Meet 24-hour AI incident reporting clocks and build systemic resilience

Regulatory landscape and obligations

EU AI Act. Articles 62–75 cover post-market monitoring, incident reporting, corrective actions, and cooperation with authorities. Deployers must implement post-market plans that collect data on performance, incidents, and near misses. Serious incidents include breaches of safety, health, or fundamental rights, as well as misuse or reasonably foreseeable abuse. Reporting must occur within 15 days, with immediate notification for public-safety threats.^{Regulation (EU) 2024/1689} Providers must cooperate with authorities, execute corrective actions, and document updates in technical files.

OMB M-24-10. Section 7 requires agencies to establish processes for detecting and reporting safety-impacting incidents within 24 hours, followed by detailed seven-day and thirty-day reports containing root causes, mitigation steps, and lessons learned. Incidents affecting civil rights, civil liberties, or privacy demand similar treatment. Agencies must coordinate with inspectors general, the Office of Science and Technology Policy, and other oversight bodies.^{OMB M-24-10}

CIRCIA. While final rules are pending, the law will obligate critical infrastructure entities to notify CISA of substantial cyber incidents—AI-enabled or otherwise—within 72 hours, and ransom payments within 24 hours. Early preparation requires aligning AI IR processes with CIRCIA definitions, data retention, and evidence preservation requirements.^CIRCIA

Sector regulations. The EU Cyber Resilience Act introduces 24-hour reporting for exploited vulnerabilities in products with digital elements, affecting AI-enabled devices.^{Cyber Resilience Act briefing}

The UK AI Safety Institute’s roadmap outlines systemic-risk evaluation, red-teaming, and reporting expectations for frontier models, signalling future incident reporting collaboration with international partners.^{UK AISI roadmap} Organisations must monitor these developments and adjust incident programmes accordingly.

Define an AI incident taxonomy

A shared taxonomy ensures consistent triage and reporting. Establish the following categories:

• Safety-impacting events. Incidents causing or risking physical harm, critical infrastructure disruption, or safety system failure.
• Rights-impacting events. Incidents producing discriminatory outcomes, privacy breaches, or due-process violations.
• Security compromises. Prompt-injection exploits, data poisoning, model theft, jailbreaks, or adversarial manipulations.
• Integrity failures. Hallucinations, misinformation, or policy violations that mislead users or downstream systems.
• Availability incidents. Model outages, degraded performance, or dependency failures affecting service level agreements.
• Systemic-risk indicators. Events suggesting loss of control in frontier models, cascading errors across jurisdictions, or cross-customer impacts.

Map each category to severity levels. Use criteria such as affected population, regulatory exposure, financial loss, and media visibility. Align severity with reporting triggers: high-severity incidents likely require immediate notifications to regulators, while medium-severity incidents may require 24-hour reporting depending on jurisdiction.

Document taxonomy definitions in incident response plans, playbooks, and training materials. Align them with procurement and evaluation taxonomies to ensure consistent communication across functions.

Detection and monitoring strategies

Effective detection combines technical telemetry, human reporting, and supplier notifications. Key components include:

• Model telemetry. Instrument prompts, responses, confidence scores, safety filter activations, refusal rates, and escalation flags. Stream data into monitoring platforms with alert thresholds aligned to incident taxonomy.
• Input/output anomaly detection. Use statistical and machine-learning detectors to flag unusual token distributions, sentiment shifts, or behavioural changes indicative of adversarial attacks.
• Guardrail integration. Deploy policy enforcement layers (content filters, rate limiting, contextual access controls) and alert when guardrails trigger or fail.
• Human feedback. Provide end-user reporting mechanisms, such as in-product flags or hotline channels, to capture real-world issues quickly. Train reviewers on the taxonomy and escalation paths.
• Supplier feeds. Require vendors to deliver incident notifications, patch advisories, and systemic risk reports. Integrate feeds into ticketing systems for rapid triage.
• Threat intelligence. Subscribe to advisories from CISA, ENISA, and the UK AI Safety Institute. Zeph Tech’s Black Basta joint advisory demonstrates how multi-agency alerts shape monitoring priorities.

