A Practical Guide to Discovering, Prioritizing, and Enabling AI Agents Securely

Read full article here: https://www.token.security/blog/how-to-discover-prioritize-and-safely-enable-ai-agents/?utm_source=nhimg

Enterprise adoption of agentic AI is moving faster than any previous technology wave. In just months, organizations have gone from experimenting with large language models (LLMs) to deploying AI agents that handle customer support, analyze sales pipelines, optimize cloud costs, and even write production code.

But there’s a blind spot: AI agents are a new hybrid identity type. They behave like humans (flexible, conversational, adaptive) but operate like machines (fast, automated, token-driven). Traditional identity frameworks weren’t designed to secure these autonomous actors.

This playbook outlines four steps security and IAM leaders can take to discover, prioritize, and safely enable AI agents, ensuring innovation doesn’t outpace security.

Step 1: Recognize AI Agents as a New Class of Identity

Historically, enterprises managed two identity categories:

• Human identities — employees, contractors, partners.
• Non-human identities (NHIs) — workloads, APIs, containers, service accounts.

AI agents blur the line. They combine human flexibility (natural language interaction, contextual reasoning) with machine persistence (token-based access, continuous automation).

Example: A GPT-based bot that logs into Salesforce using an API token. Is it a person? A workload? And more importantly — who is accountable?

Risks of unmanaged agents include:

• Privilege escalation across environments.
• Orphaned accounts after employees leave.
• Data exposure at scale through uncontrolled automation.

The first step is treating AI agents as a distinct identity type with their own lifecycle: onboarding, monitoring, and offboarding.

Step 2: Hunt the Invisible — Discover AI Agents in Your Environment

AI agents often enter organizations informally: a developer installs an LLM library, a marketer spins up a custom GPT, or a team embeds an AI copilot into SaaS. They rarely appear in IAM dashboards but leave digital breadcrumbs.

Discovery Hotspots:

• Naming & tagging: Search for “llm-”, “agent-”, “vector-” in IAM roles or resources.
• Secrets vaults: Monitor API key usage for OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, or Google Vertex.
• Cloud AI services: Audit logs from managed AI platforms reveal the majority of traffic.
• Code repositories: Scan for AI SDK imports, IaC/Terraform modules provisioning AI resources.
• Runtime telemetry: API/audit logs, network traces, and system call activity showing LLM usage.

Automated scans must be paired with human intelligence — architecture reviews, team surveys, and security questionnaires help surface shadow AI projects before they spread.

The outcome isn’t just a list — it’s a map of AI usage tied to resources, human owners, and business context.

Step 3: From Chaos to Clarity — Prioritize AI Agents by Risk and Impact

Discovery often uncovers dozens, even hundreds, of agents. To manage them effectively, prioritize using three filters:

1. Access Sensitivity — Does the agent touch customer data, production systems, or regulated workloads?
2. Ownership Linkage — Is there a named human responsible? Orphaned agents should rise to the top.
3. Risk Scoring — Red flags include:
   • Overly broad permissions.
   • Cross-environment access (dev → prod).
   • Hardcoded or widely shared credentials.

 Examples of high-risk agents:

• A Salesforce GPT with org-wide admin rights.
• A support bot running unlogged database queries.
• AI agents tied to ex-employees but still active.

Prioritization ensures limited security resources address the most dangerous exposures first.

Step 4: Build Safe Adoption with Identity-First Controls

Blocking AI may feel safer — but it’s not realistic. Business units will adopt AI anyway, often introducing more risk in the process. The real opportunity is to become enablers of safe AI adoption by embedding guardrails from the start.

Key Practices for AI Identity Security:

• Formal identities: Assign every AI agent a unique identity.
• Clear ownership: Require a human accountable for each agent.
• Guardrails: Enforce least privilege, rotate secrets, and monitor credential usage.
• Expiration policies: Prevent agents from persisting indefinitely.
• Approved catalogs: Offer pre-vetted AI tools and integrations for safe use.

When security teams provide safe pathways, adoption accelerates in a controlled and governed way.

Riding the AI Identity Wave

AI agents aren’t a future risk — they’re here now, operating across engineering, sales, support, HR, and finance. The organizations that thrive will be those that:

1. Recognize AI agents as a hybrid identity type.
2. Discover them across environments.
3. Prioritize based on sensitivity and ownership.
4. Enable safe adoption with identity-first controls.

Security leaders face a choice: be the team that blocks innovation, or the team that makes it safe. In the era of agentic AI, those who choose enablement will define the leaders.

At Token Security, we help enterprises adopt AI safely and securely, delivering full visibility, governance, and control over AI agents — so innovation moves faster without compromising trust.