Short answer. Most enterprises treat AI agents like other non-human identities, relying on the identity and AI platform controls they already have. Those controls were built for software that runs the same job every time. AI agents are non-deterministic, so the same access can lead to different actions. Without a check on each action, security teams tend to be cautious and limit agents to low-risk work, which slows adoption.
Teams often secure agents in roughly the same order. Here's where each step stands and what goes wrong without it.
Many enterprises start by finding the agents they already run
Agents run in AI platforms, inside SaaS apps and in tools employees connect on their own. A list built by hand goes out of date quickly, so teams increasingly get visibility from agent activity itself: which agents are running, which tools they call and who they act for. Cost by team or person can also show where agents are used most.
Giving each agent an owner and its own identity is often the next step
Shared credentials hide which agent acted, and revoking one stops every agent that uses it.
A stronger setup gives each agent an owner and its own identity, linked to the delegating user. The agent holds no standing credentials and gets access per request, so no long-lived keys sit in its configuration.
Checks on each request can let agents take on higher-risk work
Access policies and reviews decide what an agent can reach. They typically don't see what it does on each request, including actions prompted by instructions hidden in an email or web page. In VentureBeat's June 2026 survey, 49% of enterprises enforce agent permissions at runtime. Teams that do often run several checks before a tool call goes through.
These checks can give security teams the confidence to approve agents for sensitive work.
Audit evidence is a step many teams are still working toward
A useful record names the agent, the delegating user, the tool and the decision for each request. Sending those records to a SIEM through webhooks keeps agent activity alongside the rest of the security data.
Frequently Asked Questions
What is agentic AI security?
Agentic AI security is the practice of controlling what AI agents can do in company systems, from the identity each agent uses to the decision on each request. It can let companies connect agents to real systems and data with limits they can enforce.
Should AI agents get a separate security program?
Agents can stay in an existing identity program, but it may not cover the riskiest part: what an agent does with its access on each request. That takes a layer built for agents, with real-time authorization, a link to the delegating user and no standing credentials.
What is agentic AI identity?
Agentic AI identity is the identity an AI agent uses to act, linked to the person it acts for. Without it, agents often run on shared keys or borrowed user tokens, so a misled agent can act with broad access and logs often can't show which agent acted or for whom. Carrying both identities on each request lets access be checked in real time and leaves a clear record.