Integrate detection controls with security operations centres (SOCs) and AI governance teams. Define responsibility for tuning detectors, responding to alerts, and maintaining dashboards. Document detection coverage in ISO/IEC 42001 management reviews.

Response lifecycle

Follow NIST SP 800-61’s incident lifecycle, adapted for AI.

Preparation

Maintain updated incident response plans, playbooks, communication templates, contact lists, forensic toolkits, and training records. Coordinate with procurement to ensure contracts support evidence collection and remediation access.

Detection and analysis

Upon alert, assemble a cross-functional incident team including AI engineers, evaluators, security analysts, legal counsel, and communications. Gather evidence: telemetry logs, model versions, dataset snapshots, prompts, and user reports. Determine incident category, severity, and regulatory scope.

Containment

Implement containment strategies such as disabling affected models, reverting to safe baselines, isolating compromised infrastructure, or deploying manual review. For generative systems, enforce safe-mode prompts or limit capabilities while investigations proceed.

Eradication and recovery

Identify root causes—misconfigurations, data poisoning, supplier changes, or malicious actors. Apply fixes, retrain models, patch guardrails, or update policies. Validate remediation through targeted evaluations and regression tests. Restore normal operations only after confirming controls are effective.

Communications

Coordinate internal and external communications with legal oversight. Notify executives, regulators, customers, and partners as required. Prepare public statements that reference actions taken, residual risks, and support channels. Ensure messaging aligns with regulatory submissions.

Regulatory reporting

Prepare reports for relevant authorities. For EU incidents, submit required data through national portals, including system details, incident description, corrective actions, and risk mitigation. For OMB reporting, deliver 24-hour, seven-day, and thirty-day updates with detailed analysis. If CIRCIA applies, coordinate with the SOC to send notifications within statutory deadlines.

Post-incident review

Conduct lessons-learned sessions within two weeks. Review detection effectiveness, response timeliness, decision-making, documentation, and stakeholder communications. Update policies, playbooks, training, and metrics accordingly. Feed insights into evaluation and procurement programmes to prevent recurrence.

Documentation and evidence

Regulators expect detailed documentation. Maintain:

• Incident response plans referencing AI-specific obligations and contact lists.
• Incident records capturing timelines, decisions, communications, remediation, and evidence retention.
• Regulatory submissions with receipts, acknowledgement numbers, and follow-up actions.
• Evaluation results confirming remediation effectiveness.
• Supplier communications documenting support provided, patch deployment, and contractual obligations.

Store documentation in secure, access-controlled repositories with retention schedules aligned to legal requirements. Provide auditors with structured evidence packages during assessments.

Telemetry architecture and tooling

Incident readiness depends on reliable telemetry. Design data flows that capture prompts, outputs, model versions, feature flags, and guardrail events across every AI capability. Route telemetry to a central platform where security and AI engineering teams can perform correlation and anomaly detection. Ensure logs are immutable, time-synchronised, and retained according to regulatory requirements—OMB M-24-10 Appendix C expects agencies to preserve evidence for independent evaluations, while the EU AI Act requires providers and deployers to maintain logs for post-market monitoring and investigations.^{OMB M-24-10Regulation (EU) 2024/1689}

Align telemetry with existing security tooling. Integrate AI logs into SIEM and SOAR platforms so analysts can pivot between AI-specific events and broader infrastructure indicators. Tag events with model identifiers, supplier metadata, and evaluation references. When an alert fires, responders should be able to trace the model release, dataset lineage, and control environment immediately.

Implement role-based access controls over telemetry stores. Sensitive prompts, personal data, and proprietary content must be protected under privacy regulations and contractual obligations. Use pseudonymisation where possible and document data-handling procedures to satisfy privacy impact assessments.

Finally, automate health checks. Create dashboards that track telemetry freshness, ingestion errors, and data quality indicators. Missing or delayed logs can undermine incident investigations, so monitor pipelines and establish on-call rotations to remediate ingestion failures quickly.

