A 20-page AI incident response plan template with 56 controls across 9 response phases - from detection through post-incident review. Covers severity classification for prompt injection, data leakage, model poisoning, hallucination harm, and bias incidents. Includes regulatory notification timelines for GDPR (72h), EU AI Act Art. 73 (72h), and HIPAA (60 days), plus a complete RACI matrix and communication protocols for AI-specific security incidents.
A 56-item AI incident response plan covering 9 phases from detection to post-incident review. Includes severity classification, containment procedures, regulatory notification timelines, and RACI matrix for AI-specific security incidents.
Organisations with a tested incident response plan save an average of $2.66 million per breach compared to those without one (IBM 2024 Cost of a Data Breach) - yet fewer than 30% of enterprises have an IR plan that covers AI-specific incident types like prompt injection, model poisoning, or hallucination harm.
The EU AI Act Article 73 and GDPR Article 33 both mandate a 72-hour notification window for serious AI incidents and personal data breaches respectively - meaning your response team must be able to classify, contain, and begin regulatory notification within three days, not three weeks.
AI incidents require fundamentally different containment procedures than traditional security events. Revoking a compromised API key is not enough - you may need to quarantine model weights, flush poisoned training data, revoke RAG document access, and audit every output generated since the compromise window began.
The average time to identify and contain a data breach is 258 days (IBM 2024). Organisations with AI-specific incident response playbooks that include pre-defined severity matrices and automated detection reduce mean time to containment by 40-60%, bringing it below 100 days.
HIPAA breach notification allows 60 days, but GDPR and EU AI Act require 72 hours - if your AI system processes data across multiple jurisdictions, the shortest deadline governs your entire response timeline, making pre-staged notification templates and legal review a critical preparedness measure.
56 actionable controls across 9 response phases to detect, contain, investigate, and recover from AI-specific security incidents.
Define the taxonomy of AI-specific incidents and assign severity levels that drive escalation, containment, and notification decisions.
Establish monitoring and detection capabilities that identify AI-specific incidents in real time. Traditional SIEM rules miss AI attack vectors.
Own the AI incident response programme, define severity thresholds, and ensure cross-functional readiness through quarterly tabletop exercises
Execute detection, triage, and containment procedures for AI-specific incidents including prompt injection, data exfiltration, and model manipulation
Manage regulatory notification timelines across GDPR (72h), EU AI Act Art. 73 (72h), and HIPAA (60 days) and maintain audit-ready incident documentation
Advise on legal obligations during AI incidents including breach notification, privilege considerations, litigation holds, and regulatory engagement strategy
Lead technical investigation, root cause analysis, and remediation for model-level incidents including poisoning, adversarial attacks, and output integrity failures
AI incidents involving PHI trigger HIPAA Breach Notification rules with a 60-day reporting window to HHS. If an AI medical device produces harmful outputs, FDA Medical Device Reporting (MDR) may also apply. Sections 4 and 7 include healthcare-specific containment and notification procedures for AI systems processing patient data.
Financial institutions must notify the SEC under Reg S-P and report to OCC/FFIEC for AI incidents affecting customer financial data. DORA Article 19 requires major ICT-related incident reporting within 4 hours of classification. Sections 3 and 7 map AI incident severity to financial regulatory thresholds and reporting obligations.
The EU AI Act Article 73 requires providers of high-risk AI systems to report serious incidents to market surveillance authorities within 72 hours. GDPR Article 33 imposes the same 72-hour window for personal data breaches. Sections 7 and 8 provide pre-staged notification templates and dual-track reporting workflows for both regulations.
Federal agencies must report AI incidents under FISMA and CISA directives, with CIRCIA mandating 72-hour reporting for covered critical infrastructure entities. Sections 5 and 9 align investigation and communication procedures to federal incident reporting requirements and NIST SP 800-61 guidance for AI system incidents.
