AI Incident Response Framework, Version 1.0

Summary

The Coalition for Secure AI's incident response framework fills a critical gap in cybersecurity: how to handle security incidents involving AI systems. Unlike traditional IT incident response that focuses on networks, servers, and applications, this framework tackles the unique challenges of AI deployments—from compromised training data and adversarial attacks to model theft and AI-powered threats. It provides security teams with AI-specific playbooks, detection strategies, and recovery procedures that account for the probabilistic nature of AI systems and their complex attack surfaces.

What makes this different from traditional incident response

Traditional incident response frameworks assume deterministic systems where you can clearly identify "normal" versus "abnormal" behavior. AI systems throw this out the window. A model might produce subtly incorrect outputs due to data poisoning, making incidents harder to detect and scope. This framework addresses AI-specific scenarios like:

• Model extraction attacks where adversaries steal proprietary algorithms through query patterns
• Adversarial inputs designed to fool AI systems into misclassification
• Training data contamination that corrupts model behavior from the ground up
• AI-generated deepfakes and synthetic content used in social engineering
• Prompt injection attacks on large language models and chatbots

The framework also accounts for AI systems' dependency on continuous data feeds and the challenge of maintaining chain of custody for machine learning artifacts during forensic analysis.

Core response playbooks

The framework organizes incident response around five AI-specific playbook categories:

Data Integrity Incidents: Covers scenarios where training or inference data has been compromised, including detection of poisoned datasets, quarantine procedures for suspect data, and model retraining decisions.

Model Security Breaches: Addresses theft of proprietary models, unauthorized access to model parameters, and intellectual property protection during incident containment.

Adversarial Attack Response: Provides step-by-step procedures for identifying and mitigating adversarial inputs, including real-time defense mechanisms and post-incident model hardening.

AI-Enabled Threat Response: Covers incidents where attackers use AI tools against your organization, such as deepfake-based social engineering or AI-generated phishing campaigns.

Supply Chain Compromise: Addresses security incidents involving third-party AI models, pre-trained components, or AI development tools integrated into your systems.

Who this resource is for

• Security operations centers (SOCs) implementing AI-aware incident response procedures
• AI/ML engineering teams who need to understand security implications of their deployments
• Chief Information Security Officers developing AI governance and risk management strategies
• Incident response consultants expanding their expertise to cover AI-related security events
• Compliance teams in regulated industries deploying AI systems with strict security requirements
• DevSecOps teams integrating AI security controls into CI/CD pipelines

Implementation roadmap

Phase 1: Assessment (Weeks 1-2) Inventory your AI systems, classify them by risk level, and map potential attack vectors. The framework includes assessment templates specific to different AI deployment patterns.

Phase 2: Playbook Customization (Weeks 3-4) Adapt the generic playbooks to your specific AI technologies, organizational structure, and regulatory requirements. This includes defining roles, escalation procedures, and communication protocols.

Phase 3: Detection Integration (Weeks 5-8) Implement AI-specific monitoring and detection capabilities. The framework provides guidance on instrumenting AI systems for security visibility without impacting performance.

Phase 4: Training and Testing (Weeks 9-12) Train your incident response team on AI-specific scenarios and conduct tabletop exercises using the framework's sample incident scenarios.

Phase 5: Continuous Improvement (Ongoing) Establish feedback loops to refine playbooks based on emerging AI threats and lessons learned from actual incidents.

Watch out for

The framework assumes a certain level of AI literacy within your security team. Organizations without existing AI expertise may struggle to implement some of the more technical recommendations without additional training or consulting support.

The guidance is necessarily broad to cover multiple AI technologies and deployment patterns. You'll need to invest time customizing the playbooks for your specific use cases—a recommendation to "isolate the affected model" looks very different for an edge AI device versus a cloud-based inference API.

The framework also doesn't address legal and regulatory considerations that vary significantly by jurisdiction and industry. You'll need to layer in compliance requirements for your specific situation.