Legal, privacy, and communications coordination

Legal and privacy teams should be embedded throughout the incident lifecycle. Establish privilege protocols that allow legal counsel to direct investigations when necessary, preserving confidentiality while supporting regulator cooperation. Document thresholds for when legal review is required—such as cross-border data transfers, potential litigation exposure, or whistle-blower complaints.

Coordinate with privacy officers to assess whether incidents trigger GDPR, CCPA, or sectoral breach notifications alongside AI-specific reports. For example, an AI system that exposes personal data may require simultaneous notifications to EU data protection authorities and AI market surveillance bodies. Maintain a matrix mapping jurisdictional obligations, data categories, and reporting windows.

Communications teams should prepare message templates tailored to employees, customers, regulators, and the public. Templates must reference remediation steps, contact points, and available support resources. Align messaging with regulator submissions to avoid discrepancies that could erode trust. Conduct media training for executives who may address high-profile incidents.

Customer and partner engagement

Many AI incidents affect customers or partners directly. Establish dedicated liaison teams responsible for outbound communications, impact assessments, and remediation guidance. Provide customers with clear instructions on workarounds, temporary safeguards, and reporting channels for downstream issues. When vendors supply the AI capability, coordinate joint statements and ensure that customer-facing guidance references authoritative sources.

Create service-level targets for customer outreach—for example, initial notifications within six hours of confirming impact and daily updates until remediation concludes. Track customer sentiment and support ticket trends to verify that mitigations are effective. Feed lessons into procurement and product management so contractual commitments and roadmap priorities reflect customer expectations.

Example reporting timeline

Use the following template to rehearse reporting workflows:

1. T0–2 hours. Confirm incident scope, assemble response team, preserve evidence, and notify legal counsel.
2. T2–6 hours. Issue internal alerts, engage supplier contacts, determine regulatory triggers, and prepare preliminary customer communications.
3. T6–24 hours. Submit 24-hour reports to OMB and the National AI Initiative Office if thresholds are met, notify relevant EU national authorities for serious incidents, and provide customers with actionable guidance.
4. T+7 days. Deliver detailed reports including root-cause analysis, mitigation steps, and residual risk assessments.
5. T+30 days. Provide final updates, document lessons learned, and integrate remediation into evaluation and procurement roadmaps.

Adapt the timeline for CIRCIA once final rules are published, ensuring cyber incident reporting integrates with AI-specific obligations.^CIRCIA

Third-party and supplier coordination

AI incidents frequently involve upstream vendors, managed services, or integration partners. Maintain a supplier response matrix that lists contractual obligations, escalation contacts, and shared evidence repositories. EU AI Act Article 71 requires providers and deployers to cooperate with competent authorities, which extends to sharing incident data and corrective actions with customers and regulators.^{Regulation (EU) 2024/1689}

Establish joint playbooks with key vendors covering telemetry exchange, forensic support, and coordinated communications. Validate that vendors can deliver required evidence within agreed timeframes and that access controls allow secure sharing without exposing unrelated customer data. For cloud-hosted services, confirm that logging configurations capture tenant-specific events to avoid gaps during investigations.

Engage third-party assessors—such as managed security providers or industry information sharing and analysis centres (ISACs)—to provide independent validation and threat intelligence. Participation in forums like the AI Safety Institute Consortium can surface emerging incident patterns and mitigation techniques that inform internal playbooks.^{NIST AISIC}

Document outcomes from supplier exercises and audits. Track remediation commitments, retest dates, and outstanding risks in a shared register reviewed during governance meetings. Linking supplier follow-up to procurement scorecards prevents repeated slippage and ensures lessons learned inform contract renewals.

Exercises and resilience

Regular exercises test readiness and build muscle memory. Design tabletop scenarios covering:

• Bias or discrimination discovered in workforce tools, referencing Department of Labor principles.
• Prompt-injection attack on a customer-facing assistant causing data leakage.
• Systemic-risk event in a foundation model requiring coordination with the European AI Office and UK AI Safety Institute.
• Supply-chain compromise where a vendor update introduces malicious code.
• Cross-border incident requiring simultaneous EU and U.S. notifications.

