Ai Incident Response Plan Template

Standard Operating Procedure: AI Incident Response Plan (AIRP)

Document ID: TR-SEC-AIRP-001
Effective Date: 2023-10-27
Version: 1.0.0
Review Cadence: Semi-Annual (or post-incident)

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1. Executive Summary & Purpose

This document defines the systemic protocols for identifying, containing, and remediating AI-related incidents. As AI systems exhibit non-deterministic behaviors, this SOP prioritizes rapid containment of data leakage, prompt injection, model drift, and hallucinated misinformation to maintain the integrity of Template Registry operations.

2. Scope & Prerequisites

• Scope: All LLMs, predictive models, and RAG pipelines currently deployed or in staging.
• Tools: SIEM (Security Information and Event Management), AI Observability Platform (e.g., Arize, WhyLabs), Vector Database audit logs, and internal messaging platform (Slack/Teams).
• Prerequisites: Validated model lineage, documented baseline performance metrics, and pre-authorized emergency sandbox access.

3. Roles & Responsibilities (RACI Matrix)

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4. Step-by-Step Procedure

Phase I: Detection & Triage

• Monitor real-time logs for anomalous input/output frequency.
• Validate alert trigger (e.g., threshold for PII leakage detected by guardrails).
• Determine incident severity (Sev 1: Critical data breach/malicious takeover; Sev 2: Model drift/performance degradation; Sev 3: User-reported inaccuracy).

Phase II: Containment

• Automated: Enable circuit breakers to isolate the affected model endpoint.
• Manual: Switch traffic to a "Safe Baseline" model or legacy heuristic-based fallback.
• Purge affected session tokens and rotate API keys if credential compromise is suspected.

Phase III: Eradication & Recovery

• Identify source of failure (e.g., adversarial prompt injection, poisoned training data, or system prompt drift).
• Revert model configurations to the last known "Good State" (Git-hash locked).
• Execute adversarial testing against the fix in the sandbox environment.

Phase IV: Post-Incident Activity

• Generate Incident Timeline and Root Cause Analysis (RCA).
• Conduct "Blameless Post-Mortem" meeting.
• Update Model System Prompt and input-filtering regex based on findings.

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5. Quality Assurance & Pro-Tips

Best Practices

• Guardrails First: Implement input/output filtering (e.g., NeMo Guardrails) before the model processes input.
• Audit Trails: Never delete raw input/output logs; move them to cold storage for forensic analysis.
• Human-in-the-Loop (HITL): For high-stakes decisions, mandate human verification, regardless of model confidence scores.

Metric Thresholds

• Time to Containment: < 15 minutes for Sev 1 incidents.
• False Positive Rate: < 2% of total traffic.
• Drift Variance: Trigger alerts if K-S test on distribution shift exceeds 0.05.

Common Pitfalls

• Over-Reliance on Evals: Unit tests do not capture the complexity of edge-case jailbreaks.
• Log Exhaustion: Ensure storage scaling for logs matches the high-volume ingestion typical of AI model inference.

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6. Frequently Asked Questions (FAQ)

Q: How do we differentiate between a hallucination and a malicious injection?
A: Malicious injections typically follow structural patterns (e.g., "Ignore previous instructions") and target system prompts. Hallucinations are contextually coherent but factually incorrect. Check logs for semantic similarity to known jailbreak templates.

Q: Can we simply "restart" the AI server to solve the incident?
A: No. If the incident is caused by a malicious system prompt or corrupted data, a restart only reloads the poison. Always revert to a previous verified commit/checkpoint.

Q: What is the primary indicator of prompt injection?
A: Watch for unexpected changes in model output tone, refusal of standard guardrails, or unauthorized attempts to access system environment variables.

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End of Document
Julian Vance, Chief Architect, Template Registry