AI Incident Response Checklist

Step-by-step checklist for responding to AI security incidents, from initial detection through containment, investigation, remediation, and post-incident review.

AI Incident Response Checklist

AI 安全 incidents differ from traditional cybersecurity events in several fundamental ways. Model behavior is probabilistic rather than deterministic, making root cause analysis harder. 攻擊 evidence may not persist in traditional logs. And the blast radius of a compromised model can extend across every interaction 模型 handles.

This checklist provides a structured approach to AI incident response organized into six phases: preparation, 偵測 and triage, containment, investigation, remediation, and post-incident review.

Phase 1: Preparation (Before an Incident)

Preparation is the most important phase. Organizations that invest in preparation respond faster and more effectively when incidents occur.

Team Readiness

• 識別 AI incident response team members — Include ML engineers, 安全 analysts, data scientists, legal counsel, and communications personnel. AI incidents require cross-functional expertise that traditional 安全 teams may lack.
• Define roles and responsibilities — Assign an incident commander, technical lead, communications lead, and legal advisor. Ensure each role has a backup.
• Establish communication channels — Set up dedicated, secure channels for incident coordination. Avoid using the same AI systems that may be compromised.
• Conduct tabletop exercises — Run quarterly exercises covering scenarios like 提示詞注入 at scale, model 投毒 discovery, 訓練資料 breach, and 對抗性 manipulation of production models.
• Create decision trees — Pre-define escalation criteria: when to involve legal, when to notify customers, when to take models offline.

Technical Preparation

• 實作 comprehensive logging — Log all model inputs and outputs with timestamps, session identifiers, and user attribution. Ensure logs are stored separately from the systems they monitor.
• Establish model behavior baselines — Document expected 輸出 distributions, latency patterns, 符元 usage, and refusal rates. Anomaly 偵測 requires knowing what normal looks like.
• Deploy 監控 and alerting — Set up automated alerts for 輸出 distribution drift, unusual 符元 patterns, spike in refusal rates, latency anomalies, and unusual API usage patterns.
• Maintain model inventory — Keep an up-to-date catalog of all deployed models, their versions, 訓練資料 sources, 微調 history, and dependencies.
• 實作 rollback capability — Ensure you can quickly revert to a previous model version or configuration without service disruption.
• Secure forensic infrastructure — Prepare isolated environments for analyzing compromised models, poisoned 訓練資料, or 對抗性 inputs without risk of contamination.

Documentation

• Document model architectures — Include data flows, API endpoints, 認證 mechanisms, and integration points for every deployed model.
• Maintain vendor contact lists — For models using third-party APIs (OpenAI, Anthropic, etc.), keep current contact information for their 安全 teams and know their incident reporting procedures.
• Create playbooks for common scenarios — Develop specific response procedures for 提示詞注入, 資料投毒, model theft, 對抗性 attacks, and 供應鏈 compromise.

Phase 2: 偵測 and Triage

The first hours of an AI incident are critical. Rapid 偵測 and accurate triage determine everything that follows.

Initial 偵測 Signals

AI incidents are typically detected through one of these channels:

Triage Checklist

• Assign incident severity — Use a consistent scale. Suggested AI-specific severity criteria:

• Determine scope — Which models are affected? Which environments? How many users have been exposed? What data has 模型 had access to?
• 識別 attack vector — Is this a 提示詞注入, 資料投毒, model manipulation, 供應鏈 compromise, or infrastructure attack?
• 評估 data exposure — Has 模型 leaked sensitive data? Has 訓練資料 been compromised? Are there privacy implications?
• Notify stakeholders — Alert the incident response team, management chain, and legal counsel based on severity.
• Begin incident log — Start documenting every action, finding, and decision with timestamps. This log is critical for post-incident review and potential regulatory reporting.