Conduct technical simulations for high-risk systems, including red-team exercises, chaos engineering, and failover drills. Align with Zeph Tech’s incident audit briefing to rehearse regulator walkthroughs.

Track exercise outcomes, remediation plans, and executive briefings. Use metrics to demonstrate improvement over time.

Integrate with enterprise functions

AI incident response intersects multiple teams. Coordinate with:

• Procurement. Ensure contracts support incident investigation, evidence access, and remediation commitments. Reference the AI procurement guide for clause alignment.
• Evaluation. Feed incidents into evaluation backlogs for targeted testing and risk reassessment.
• Security operations. Align AI telemetry with broader SOC tooling and playbooks. Integrate with vulnerability management for AI-enabled products.
• Privacy and compliance. Assess legal exposure, privilege considerations, and cross-border data transfers.
• Communications and public affairs. Prepare messaging, stakeholder briefings, and regulator engagement strategies.
• Workforce enablement. Provide training on incident reporting channels and escalation for staff operating AI systems.

Establish a governance committee that reviews incidents quarterly, monitors metrics, and approves programme updates.

Metrics and continuous improvement

Measure programme effectiveness with metrics such as:

• Mean time to detect AI incidents by category.
• Mean time to contain and recover.
• Compliance with 24-hour, seven-day, and fifteen-day reporting windows.
• Percentage of incidents with completed lessons-learned reviews within 14 days.
• Training completion rates for AI incident responders.
• Exercise coverage across high-risk systems.

Report metrics to leadership monthly. Use trends to adjust resource allocation, tooling, and training. Align improvements with ISO/IEC 42001 continual improvement requirements.

Develop composite indicators that link incident performance to business outcomes. For example, track the percentage of product releases delayed due to unresolved AI risks, or the number of customer contracts requiring remediation commitments. Tie these indicators to procurement and evaluation dashboards so leadership can view risk posture holistically.

Benchmark metrics externally when possible. Participate in industry sharing groups, leverage NIST or CISA maturity assessments, and compare results against Zeph Tech’s incident rehearsal findings to identify areas for acceleration. Document rationale when metrics change to maintain historical comparability.

Share metric summaries with audit committees and risk councils to reinforce oversight and secure resources for remediation initiatives.

Ninety-day implementation roadmap

Follow this plan to launch or upgrade AI incident response capabilities.

Days 1–30: Baseline and governance

• Inventory AI systems. Map AI deployments to incident taxonomy, noting regulators, suppliers, and evaluation status.
• Publish AI IR policy. Document obligations under EU AI Act, OMB M-24-10, CIRCIA, and sector rules. Obtain executive approval.
• Form incident steering group. Include security, CAIO office, legal, procurement, communications, and evaluation leads.
• Assess tooling. Review telemetry coverage, alerting, logging, and evidence retention. Identify gaps.

Days 31–60: Capability deployment

• Implement detection enhancements. Configure AI telemetry streams, anomaly detectors, and guardrail alerts.
• Develop playbooks. Draft category-specific response playbooks with regulatory reporting steps.
• Launch training. Deliver responder training on taxonomy, tooling, and communication protocols.
• Run tabletop. Conduct at least one cross-functional tabletop covering a high-severity scenario.

Days 61–90: Validation and optimisation

• Execute live drills. Simulate detection-to-reporting flows, measuring time against statutory windows.
• Integrate with enterprise risk. Feed metrics into risk dashboards and board reporting.
• Refine documentation. Update policies, playbooks, and evidence templates based on drill outcomes.
• Schedule continuous improvement. Plan quarterly exercises, supplier coordination calls, and policy refreshes.

Appendix: Artefact checklist

• AI-specific incident response policy and playbooks.
• Incident taxonomy, severity matrix, and regulatory mapping.
• Telemetry architecture diagrams and alert configuration records.
• Regulator reporting templates (EU AI Act, OMB M-24-10, CIRCIA drafts).
• Exercise schedules, scenarios, and after-action reports.
• Training curricula and attendance logs for incident responders.
• Evidence repository with incident records, remediation plans, and communications logs.

Maintaining these artefacts demonstrates readiness, supports audits, and aligns AI adoption with safety and resilience commitments.