The landscape of AI security is evolving rapidly, with a reported increase of over 56% in AI-related incidents year-over-year. By 2026, organizations must prioritize the development of robust security frameworks for AI, as reliance on these technologies continues to grow. Attackers are increasingly targeting various components, including models, data, and workflows, necessitating comprehensive protective measures.
Unlike conventional cybersecurity, AI security involves safeguarding a wider range of elements such as training data, model artifacts, and interactions between humans and AI systems. The complexity of these systems demands not only standard security protocols like identity verification but also tailored controls that focus on model governance and prompt defense strategies.
To enhance incident response and threat modeling, establishing an inventory of AI assets is essential. This inventory should encompass models, datasets, tools, endpoints, and third-party services. Utilizing a NIST-style mapping approach can ensure that this inventory remains dynamic and up-to-date.
The risks associated with the AI supply chain are significant, as vulnerabilities in one component can impact the entire system. As AI agents gain autonomy, they introduce new risks akin to insider threats, highlighting the need for organizations to implement stringent security measures, including real-time monitoring and governance of tools.