Define the taxonomy of AI-specific incidents and assign severity levels that drive escalation, containment, and notification decisions. Traditional incident classification frameworks do not account for AI-specific attack vectors - this section establishes a purpose-built severity matrix.
Establish monitoring and detection capabilities that identify AI-specific incidents in real time. Traditional SIEM rules miss AI attack vectors - this section covers purpose-built detection for prompt injection, data exfiltration through AI channels, and model integrity monitoring.
Rapidly assess the scope, impact, and regulatory implications of a detected AI incident. Effective triage determines whether an incident requires full response mobilisation or can be handled through standard operating procedures.
Execute immediate containment actions to stop ongoing damage and prevent incident expansion. AI containment requires actions beyond traditional network isolation - you may need to quarantine models, flush caches, revoke RAG access, and halt automated pipelines.
Take our 2-minute assessment and get a personalised AI governance readiness report with specific recommendations for your organisation.
Start Free AssessmentConduct thorough forensic investigation to determine how the AI incident occurred, what data or systems were affected, and what the root cause was. AI investigations require specialised techniques including prompt log analysis, model behaviour forensics, and training data provenance review.
Remove the threat from your environment and restore AI systems to normal operations. Eradication for AI incidents may require retraining models, rebuilding RAG pipelines, and validating output integrity before returning systems to production.
Execute mandatory regulatory notifications within the required timelines. AI incidents may trigger multiple notification obligations simultaneously across different jurisdictions - GDPR requires 72-hour notification to supervisory authorities, EU AI Act Article 73 mandates 72-hour serious incident reporting, and HIPAA allows 60 days for breach notification to HHS.
Conduct a structured post-incident review to extract lessons learned and drive continuous improvement. Every AI incident is an opportunity to strengthen your defences - but only if findings are formally documented, tracked, and implemented.
Define clear roles, responsibilities, and communication channels for every phase of the AI incident response lifecycle. Ambiguity during incidents costs time - this section eliminates confusion about who does what, who is informed, and how information flows.
Build a complete AI governance programme with these complementary templates.
A comprehensive 47-point checklist across 9 security domains to help CISOs build a board-ready AI governance policy. Covers acceptable use, data classification, shadow AI, vendor assessment, compliance mapping, incident response, and more.
Download FreeA structured 48-item risk register across 8 risk domains with a 5x5 scoring matrix to help CISOs identify, assess, treat, and track AI-specific risks. Covers data privacy, model reliability, bias, security, compliance, operational, and reputational risk categories with board-ready reporting dashboards.
Download FreeAn 18-page operational playbook with 56 action items across 8 discovery phases for finding, assessing, and remediating unsanctioned AI usage across your organisation. Covers network-level detection, browser extension monitoring, SaaS auditing, department surveys, risk scoring, migration pathways, and ongoing safe harbour programmes.
Download FreePrompt injection is the most critical vulnerability in enterprise LLM deployments. Learn how direct and indirect prompt injection attacks work, explore the OWASP LLM Top 10, and implement multi-layer defense strategies including input validation, output filtering, and architectural isolation.
A comprehensive guide to the 10 most dangerous attack vectors targeting large language models in 2026. From prompt injection and data poisoning to model extraction and agent tool misuse, learn how each attack works, its real-world impact, and enterprise defense strategies.
AI red teaming is the practice of adversarially testing AI systems to discover vulnerabilities before attackers do. Learn the methodologies (NIST 600-1, Microsoft AI Red Team), attack types to test, and how to build a continuous adversarial testing program for enterprise LLM deployments.
Fill in your details below for instant access to the full 20-page checklist.
“This framework saved us 3 months of policy development. We went from zero AI governance to audit-ready in under 2 weeks.”
— Security Leader, Mid-Market Healthcare Organisation
Need more than a checklist?
See how Areebi automates and enforces every control in this checklist across your entire organisation.
Book a DemoThe checklist tells you what to do. Areebi does it for you - automated DLP, audit logging, policy enforcement, and compliance reporting across every AI interaction.