AI agent security is moving through a familiar maturity curve: adoption, then visibility, and finally, control. But what we've collectively discovered is that enforcing least privilege for AI agents is harder than we ever imagined. This is why there are so many approaches, from prompt filtering to identity-layer access controls. Where we've collectively landed is that understanding the intent of AI agents is essential to securing them. It's not easy, but it's the only path forward.
Organizations approach this challenge with different levels of sophistication. For many, the current goal is simply to find the AI agents already operating across the business. That is a necessary first step. AI agents are appearing in SaaS platforms, developer environments, cloud workflows, customer support systems, productivity tools, and internal applications. Some are sanctioned, and others are not.
But discovery alone isn't useful. AI agents are not passive; they reason, plan, call tools, invoke APIs, access data, and take action without a human in the loop. The risk is not that an organization has too many agents. The risk is that those agents can operate across systems without consistent identity, intent, ownership, and enforcement. There are also different types of AI agents, each requiring a different approach to securing them.