Evidence Preservation

• Capture current model state — Save model weights, configuration, and deployment parameters before any changes.
• Preserve logs — Ensure logging systems are writing to immutable storage. Copy relevant logs to a secure forensic environment.
• Save 對抗性 inputs — If specific attack inputs have been identified, preserve them with full context including headers, session data, and preceding conversation.
• Screenshot or record anomalous behavior — Visual evidence of unexpected model behavior can be invaluable for investigation and reporting.
• Document the timeline — When was the anomaly first observed? When was it reported? Establish a preliminary timeline.

Phase 3: Containment

Containment limits the blast radius. The goal is to stop the incident from getting worse while preserving evidence for investigation.

Immediate Containment (First Hour)

• 評估 whether to take 模型 offline — Weigh the impact of downtime against the risk of continued 利用. For P1 incidents, default to taking 模型 offline.
• 實作 輸入 filtering — If the attack vector is known, deploy 輸入 filters to block similar payloads. 這是 a temporary measure, not a fix.
• Restrict model access — Reduce 模型's tool access, API 權限, or data source connections to minimize potential damage.
• Isolate affected systems — If 模型 has been used to access other systems via 工具使用 or 代理, isolate those systems for investigation.
• Revoke compromised credentials — If 模型 has been used to exfiltrate API keys, 符元, or credentials, revoke them immediately.
• Enable enhanced logging — Increase logging verbosity on affected systems to capture additional evidence.

Short-Term Containment (First Day)

• Deploy a clean model version — If a known-good model version exists, deploy it as a replacement. Verify its integrity before deployment.
• 實作 additional 監控 — Set up targeted 監控 for the specific attack pattern and related variations.
• Review and restrict integrations — Audit all systems the affected model connects to. Disable non-essential integrations.
• Communicate with affected users — If users have been exposed to harmful outputs or their data has been compromised, notify them according to your communication plan.
• Coordinate with vendors — If the incident involves a third-party model API, report the incident to the vendor's 安全 team.

Phase 4: Investigation

Investigation determines what happened, how it happened, and the full extent of the impact.

攻擊 Vector Analysis

• Reconstruct the attack timeline — Using logs, 識別 the first malicious 輸入, the progression of the attack, and all affected interactions.
• Classify the attack type — Map the attack to known categories:

• 識別 攻擊者's objectives — Was this data exfiltration, service disruption, reputation damage, or something else? 理解 objectives helps predict what else 攻擊者 may have done.
• Determine the entry point — How did 攻擊者 gain access? Was it through 模型's public API, a compromised integration, a 供應鏈 vector, or insider access?

Impact 評估

• Quantify data exposure — Determine exactly what data 模型 may have disclosed. Review 輸出 logs for PII, credentials, proprietary information, or 訓練資料 leakage.
• 識別 affected users — Build a list of users who interacted with the compromised model during the incident window.
• 評估 downstream impact — If 模型 triggered actions via 工具使用 or 代理 workflows, determine what actions were taken and their consequences.
• 評估 model integrity — For 投毒 attacks, 評估 whether 模型's weights, 微調, or RLHF data have been corrupted.
• Check for lateral movement — Determine whether 攻擊者 used the AI system as a pivot point to access other systems.

Root Cause Analysis

• 識別 the 漏洞 — What specific weakness did 攻擊者 利用? Was it a missing 輸入 filter, insufficient access control, vulnerable dependency, or architectural flaw?
• Determine contributing factors — What environmental conditions enabled the attack? Missing 監控, inadequate access controls, lack of 輸入 validation, or insufficient 安全 對齊?
• 評估 偵測 gap — Why was the incident not detected sooner? What 監控 or alerting would have caught it earlier?

Phase 5: Remediation

Remediation fixes the 漏洞 and restores normal operations.

