles, evidence to collect, escalation deadlines, and the person who may authorize each containment step.
- Acknowledge and assign. The on-call responder records the report, starts the incident channel and timeline, names the commander, and sets an initial severity with its rationale.
- Protect people and stop expansion. If harm is active, pause the affected workflow, block abusive traffic, or require human approval before any external action. Do not wait for a perfect root-cause hypothesis.
- Preserve evidence. Snapshot relevant logs, model and prompt hashes, policy configuration, retrieved documents, tool permissions, user reports, and deployment state under the approved retention rules.
- Establish scope. Identify affected versions, tenants, geographies, populations, time window, and whether the issue reproduces. Check adjacent features that share the model, data, vendor, or policy layer.
- Page specialists. Bring in ML, product, security, privacy/legal, trust and safety, support, communications, and an executive sponsor according to the severity matrix.
- Set the escalation clock. Record the next decision time, next user-facing update, and conditions that automatically raise severity or trigger a shutdown.
Keep a short decision log: observed fact, uncertainty, option considered, approver, action, expected effect, and rollback condition. This prevents responders from repeating unsafe experiments and gives later investigators a defensible record.
How should containment and shutdown controls work?
Prepare reversible controls before launch, with an owner and tested command or UI path for each one. The safest response is often a graduated reduction in capability rather than an irreversible deletion.
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| Control | Use when | Entry and exit criteria to define |
|---|---|---|
| Rate-limit or block traffic | Abuse, scraping, automated attacks, or a report spike is amplifying harm. | Enter when volume or abuse threshold is met; exit after attack indicators and queue size return to baseline. |
| Tighten filters or policies | A narrow harmful-output pattern is known and a broader feature can remain useful. | Enter with a reviewed rule; exit after adversarial and regression tests show acceptable false positives. |
| Route to a safer model or static response | The primary model is drifting, unavailable, or producing unsafe content. | Enter when quality or safety guardrails fail; exit after the original version passes the restoration checklist. |
| Require human review | Outputs affect high-impact decisions or confidence and safety signals are uncertain. | Enter before release to a user or external system; exit when queue capacity and measured risk meet the approved threshold. |
| Disable a tool or capability | External actions, retrieval, code execution, or a data connector is implicated. | Enter on evidence of misuse or integrity loss; exit only after permissions, data, and tests are verified. |
| Roll back model, prompt, policy, or data | A recent change correlates with the incident and a known-good version exists. | Enter after artifact integrity is checked; exit after replay, safety, security, and performance tests pass. |
| Bypass or deactivate the AI feature | Harm is severe, continuing, or not bounded by narrower controls. | Enter on critical criteria defined by the commander; exit after independent approval and a documented restoration test. |
The UK toolkit recommends bypass or deactivation procedures, redundant or backup systems, and regular review of incident thresholds. Build a non-AI path that preserves essential service, tell support what users will see, and test that the kill switch works under load. Every control needs an exit condition; leaving an emergency restriction in place indefinitely can create a different safety or accessibility failure.
How do you investigate, recover, and verify a fix?
Separate containment from root-cause analysis. First make the situation safe; then determine the technical and organizational contributors.
Reproduce and bound the failure
Use preserved prompts, data snapshots, model and policy versions, and tool traces to reproduce the behavior in an isolated environment. Test neighboring languages, user groups, tenants, and workflows rather than only the original example. Distinguish a model limitation from a prompt, retrieval, data, policy, integration, access-control, or human-review failure.
Assess impact
Count affected requests and users where possible, identify sensitive data or decisions involved, and estimate duration and continuing exposure. Record uncertainty explicitly: an unknown population or unverified deletion should remain an open risk, not be silently treated as zero impact.
Restore with evidence
Before re-enabling traffic, run regression, safety, security, adversarial, fairness, privacy, and performance tests against the repaired configuration. Verify monitoring and rollback controls, review the change with the required approvers, restore gradually, and watch the original indicators plus leading signals for recurrence. Keep the incident open until the commander accepts the restoration evidence and any notification or follow-up obligations are assigned.
How should the team communicate during an AI incident?
Communication is part of mitigation, not a public-relations add-on. Tell internal responders what is known, what is uncertain, who is affected, which feature is restricted, what users should do, and when the next update will arrive. Use one factual timeline and label hypotheses as hypotheses.
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For customers or affected communities, explain the practical impact, dates or duration, protective steps, available human support, and how to report additional harm. Do not expose personal data, exploit details, or unverified causal claims. Privacy/legal should determine whether a regulator, partner, customer, or individual notice is required in the relevant jurisdiction and contract.
