As artificial intelligence moves from generating information to making decisions and taking actions, AI integrity becomes a foundational requirement. At its simplest, AI integrity is the degree to which an AI system remains faithful to its intended objectives, constraints, permissions, and operating principles. It is not only a question of whether the system produces accurate outputs, but whether it continues to behave within the boundaries established for it as its environment, information, and circumstances change.
This distinction becomes especially important with agentic AI. An autonomous agent may interpret goals, retrieve information, maintain memory, use tools, communicate with other agents, and act on external systems. Integrity therefore has to extend across the entire chain from information and reasoning to authority and action. The integrity of the model matters, but so do the integrity of its memory, identity, instructions, permissions, tools, and communications. A system may be functioning exactly as designed at one layer while having been compromised or redirected at another.
AI integrity is therefore related to, but distinct from, AI safety and AI security. Safety asks whether a system can operate without creating unacceptable harm. Security asks whether it can resist attack or unauthorized access. Integrity asks whether the system remains what it is supposed to be and does what it is authorized to do. As AI gains greater autonomy, this becomes a continuous rather than static property. The important question is no longer simply whether we can trust an AI system's answer, but whether we can trust the system to remain within the boundaries of what it was meant to know, decide, and do.