Rethinking identity security in the age of autonomous AI agents

The rise of autonomous AI agents is challenging the very foundation of enterprise security. These systems don’t just follow static workflows or code. They make independent decisions, take actions across systems, and in many cases, do so without human oversight.

For CISOs, this shift introduces a new and urgent category of non-human identities (NHIs) that traditional human-focused identity models, controls, and monitoring frameworks aren’t equipped to govern.

The Emerging Technical Risks of AI Agents

Shadow Agents: Unlike employees, AI agents rarely go through formal onboarding or offboarding. This is leading to agent sprawl and shadow AI deployments. Many agents persist long after their use case has ended, still holding credentials, active tokens, or connections to critical systems and applications. These agents become attractive to attackers and a growing governance blind spot due to the excessive permissions they hold.

Privilege Escalation: Agents often operate with over-privileged permissions. This gives them broader access than necessary, and in some cases, the ability to chain their privileges to full admin permissions. Attackers can exploit these gaps by hijacking agents or feeding them instructions to invoke unauthorized actions via legitimate APIs, creating breaches that appear “trusted” in the logs.

Data Exfiltration: AI agents can aggregate and transmit sensitive data at scale. If compromised or even just poorly scoped, an AI agent with an API token or a SaaS integration can leak internal data to either its users (customers, employees, or other agents) or to third-party endpoints without triggering alerts. Subtle prompt manipulations or agent-to-agent message chaining can be used to extract proprietary datasets and intellectual property, and many security tools still fail to flag these as anomalies. Not only is this a massive security risk, but it is also a potential compliance failure for the organization.

Explore how these and other vulnerabilities fit into the broader risk landscape in our overview of the top 10 security risks of autonomous AI agents.

Why Traditional Security Tools Fall Short

Legacy security tools assume human intent and interactions. They verify users using biometrics, monitor sessions, and look for deviations from expected patterns.

But, agentic AI operates in unfamiliar ways. It spawns sub-agents, invokes new API calls on the fly, and self-reasons based on evolving objectives. Its behavior doesn’t match baseline human or a static script activity, and so it often confuses detection tools.

Worse, many AI agents operate without clear human ownership. In multi-agent workflows, the initiating identity is quickly lost as actions propagate across tools.

The result is a sprawling web of activity with no centralized control or traceability. Audit logs can’t answer “who did this?” when the “who” is an autonomous, ephemeral agentic process.

Identity-First Security: The Needed Shift

For security leaders, the only viable path forward is identity-first security for AI agents.

That means that every agent must have a unique, managed identity, its permissions must be scoped tightly to the task at hand, and its lifecycle must be properly managed.

Without identity at the center, all other controls fail. You can’t enforce least privilege, detect anomalies, or assign accountability if you don’t know who an agent is owned by and what it’s supposed to be doing.

What CISOs Can Do Now

To keep agentic AI from spiraling out of control, CISOs should take immediate action:

1. Discover and Inventory Agents: Begin by identifying every autonomous agent operating in your environment—chatbots, API connectors, internal copilots, MCP servers, and any AutoGPT-like tools. Catalog where they run, what they access, and who created them.
2. Assign Ownership: Require that each agent has a designated human owner responsible for its purpose, access, and lifecycle. Unowned agents should be flagged and terminated.
3. Enforce Least Privilege: Review agent permissions regularly. Avoid giving blanket or inherited access. Set expiration policies for tokens and automate privilege reviews just as you would for privileged user accounts.
4. Propagate Identity Context: Ensure that identity flows through every step of a multi-agent chain. If Agent A invokes Agent B, permissions should be constrained to the original user’s context. Without identity binding, every agent becomes a potential superuser.
5. Monitor and Audit Agent Behavior: Treat agents as high-risk entities in your SIEM. Look for anomalies such as unexpected API calls, new integration attempts, or changes in data access patterns. Use immutable logs and establish security guardrails.
6. Establish a Kill Switch: Agents that misbehave must be terminated quickly. Build emergency response processes specifically for autonomous actors and rotate secrets that may have been compromised.
7. Integrate Agents into IAM Systems: Bring AI agents into your identity fabric. Assign them roles, issue credentials from secure vaults, and apply existing policy controls where applicable.

Prepare Now or Lose Control Later

The biggest risk with agentic AI isn’t a specific exploit. It’s the illusion of safety. These agents often run inside trusted applications, using familiar credentials, and perform tasks that look benign on the surface.

But without visibility, scope, or ownership, they’re likely to become entry points for lateral movement, data theft, or system manipulation.

As AI becomes embedded in more enterprise workflows, the sprawl of ungoverned agents will accelerate.

Security leaders who act now by placing identity, visibility, and access governance at the core of AI adoption will be positioned to harness the benefits of agentic AI without sacrificing control.

To see how this is being achieved in practice, book a demo with Token Security.

Sponsored and written by Token Security.