Discovering and Securing AI Identities: A Practical Guide for Modern Enterprises

Read full article here: https://www.oasis.security/blog/ai-identities-visibility/?utm_source=nhimg

AI identity security is becoming one of the most urgent priorities for CISOs, IAM teams, and security leaders. A few years ago, enterprise AI meant calling a model through an API. Today, AI exists as agentic systems — autonomous digital workers capable of planning, acting, and iterating across business systems.

Platforms like Azure AI Foundry, AWS Bedrock, Google Vertex AI, and others are now powering fleets of AI agents that can read data, trigger workflows, and interact with SaaS and internal services. With this shift, enterprises are discovering a new layer of the attack surface:

AI agents are identities — with credentials, permissions, and reach across your digital ecosystem.

And like any identity, if not managed correctly, AI agents introduce risk.

What Are AI Identities? (Definition)

An AI identity is the authentication and authorization layer that enables an AI agent to access enterprise data, APIs, SaaS systems, or internal services. AI identities include:

• API keys
• OAuth tokens
• Service accounts
• Embedded workload identities
• SaaS-granted permissions

This means every AI agent deployed is effectively a new identity — with privileges, behavioral patterns, and potential system access.

Why AI Identity Security Matters

AI is evolving beyond “answering questions.” Agents now:

• Update ticketing systems
• Complete financial tasks
• Read/write to internal databases
• Access CRM or HR systems
• Execute workflow automation

This creates an identity explosion. The average enterprise now has:

Without visibility and governance, AI agents create:

• Secret sprawl
• Shadow access
• Over-permissioned agents
• Orphaned identities
• Compliance failures

This is the fastest-growing identity blind spot in 2025.

The AI Identity Governance Gap

Most enterprises are still in “adopt first, secure later” mode. Common issues include:

• AI agents created without registration
• OAuth tokens shared across multiple agents
• Static secrets hard-coded in pipelines
• No mapped ownership or lifecycle
• Privileges stacking up over time
• No audit trail for agent actions

Without AI identity discovery, security teams don’t know:

• how many agents exist,
• who owns them,
• what systems they access, or
• whether access is justified.

That lack of visibility makes zero-day mistakes almost guaranteed.

How to Discover, Map, and Secure AI Identities (5-Step Framework)

To secure AI agents at enterprise scale, organizations must approach them as identities.

Step 1 — Discover All AI Agents and Identities

Identify every agent across:

• Cloud AI platforms
• SaaS integrations
• CI/CD environments
• Local deployments
• Shadow AI projects

Discovery must include credentials, permissions, and behaviors — not just agent names.

Step 2 — Map Access and Relationships

For every AI agent, map:

• Which systems it reaches
• With which credentials
• At what privilege level
• Using which workflows

This creates a graph of AI access across the enterprise.

Step 3 — Assign Ownership

Every AI identity needs a human owner responsible for:

• Lifecycle
• Permissions
• Misuse or drift

Ownership is the anchor of accountability.

Step 4 — Enforce Least Privilege

AI agents should follow the same identity principles as humans:

• Minimal access required
• Just-in-time tokens when possible
• No indefinite privileged access

Step 5 — Automate Lifecycle & Decommissioning

AI identities should be cleaned up like user accounts:

• Credentials rotate automatically
• Privileges reviewed during access reviews
• Access ends when the agent’s task ends

Lifecycle = security.

How Oasis Secures AI Identities End to End

Oasis provides the first AI identity protection platform designed for the agentic era. Oasis discovers every agent and every non-human identity it relies on, then maps access and risk across your environment.

Key Capabilities

• AI agent discovery across cloud, SaaS, and internal systems
• Identity posture & risk scoring (privilege, sensitivity, anomalies)
• Credential lifecycle & secret hygiene
• Ownership attribution and automated recommendations
• Continuous monitoring and threat detection
• Compliance alignment (EU AI Act, NIST AI RMF)

The Oasis Seven-Pillar Framework for AI Identity Security

1. Discovery & Inventory
2. Ownership & Accountability
3. Credential Lifecycle & Hygiene
4. Access Security
5. Vendor Trust
6. Monitoring & Threat Detection
7. Continuous Risk Improvement

Designed to secure AI agents and the non-human identities powering them.

Key Takeaway

Every AI agent — whether a workflow bot, a productivity copilot, or a custom LLM pipeline — becomes a new identity in your infrastructure.

Securing AI is not about locking down models.
It’s about governing the identities that AI agents use to act.

When organizations bring discovery → mapping → ownership → access control → lifecycle management, AI becomes:

✔ Safe
✔ Responsible
✔ Scalable

Oasis Security provides that foundation — discovering every agent, mapping every identity, and enabling governed AI adoption across the enterprise.