How to Discover, Prioritize, and Safely Enable AI Agents

The Agentic AI Identity Security Playbook

The adoption of artificial intelligence inside enterprises is moving faster than any previous wave of technology. Within months, organizations went from cautious experimentation with large language models to deploying AI agents that drive customer support, analyze sales pipelines, and even write production code.

But this rapid adoption has created a blind spot: AI agents represent a new, hybrid identity type. They act like humans in their flexibility, but they behave like machines in their speed, scale, and automation. Traditional identity frameworks are not equipped to manage these autonomous agents.

This playbook provides security and identity teams with four steps to discover, prioritize, and safely enable AI agents in the enterprise:

1. Recognizing AI agent identities as a distinct class
2. Discovering where they live and how they operate
3. Prioritizing them for risk and business impact
4. Building identity-first controls that enable safe adoption and secure operation

Step 1: Recognize AI Agent Identities as a New Class

For decades, identity was a two-category system: human identities (employees, contractors) and machine or non-human identities (services, workloads, containers, APIs, etc.). The Non-Human Mgmt Group recently conducted a poll on whether agentic ai Identities are just non-human identities (NHIs) or a new breed of identity and the results were interesting:

• 57% said they are just NHIs
• 43% said they are a new breed of identity

If AI agents are new type of identity, it’s because they are breaking the current NHI model by combining:

• Human flexibility — natural language interaction, contextual queries, dynamic usage
• Machine robustness — token-based access, automation, persistence

This hybrid identity creates ambiguity. When a GPT-based bot accesses Salesforce using a personal API token, is it acting as a person or a service account? More importantly, who is accountable?

Unmanaged AI agents can:

• Escalate privileges across environments
• Persist as orphaned accounts after employees leave
• Expose sensitive data at scale

The first step is acknowledging AI agents as a new identity type requiring full lifecycle management: onboarding, monitoring, and offboarding.

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

You can’t protect what you can’t see. AI agents often enter organizations informally: a developer adds an LLM library, a marketer spins up a custom GPT, or a team integrates an AI copilot into a SaaS app.

They don’t show up in IAM reports, but they leave signals. Focus on these discovery hotspots:

1. Naming & Tagging Patterns: Search for “LLM,” “GPT,” “agent,” or “vector” in resource names
2. Secrets Vaults: Monitor API key usage for OpenAI, Anthropic, AWS Bedrock, Azure OpenAI Studio, Google Vertex, and others
3. Managed AI Services: Collect logs from AI providers; this reveals ~90% of AI traffic
4. Code Repositories and Pipelines: Scan for AI SDK imports and Terraform/IaC templates provisioning AI resources
5. Runtime Telemetry: Watch for calls to AI APIs in audit and monitoring logs

Automated scans must be paired with human intelligence. Architecture reviews, security questionnaires, and team surveys surface AI adoption early, before agents become entrenched.

The outcome of this stage isn’t just a list. It’s a map of AI usage, tying each agent to resources, human owners, and risk context.

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

Discovery often reveals dozens, even hundreds, of AI agents. Without prioritization, managing them and mitigating risk becomes overwhelming.

Use three filters to focus your efforts:

1. Access Sensitivity: Does the agent touch production systems, customer data, or regulated information?
2. Ownership Linkage: Is there a named human responsible? Orphaned agents should be top of the queue to remediate
3. Risk Scoring: Flag agents with:
   
   • Overly-broad permissions
   • Cross-environment access (i.e., dev → prod)
   • Widely shared or hard-coded credentials.

Red Flags to Act On Immediately:

• A Salesforce GPT with organization-wide admin rights
• A customer support bot with unlogged database queries
• AI agents tied to departed employees but still active

Prioritization ensures your limited resources go toward addressing the agents with the greatest potential for harm.

Step 4: Security as an Enabler — Building Safe AI Adoption Through Identity-First Controls

Blocking AI adoption outright might seem tempting, but it’s not a practical strategy, as organizations must innovate to gain operational efficiencies and competitive advantages. Because of the rapid adoption of AI, business units will find workarounds that will introduce new risks. The real opportunity is to enable safe adoption and promote AI innovation by embedding identity-first controls from the start.

Key practices include:

• Formal Identities: Assign every AI agent a unique identity
• Clear Ownership: Require a human responsible for every agent
• Guardrails: Enforce least privilege, introduce permission right-sizing, monitor credential use, and manage secrets regularly
• Expiration Policies: Ensure AI agents don’t persist indefinitely
• Approved Catalogs: Offer business units pre-vetted AI tools and integrations

When security teams provide a safe pathway, adoption accelerates in a controlled manner. Instead of being blockers, security becomes a trusted partner in innovation.

Riding the AI Identity Wave

The rise of AI agents is not a future risk; it's happening now. They’re already embedded in workflows across all enterprise functions, including engineering, marketing, sales, support, HR, finance, and more.

This playbook lays out a path forward:

1. Recognize AI agents as a hybrid identity type
2. Discover them through automated methods
3. Prioritize based on sensitivity, ownership, and risk
4. Enable safe adoption with identity-first guardrails

Organizations that move quickly will reduce risk while accelerating their AI strategies. Those that wait risk being blindsided by invisible agents with access to their most sensitive systems. Security has a choice: be the team that blocks innovation or be the team that makes it safe. In the era of agentic AI, the latter will define the leaders.

Token Security helps you embrace AI safely and securely without delaying innovation. By delivering complete visibility, governance and control of AI agents, Token Security empowers enterprises to adopt AI at scale with confidence. To learn more, request a demo of the Token Security platform: https://www.token.security/book-a-demo.