This is a governance artifact to share with clients or regulators to demonstrate how AI-related incidents will be managed.

Incident & Escalation Playbook for AI Systems

Mercury Security | 2025

Introduction

AI incidents—such as unsafe outputs, system failures, or compliance breaches—require structured response processes. Without predefined playbooks, organizations risk delayed responses, unclear accountability, and increased regulatory exposure. This playbook provides a standard approach to incident identification, escalation, containment, and reporting for AI agents and audited systems. It aligns with the EU AI Act, GDPR, NIST AI RMF, and ISO/IEC 42001 (European Union, 2016; European Union, 2024; NIST, 2023; ISO, 2023).

Purpose

The purpose of this playbook is to:

• Ensure rapid and consistent handling of AI-related incidents.
• Define clear escalation routes and response timelines.
• Provide defensible documentation for governance and regulatory review.

Scope

This playbook applies to all AI systems hosted or audited by Mercury Security, including:

• Customer-facing agents (chatbots, reception, social media posting).
• Internal knowledge retrieval and workflow agents.
• Audit and evidence-processing systems.

Incident Categories

Incidents are categorized as follows:

• Category 1 — Minor: Low-impact issues, such as isolated hallucinations or non-critical guardrail failures.
• Category 2 — Significant: Repeated unsafe outputs, partial log failures, or delayed escalations.
• Category 3 — Critical: PII leaks, systemic bias detected, failure of refusal guardrails, or uncontained harmful outputs.

Escalation Matrix

Incident Response Process

1. Identification: Incident detected via logs, monitoring alerts, or user reports.
2. Classification: Assign incident to Category 1, 2, or 3 based on impact.
3. Containment: Apply immediate guardrails or disable affected feature.
4. Escalation: Notify appropriate teams based on escalation matrix.
5. Investigation: Collect evidence (logs, screenshots, outputs).
6. Resolution: Implement fixes, document actions, and retest system.
7. Reporting: Generate incident report with root cause and remediation steps.
8. Review: Governance team conducts after-action review to improve processes.

Incident Report Template

Incident ID: ___________________________
Date/Time Detected: ____________________
System Affected: ________________________
Category: ☐ Minor ☐ Significant ☐ Critical
Description of Incident: ________________________________________
Actions Taken: _______________________________________________
Escalation Triggered: ☐ Yes ☐ No
Resolution Date: _______________________
Follow-up Actions: ____________________________________________
Reviewed By: __________________________

Continuous Improvement

Incident patterns must be analyzed quarterly to identify systemic risks. Recurring issues should trigger a governance review and, where necessary, a full re-audit of affected systems.

Conclusion

An incident playbook transforms unpredictable failures into manageable governance events. By defining categories, escalation routes, and response timelines, Mercury Security ensures AI systems remain transparent, defensible, and resilient.

References

European Union. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council (General Data Protection Regulation). Official Journal of the European Union.

European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (AI Act). Official Journal of the European Union.

ISO. (2023). ISO/IEC 42001:2023 Information technology — Artificial intelligence — Management system. International Organization for Standardization.

National Institute of Standards and Technology. (2023). AI Risk Management Framework (NIST AI RMF 1.0). Gaithersburg, MD: NIST.