A Practical Incident-Response Framework for Generative AI Systems

Generative Artificial Intelligence (GenAI) systems have introduced new classes of security incidents that traditional response frameworks were not designed to manage, ranging from model manipulation and data exfiltration to misinformation cascades and prompt-based privilege escalation. This study proposes a Practical Incident-Response Framework for Generative AI Systems (GenAI-IRF) that bridges established cybersecurity standards with emerging AI assurance principles. Using a Design Science Research (DSR) approach, this study identifies six recurrent incident archetypes and formalises a structured playbook aligned with NIST SP 800-61r3, NIST AI 600-1, MITRE ATLAS, and OWASP LLM Top-10. The artefact was evaluated in controlled scenarios using scenario-based simulations and expert reviews involving AI-security practitioners from academia, finance, and technology sectors. The results suggest high inter-rater reliability (κ = 0.88), strong usability (SUS = 86.4), and improved incident resolution times compared to baseline procedures. The findings demonstrate how traditional response models can be adapted to GenAI contexts using taxonomy-driven analysis, artefact-centred validation, and practitioner feedback. This framework provides a practical foundation for security teams seeking to operationalise AI incident response and contributes to the emerging body of work on trustworthy and resilient AI systems.