Guest Author: Ariel Zommer, Staff Product Marketing Manager, Security, Okta
TL;DR:
AI agents are reshaping the enterprise in a way that feels familiar, yet entirely new.
Like cloud and SaaS, artificial intelligence (AI) agents promise speed, scale and new ways to get work done. But unlike earlier technology shifts, AI agents are spreading faster, behaving less predictably and often operating without the identity foundation organizations use to secure people, applications and services.
That matters because when an agent has too much access, stale permissions or unmanaged credentials, the impact can move quickly across every system it touches.
The goal is not to slow AI innovation. It is to make sure AI agents have the same accountability and controls organizations already expect from human users.
Security teams have spent decades building identity programs for employees, contractors, privileged users and applications. AI agents are now testing those programs at record speed.
We have already started to see this show up in public breach reports. In one recent incident involving a developer platform, the issue was not a software vulnerability or infrastructure flaw. The attack path came through an OAuth connection between an employee’s corporate account and a third-party AI tool. Once that tool was compromised, pre-granted trust created a path into internal systems, including API keys, tokens and environment variables.
That was not just an AI problem. It was an identity problem: who or what had access, what that access allowed and how long that trust remained in place.
Securing the agentic enterprise requires answering three foundational questions:
These questions sound simple, but many organizations cannot answer them consistently today. Agents may come from internal development teams, SaaS platforms, cloud services, automation tools or employee-authorized AI applications. Some are approved. Some are experimental. Some are invisible to security teams – also known as shadow AI.
That is why identity needs to become the control plane for AI agents. It starts with: where are my agents?
Agent sprawl is moving faster than any identity problem security teams have faced before. Developers can spin up dozens of agents in a single day, many of which bypass IT entirely.
If you skip the basics now, every agent shipped without a defined owner or a properly scoped credential becomes technical debt: hardcoded API keys, shared secrets and orphaned agents that get harder and more expensive to clean up the longer they sit. That’s why it is essential to put controls in place now to discover agents across your environment so that you can bring them under your control.
Once you discover these agents, the next critical step is to register them in a centralized directory alongside your users. This means giving each agent a unique identity and defining a clear owner, business purpose and lifecycle state.
But it doesn’t stop there. An agent with an owner and a purpose can still be a risk if it has unchecked access to your systems.
Once you know where agents are, you need to understand what those agents can reach.
Many agent risks come from standing access, hardcoded credentials, over-permissioned service accounts or tokens that live longer than the task requires.
These are not edge cases. 53% of public MCP servers use static secrets and only 8.5% implement OAuth (Stanford SACR, March 2026).. The pattern is consistent: access that should expire never does and the risk increases with every agent rollout.
A stronger model uses managed, least-privilege connections. That means issuing scoped, short-lived access only for what the agent needs and only for as long as it needs it.
The blast radius of a compromised agent is defined by its connections, which now span internal APIs, MCP servers, SaaS platforms, service accounts, vaulted secrets and increasingly other agents.
Agent-to-agent connections are a familiar problem: service-to-service communication. Only much worse. Agents initiate connections autonomously, so accountability gets harder to trace with every hop.
We’ve already seen what this looks like when it goes wrong. A compromised OAuth connection between a corporate account and a third-party AI tool can create a path into internal systems, including API keys, tokens and environment variables. The key weakness here: trust had been granted and never scoped down or rescinded.
The principle is straightforward: every connection an agent uses is specific, policy-driven and revocable.
Visibility and access control are only part of the full picture. Organizations also need to govern what agents can do with that access over time.
That includes request and approval workflows, recurring access certifications, centralized audit logs and a reliable way to deactivate agents that behave unexpectedly.
When an agent is compromised or starts acting outside its intended purpose, security teams need the ability to terminate it in one action, across every connected system it touched, not chase down access one system at a time.
Organizations need reliable logs. Agent activity can look like normal automation, until it doesn’t. Capturing tool calls, authorization decisions, access attempts and behavior signals gives security teams the telemetry they need to investigate issues, support audits and respond faster. Because every action is tied to an agent identity, those logs are attributable, auditable and actionable.
For high-stakes actions, organizations should also consider human-in-the-loop controls. Not every agent action needs human approval, but sensitive workflows, such as changing production environments, accessing regulated data or initiating financial actions, warrant stronger oversight. You need to define exactly where that threshold sits and enforce it consistently.
AI agent security should not depend on a single cloud, platform or framework. Agents will operate across ecosystems and identity controls need to follow them.
That is why organizations evaluating their approach should prioritize three things: vendor neutrality, full lifecycle coverage and compatibility with what they already have in place.
For example, look for a solution that gives every AI agent a first-class identity, no matter which agent framework, cloud environment or SaaS platform it runs on. Make sure you can register and manage homegrown agents, import known agents from external platforms and surface shadow AI agents through signals such as OAuth consent grants.
From there, you can govern connections to internal APIs, secrets, service accounts, applications and MCP servers with scoped, short-lived access.
And then, evaluate agent actions at runtime against policy, with human-in-the-loop approval for high-stakes actions, centralized audit logs to support review and a kill switch to cut off an agent’s access the moment something goes wrong.
The organizations that lead in AI will not simply be the ones that deploy the most agents. They will be the ones that govern them more responsibly.
A practical starting point is to build an AI agent identity inventory, map agent access to sensitive systems and define lifecycle controls for approval, review, monitoring and deactivation. Dive deeper into how Okta helps you discover, onboard, protect and govern AI agents across your environment.
AI agents are moving quickly. Identity security needs to move with them.
Ready to bring your AI agents under control? GuidePoint Security works with organizations to assess identity maturity, evaluate AI-related access risk and build security programs that align with business goals. Together, GuidePoint Security and Okta help organizations establish an identity foundation that supports AI adoption without losing control of access.