Defending AI Systems: A New Framework for Incident Response in the Age of Intelligent Technology

The rapid adoption of artificial intelligence is transforming how organizations operate, but it’s also creating an entirely new battlefield for cybersecurity teams. As AI systems progress through increasing levels of agentic sophistication, from perceptively autonomous assistants, to reactively autonomous agents that observe independently but act under human direction, to partially autonomous systems that operate independently while seeking approval for critical decisions, and finally to fully autonomous agents requiring no human supervision, they’re introducing security challenges that traditional incident response playbooks simply weren’t designed to handle.

The Coalition for Secure AI (CoSAI) has just released a comprehensive framework that addresses this critical gap: the AI Incident Response Framework, Version 1.0. This document provides security teams with the tools, knowledge, and structured approaches they need to protect AI systems from emerging threats.

Why Traditional Security Approaches Fall Short

Think about how you’d respond to a typical data breach: you’d isolate compromised systems, analyze logs, patch vulnerabilities, and restore from backups. But what happens when an attacker doesn’t break into your system—they simply ask your AI chatbot the right questions?

AI systems face unique threats that don’t fit neatly into conventional security categories:

• Prompt injection attacks that manipulate AI behavior through cleverly crafted input
• Memory poisoning that corrupts an AI agent’s long-term context
• Context poisoning where attackers inject malicious content into the knowledge bases AI systems reference
• Model extraction attempts that steal proprietary AI capabilities
• Jailbreaking techniques that bypass safety guardrails

These aren’t theoretical concerns. The framework documents real-world case studies, including attacks on major financial institutions and demonstrations of how AI systems can be systematically compromised without ever touching the underlying infrastructure.

A Practical Approach to AI Security

The CoSAI framework doesn’t just identify problems, it provides actionable solutions organized around the NIST incident response lifecycle, adapted specifically for AI systems:

1. Preparation
   Before incidents occur, organizations need to inventory their AI assets, establish specialized response teams, and implement comprehensive monitoring. The framework emphasizes the importance of capturing AI-specific telemetry: prompt logs, model inference activity, tool executions, and memory state changes.
   
2. Detection and Analysis
   AI incidents often look different from traditional security events. The framework provides detailed guidance on monitoring for anomalies like unexpected model drift, suspicious prompt patterns, and unusual retrieval behaviors in RAG systems.
   
3. Containment, Eradication, and Recovery
   When an AI system is compromised, response teams need specialized containment strategies. Should you roll back to a previous model version? Purge poisoned memory? Rebuild your vector database? The framework offers architecture-specific guidance for different AI system types.
   
4. Post-Incident Activities
   Learning from AI incidents requires understanding both technical root causes and how to prevent similar attacks across different AI architectures. The framework emphasizes building organizational knowledge that improves long-term AI security posture.
   

Ready-to-Use Playbooks

One of the framework’s most valuable contributions is its library of incident response playbooks written in the OASIS Collaborative Automated Course of Action Operations (CACAO) standard. These aren’t abstract guidelines, they’re detailed, actionable workflows for responding to specific AI attacks:

• Detecting training data poisoning
• Responding to multi-channel prompt injection
• Handling memory injection attacks (MINJA)
• Mitigating RAG poisoning
• Addressing cloud credential abuse through SSRF vulnerabilities

Each playbook includes detection methods, triage criteria, containment steps, and recovery procedures, making them immediately usable by security operations teams.

Understanding AI Architecture Vulnerabilities

The framework includes comprehensive technical references covering five common AI architecture patterns, from basic LLM applications to complex agentic RAG systems. For each pattern, it maps:

• Specific components and their functions
• MITRE ATLAS techniques that apply to each component
• Targeted mitigation strategies
• Real-world attack scenarios

This architectural approach helps security teams understand not just what to protect, but how different AI system designs create different security challenges.

Who Should Read This?

The framework is designed for diverse audiences:

• CISOs and security leaders will find executive summaries, regulatory guidance, and organizational frameworks
• Security analysts and incident responders get detailed detection methods, forensic guidance, and response workflows
• AI/ML engineers receive architecture-specific security considerations and monitoring requirements
• Compliance officers find regulatory communication templates and information-sharing guidelines

The Path Forward

As AI systems become more autonomous and integrated into critical business processes, the security stakes continue to rise. An AI system that makes incorrect recommendations, leaks sensitive data, or takes unauthorized actions can have immediate and severe business impact.

The AI Incident Response Framework represents a critical step toward mature, systematic AI security practices. It acknowledges that AI security isn’t just about protecting models, it’s about securing entire ecosystems of data pipelines, retrieval systems, agent workflows, and human interactions.

Ready to strengthen your organization’s AI security posture? Download the full AI Incident Response Framework from the Coalition for Secure AI.

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The AI Incident Response Framework, V1.0 is an open-source project from the Coalition for Secure AI (CoSAI), an OASIS Open Project bringing together AI and security experts from industry-leading organizations. Learn more at CoSAI GitHub.