Technical Remediation

• Fix the root cause — 實作 a permanent fix for the 漏洞, not just a workaround. This may involve model retraining, architecture changes, or infrastructure hardening.
• Validate the fix — 測試 the remediation against the original attack and known variations. Use 紅隊 測試 to verify the fix is effective.
• Deploy the fix through normal change management — Unless urgency requires it, follow standard deployment procedures to avoid introducing new issues.
• Update 偵測 rules — Add 監控 and alerting for the attack pattern and its variations.
• Retrain if necessary — If 訓練資料 was poisoned, retrain 模型 from clean data. Verify data provenance before retraining.
• Update 安全 對齊 — If the attack exploited a 安全 bypass, work with the 對齊 team to address the gap.
• Patch dependencies — If the attack exploited a 漏洞 in a dependency such as a model serving framework or 向量資料庫, update to patched versions.

Process Remediation

• Update incident response playbooks — Incorporate lessons learned into your response procedures.
• Revise 監控 coverage — Address 偵測 gaps identified during the investigation.
• Strengthen access controls — Tighten 權限 based on what the investigation revealed about access patterns.
• Update 訓練 programs — Ensure the team is trained on the attack techniques encountered.

Recovery

• Restore normal operations — Remove temporary containment measures and return to standard operating procedures.
• Verify system integrity — Confirm that all affected systems are functioning correctly and securely.
• Monitor for recurrence — Maintain enhanced 監控 for a defined period, typically 30 to 90 days, to detect any recurrence.

Phase 6: Post-Incident Review

The post-incident review is where organizations learn and improve. Skip this phase at your peril.

Conduct the Review

• Schedule a blameless retrospective — Hold the review within two weeks of incident closure. Include all team members who participated in the response.
• Review the timeline — Walk through the incident chronologically, from 偵測 to resolution. 識別 what went well and what could improve.
• 評估 response effectiveness — How quickly was the incident detected? How effective was containment? Were the right people involved at the right times?
• 識別 systemic issues — Look beyond the specific incident for patterns that could lead to future incidents.

Documentation and Reporting

• Write an incident report — Document the incident comprehensively, including timeline, impact, root cause, remediation, and lessons learned.
• Update 模型 risk register — Add the identified 漏洞 and any new risk factors to your model risk tracking system.
• File regulatory reports — If required by regulation such as GDPR or HIPAA, file incident reports within mandated timeframes.
• Share findings appropriately — 考慮 sharing anonymized findings with the broader AI 安全 community to help others defend against similar attacks.
• Track remediation actions — Assign owners and deadlines for all remediation items. Review progress in regular 安全 meetings.

Metrics to Track

AI-Specific Incident Scenarios

Scenario: 提示詞注入 at Scale

When automated 提示詞注入 is detected affecting multiple users simultaneously:

1. Immediately enable 輸入 rate limiting
2. Deploy 提示詞注入 偵測 filters
3. Review all outputs generated during the attack window
4. 識別 and notify affected users
5. Analyze attack payloads for data exfiltration attempts
6. Check whether 模型's tool access was exploited

Scenario: Training Data Poisoning Discovery

When poisoned data is discovered in a production model's 訓練 set:

1. Determine when the poisoned data entered the pipeline
2. 識別 all models trained on the affected dataset
3. Compare model behavior before and after the 投毒
4. Retrain affected models from verified clean data
5. Audit the data pipeline for the injection vector
6. 實作 data provenance controls

Scenario: Model Supply Chain Compromise

When a third-party model or dependency is found to be compromised:

1. 識別 all systems using the compromised component
2. Verify integrity of local copies against known-good checksums
3. Isolate affected systems
4. Deploy verified alternatives
5. Audit actions taken by systems using the compromised component
6. Report to the affected vendor and relevant ISACs

Quick Reference: First 60 Minutes

For teams that need a rapid-start guide, here are the critical first-hour actions:

1. Minute 0-5: Confirm the incident is real and not a false positive
2. Minute 5-10: Assign severity, notify incident commander
3. Minute 10-15: Begin evidence preservation including logs and model state
4. Minute 15-30: 實作 immediate containment by restricting access and filtering inputs
5. Minute 30-45: 評估 scope and 識別 affected users and systems
6. Minute 45-60: Brief stakeholders, begin investigation

The first hour sets the trajectory for the entire response. Move quickly but methodically, and document everything.