Status: Summary of Final Blueprint
Author: Shahab Al Yamin Chawdhury
Organization: Principal Architect & Consultant Group
Date: August 23, 2023
Version: 1.0
1. Introduction & Executive Summary
This document provides a condensed overview of the comprehensive AI-specific Incident Response Plan (IRP). The full blueprint is designed to equip large enterprises with an adaptive framework to manage the sophisticated, probabilistic security threats introduced by AI and Machine Learning systems. It integrates leading standards like the NIST AI Risk Management Framework (RMF) and MITRE ATLAS to create a resilient defense strategy across the entire AI lifecycle.
The core of the plan adapts the classic IR lifecycle to address the unique challenges of AI, emphasizing proactive defense, behavioral anomaly detection, and dynamic containment strategies. It also details the creation of a cross-functional AI Incident Response Team (AIRT) and establishes a data-driven approach to measure performance through Key Performance Indicators (KPIs), transforming the IRP into a proactive management tool.
2. AI Threat Landscape & Taxonomy
A modern, shared vocabulary is essential for classifying AI-specific security incidents. The primary categories are:
- Input and Prompt Manipulation: Exploiting language interfaces through Prompt Injection (direct or indirect) to bypass safety controls.
- Data and Model Integrity Attacks: Corrupting AI systems via Training Data Poisoning to create backdoors or degrade performance.
- Output and Inference Exploitation: Using a model’s output maliciously, such as through Insecure Output Handling, leading to traditional exploits like XSS.
- Unauthorized Model Access & Exfiltration: The theft of proprietary models (Model Theft) or the leakage of sensitive training data (Sensitive Information Disclosure).
- Resource Depletion Attacks: Overwhelming AI systems with computationally expensive queries to cause a Model Denial of Service (DoS).
- Excessive Agency: Exploiting autonomous AI agents, leading to unintended and harmful actions (Rogue AI Agent).
These incidents are mapped to the MITRE ATLAS framework, which provides a catalog of adversarial tactics and techniques, enabling a threat-informed defense.
3. Governance & Risk Management
An effective AI IRP is an extension of a robust governance strategy.
- NIST AI RMF Integration: The IRP operationalizes the NIST framework’s core functions (Govern, Map, Measure, Manage) to structure the approach to AI risk. This includes creating an AI Asset Inventory, defining risk tolerance, and continuously monitoring for threats.
- AI Incident Response Team (AIRT): A cross-functional team is essential, bringing together expertise from Cybersecurity, Data Science, MLOps, Legal, Risk, and Communications. A RACI matrix defines clear roles and responsibilities.
- Regulatory Compliance: The plan addresses stringent reporting requirements from regulations like the EU AI Act and GDPR, which mandate rapid notification for serious incidents (as short as 2-15 days for the EU AI Act). This necessitates automated detection and reporting workflows.
4. AI-Specific Incident Response Lifecycle
The traditional IR lifecycle is adapted for the unique, probabilistic nature of AI systems.
- Phase 1: Preparation: Focuses on proactive defense, including maintaining the AI asset inventory, continuous threat modeling, securing the AI development lifecycle (AI-SDLC), and conducting AI Red Teaming exercises.
- Phase 2: Detection & Analysis: Shifts from signature-based detection to behavioral anomaly detection. This involves monitoring model performance metrics, data drift, and resource consumption. A new discipline of AI forensics is required to preserve model state and other probabilistic evidence.
- Phase 3: Containment, Eradication & Recovery: Employs a Dynamic Containment Strategy Matrix that provides graduated response options beyond a simple on/off switch. Eradication is more complex, often requiring a full, costly model retraining to remove poisoning, which has significant financial and operational implications.
- Phase 4: Post-Incident Activity: Uses an adapted Root Cause Analysis (RCA) framework that accounts for multiple contributing factors in probabilistic systems. A formal stakeholder communication plan ensures timely and compliant notifications.
5. Tactical Playbooks & Performance Metrics
- Tactical Playbooks: The IRP includes detailed, step-by-step playbooks for high-priority incidents, including:
- Prompt Injection and Insecure Output Handling
- Training Data and Model Poisoning
- Model Denial of Service (DoS)
- Sensitive Data Disclosure and Model Theft
- Rogue AI Agent Behavior
- KPIs and Dashboard: The plan’s effectiveness is measured through a robust framework of KPIs, which are visualized on an AI Security Operations Dashboard. Key metrics include:
- Mean Time to Detect (MTTD) AI Anomaly
- Mean Time to Contain (MTTC) AI Incident
- Mean Time to Recover (MTTR) AI Service
- Mean Cost per AI Incident
These metrics transform the IRP from a static document into a dynamic, data-driven tool for managing AI risk and justifying security investments.