AI Agent Identity Management: How to Discover and Secure Autonomous Systems

Read full article here: https://www.token.security/blog/unmasking-ai-agents-how-to-discover-and-manage-identity-in-the-age-of-autonomous-systems/?utm_source=nhimg

Enterprises are embracing generative AI at full speed but lurking behind every deployment is a new, often invisible identity challenge: AI agents. These autonomous systems act on behalf of humans, access sensitive resources, and interact programmatically with APIs, tokens, and OAuth integrations. While they supercharge productivity, they also create shadow IT, security blind spots, and governance gaps.

At Token Security, we’ve developed a practical methodology to help security, IAM, and IT leaders discover, classify, and manage AI agent identities before they spiral out of control.

Why AI Agents Represent a New Identity Class

For years, digital identities fell neatly into two buckets:

• Human identities — flexible, role-based, and small in number.
• Workload identities — rigid, task-based, and designed for containers, scripts, or services.

AI agents blur these boundaries. They interact like humans but are implemented programmatically. Examples include:

• Cloud optimizers that right-size AWS or Azure resources.
• Custom GPTs integrated with Salesforce or Google Drive.

Because they’re powered by tokens, service accounts, and OAuth, AI agents are harder to track, govern, and secure, making discovery an urgent priority.

Why Discovery Can’t Wait

Unmonitored AI agents create risks such as:

• Shadow IT — agents deployed without IT oversight.
• Over-permissioned access — credentials with more power than necessary.
• Orphaned identities — agents with no clear owner, still active in production.

Without visibility, enterprises face compliance gaps and security debt. With visibility, identity teams can become enablers of secure AI adoption.

A Practical Methodology for AI Agent Discovery

Our approach breaks discovery into three key stages:

1- Discovery Inputs (Where to Look)

Collect data across your ecosystem, including:

• Naming patterns (llm-, agent-, vector- in IAM roles).
• Secrets vaults where tokens are stored.
• Cloud AI inventories such as managed AI services and vector DBs.
• Code repositories for SDK imports or Terraform IaC with AI credentials.
• Runtime telemetry — audit logs, API traces, and network flow logs.
• AI provider APIs like Anthropic’s compliance APIs.

2- Discovery Techniques (How to Find Agents)

Use a mix of:

• API queries across SaaS and cloud platforms.
• Static scans for AI libraries or IaC provisioning.
• Runtime log analysis for AI-related activity.
• Human intelligence — surfacing shadow AI projects.
• AI platform research to identify orchestrators and brokers.

3- Correlation & Risk Context (How to Prioritize)

Not all agents pose equal risk. Prioritize by:

• Linking agents to crown jewels (customer data, production systems).
• Tying agents to human owners or business units.
• Applying risk scoring for over-permissioned or orphaned agents.

From Discovery to Governance: A Lifecycle for AI Agents

Once identified, AI agents should follow a governed lifecycle like any other identity:

1. Registration — tag, classify, and assign ownership.
2. Provisioning — apply least-privilege access policies.
3. Runtime enforcement — monitor activity with guardrails.
4. Decommissioning — retire or revoke access when no longer needed.

The faster organizations adopt this lifecycle mindset, the sooner they can enable AI innovation without trading away security or compliance.

The Bottom Line

AI agents aren’t just automation scripts. They are a new class of digital identity that must be discovered, governed, and audited. By applying structured discovery and lifecycle management, enterprises can turn chaos into control — and scale AI adoption with confidence.