Written by Ido Shlomo, Co-Founder and CTO, Token Security
AI assistants are no longer summarizing meeting notes, writing emails, and answering questions. They’re taking action, such as opening tickets, analyzing logs, managing accounts, and even automatically fixing incidents.
Welcome to the age of agentic AI, which doesn’t just tell you what to do next – it does it for you. These agents are incredibly powerful, but they’re also introducing an entirely new kind of security risk.
The Quiet Rise of Autonomous Agents
Initially, AI adoption within companies seemed benign. Tools like ChatGPT and Copilot assisted people with basic writing and coding, but didn’t act independently. That’s changing quickly.
Without security reviews or approval, teams are deploying autonomous AI systems that can interpret goals, plan steps, call APIs, and invoke other agents. An AI marketing assistant can now analyze campaign performance data and actively optimize targeting and budget. A DevOps agent can scan for incidents and start remediation without waiting for a human.
The result? A growing class of agents that make decisions and take actions faster than people can monitor them.
It’s Not “Just Another Bot”
While organizations have started managing Non-Human Identities (NHIs), such as service accounts and API keys, agentic AI doesn’t fit this same mold.
Unlike a workflow, which follows a predictable series of actions, an AI agent reasons about what to do next. It’s capable of chaining multiple steps together, accessing different systems, and adjusting its plan along the way. That flexibility is what makes agents both powerful and dangerous. Because agents can act across boundaries, the simple act of giving them access to a database, a CRM, and Slack could make them among the most powerful users in the company.
Multi-agent ecosystems are introducing new levels of complexity. Once an agent starts calling or even creating other agents, the ability to trace an action back to the human who initiated it starts to blur.
AI agents are now taking action—not just following instructions.
Learn how Token Security is helping enterprises redefine access control for the age of Agentic AI, where actions, intent, and accountability must align.
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Shadow AI Is Already Here
Even cautious companies are discovering shadow AI creeping into their environments. A product manager signs up for a new AI research tool. A team connects a meeting bot to internal drives. An engineer spins up a local AI assistant that can query customer logs.
Each one is technically a service and therefore, each one needs governance. But most of these tools enter the enterprise without a formal review, security scan, or identity record.
Traditional visibility tools don’t see them clearly. CASB tools might flag a new SaaS domain, but they won’t catch a few hundred AI agents quietly running on cloud functions or VMs.
It’s not malicious; it’s just fast. And speed has always been the enemy of oversight.
New Rules for a New Type of Identity
So, how do you secure something that you may not have visibility into and is operating at machine speed? Security teams need to adapt their identity strategies in new ways:
- Track ownership and lifecycles. Every agent needs a named owner. When the human leaves, the agent should too.
- Apply intent and context. Every agent action should carry “on behalf of” data: who triggered it, what task it’s fulfilling, and what data it’s entitled to touch. Lose that chain, and you lose accountability.
- Default to read-only permissions. Agents should start with view access only. Write privileges must be explicitly approved and time-limited.
The Lifecycle Problem
Most companies don’t have a clean process to retire AI agents when they’re no longer needed. A developer prototype that started as an experiment in March is still running in October, using credentials created by someone who is no longer with the company. Another agent quietly evolved through prompt and tool changes until it now has access to customer data. While these agents aren’t malicious, they’re invisible, persistent, and powerful.
That’s why more enterprises are creating AI agent inventories that list every active agent, its purpose, owner, permissions, and lifespan. It’s the groundwork needed to make AI agents and their identities manageable.
Guardrails Over Fear
The goal isn’t to stop agents from working as your organization looks to AI to gain efficiencies and competitive advantages. It’s to make sure they have effective oversight and governance.
Just as organizations don’t grant a new hire admin access to everything, they need to give AI agents specific responsibilities, review their work, and check their decisions.
The key is governance to enable teams to build systems that automatically limit scope, log behavior, and shut down rogue processes before they cause harm. Because, these agents aren’t just summarizing reports or triaging tickets. They are closing incidents, approving transactions, and interacting directly with customers.
When that happens, “shadow AI” won’t be a curiosity, it will be a crisis.
The Takeaway
Agentic AI isn’t a future problem. It’s already in your stack. If you’re still managing identities as either human or non-human, it’s time to make room for a third category: autonomous actors. They need identity, permissions, and accountability.
They also need control and governancem, and the sooner we treat agents like coworkers with superpowers, and not scripts with credentials, the safer the enterprise will be.
See Token Security’s AI Security Guide for more best practices from 14+ cybersec industry leaders.
Sponsored and written by Token Security.