NIST AI RMF MANAGE 4 calls for documented response, recovery, and communication plans, post-deployment monitoring, and communication about incidents and errors to relevant AI actors and affected communities. The framework states that “Incidents and errors are communicated to relevant AI actors, including affected communities.” See the NIST AI Risk Management Framework 1.0.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you test and improve the plan?
A plan that has not been exercised is a contact list, not a response capability. Test the controls and the human decisions separately, then combine them in realistic exercises.
Run practical exercises
- Tabletop a harmful-output event with an ambiguous report, a vulnerable user, and competing product deadlines.
- Inject a prompt-injection or data-exfiltration scenario that requires security isolation and privacy review.
- Exercise provider outage and model rollback with the safer fallback, human queue, and customer-support scripts.
- Simulate drift or classifier disagreement so reviewers must decide whether to pause, sample more, or escalate.
- Verify the kill switch, backups, access permissions, logging, paging, and restoration checklist in a controlled environment.
Measure time to detect, acknowledge, contain, communicate, recover, and close; recurrence rate; false-positive and false-negative alert rates; user-harm measures; and completion of corrective actions. Trend these by feature and severity rather than celebrating a single fast response.
Turn findings into owned work
Run a blameless review that identifies technical, process, training, vendor, and product contributors. Each corrective action needs an owner, due date, acceptance test, and tracking location. Feed lessons into evaluation sets, red-team cases, monitoring thresholds, model cards or system documentation, vendor reviews, user education, and product design.
NIST SP 800-53 IR-04 defines incident handling as preparation, detection and analysis, containment, eradication, and recovery. FedRAMP and NIST guidance also require lessons from incidents and tests to update procedures, training, and the plan after system or organizational changes; review the control text at the incident-response controls page.
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What should be in the published playbook template?
Keep a concise operational page that links to deeper procedures. At minimum, include:
- System card: feature, model and version, data sources, tools, users, geographies, intended use, foreseeable misuse, dependencies, and fallback.
- Contacts: commander and backups, on-call routes, specialist owners, executives, vendors, regulators, and approved communication channels.
- Severity matrix: harm classes, context factors, thresholds, escalation clocks, required approvers, and notification triggers.
- Detection map: dashboards, alert rules, sampling plan, reviewer queues, report intake, log location, retention, and access controls.
- Containment runbooks: exact commands or UI paths for rate limits, policy changes, routing, human review, rollback, tool disablement, bypass, and deactivation, with entry and exit tests.
- Investigation record: timeline, evidence checklist, affected-scope analysis, hypotheses, decisions, approvals, and links to changes.
- Recovery and communication: regression and adversarial tests, staged restoration, status-message templates, support instructions, and notification owners.
- Learning log: metrics, root and contributing causes, corrective actions, due dates, and the next exercise or review date.
Review the page whenever a model, prompt, policy, data source, vendor, user population, geography, or organizational owner changes, and after every exercise or incident.
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Start with one inventory, one commander rotation, one severity matrix, and two tested controls: human review and a complete feature bypass. Add logging that captures model and prompt versions, a monitored report channel, and a weekly review of anomalies. As usage and risk grow, split specialist duties, add quantitative thresholds, and automate paging, sampling, and rollback checks. A small team still needs explicit authority; “the engineer on duty will decide” is not a durable decision right.
For high-impact workflows, involve privacy, legal, security, and affected-user representatives before launch rather than waiting for the first incident. Thresholds, retention, notification duties, and restoration tests are deployment-specific decisions that must be checked against current law, contracts, provider behavior, and the product’s risk class.
Frequently Asked Questions
What is the difference between an AI incident and a normal software bug?
A normal bug often has a deterministic input-output failure, while an AI incident can be probabilistic, socially harmful, population-specific, or caused by changing data and model behavior. Treat a bug as an AI incident when it creates material safety, privacy, security, fairness, legal, or oversight risk, even if availability is unaffected.
How long should AI incident logs and prompts be retained?
There is no universal period. Set retention by the data involved, contractual and legal duties, investigation needs, and deletion requirements; minimize or redact content, restrict access, and preserve a separate evidence copy only when justified by an incident or legal hold.
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Should a vendor outage automatically trigger an AI feature shutdown?
Not always. The decision depends on whether the outage removes safety controls, changes output behavior, blocks human review, or leaves a tested fallback; your third-party playbook should define those conditions and the supplier notification path.
How do we involve people affected by an AI failure?
Give affected users or communities a practical reporting and appeal channel, accessible explanations of the impact, protective steps, and human assistance. Feed their reports into severity assessment and corrective-action review rather than treating support volume as a purely technical metric.
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