Runtime Identity Security: The Next Evolution of Identity for AI Agents

Runtime Identity Security is the practice of continuously verifying and governing identities while they are actively performing work, not just when they first authenticate.

Instead of asking:

“Who are you?”

Runtime Identity asks:

• What are you trying to do?
• Should you still be allowed?
• Has your context changed?
• Is this action safe?
• Should this session continue?

For AI agents, this evaluation happens continuously throughout execution, not only during login.

Traditional IAM was built around people.

A user logs in.

They receive permissions.

Those permissions remain valid until logout.

AI agents don’t behave this way.

An AI agent might:

• create cloud resources
• access multiple databases
• call dozens of APIs
• execute infrastructure changes
• spawn additional agents

all within seconds.

A one-time authentication decision cannot determine whether every subsequent action is appropriate.

Runtime Identity adds continuous governance after authentication.

AI agents are autonomous.

Unlike humans, they:

• make decisions
• chain workflows
• call external tools
• interact with production systems
• operate 24×7
• act at machine speed

Modern identity systems therefore must control behavior, not just identity.

Dynamic credentials are temporary credentials generated only when needed.

Instead of storing API keys, passwords or tokens inside the AI agent, credentials are:

• created just-in-time
• limited to one task
• automatically expire
• never reused

This dramatically reduces credential theft risk.

Akeyless calls this SecretlessAI™, issuing short-lived identities while keeping secrets out of code, prompts, pipelines, and the agent itself.

Zero Standing Privilege (ZSP) means access exists only while work is being performed.

When the task finishes:

• credentials disappear
• permissions disappear
• sessions terminate

Nothing permanent remains to steal.

For autonomous AI agents, this minimizes the blast radius if an agent is compromised or manipulated.

Traditional authorization asks:

Does this identity have permission?

Intent-based authorization asks:

Does this requested action match the agent’s intended purpose?

For example:

An AI assistant asked to summarize quarterly sales should never issue:

DROP DATABASE

even if it technically has database credentials.

Akeyless evaluates the requested action against the agent’s stated objective before allowing execution, combining AI-based intent classification with deterministic policy enforcement.

Runtime Authority extends identity security beyond authentication.

Instead of validating only who enters a system, Runtime Authority continuously governs:

• every command
• every API call
• every database query
• every privileged operation

If behavior changes unexpectedly, access can be blocked immediately.

Think of it as a security checkpoint at every action, not just at the front door.

Traditional Privileged Access Management (PAM):

• grants privileged sessions
• records sessions
• rotates credentials

Runtime Identity goes further.

It continuously evaluates:

• intent
• context
• requested actions
• risk
• policy

while the session is running.

For AI agents, that distinction is critical because decisions happen continuously, not just when the session starts.

Before you can secure AI agents, you need visibility.

Agentic Identity Intelligence continuously discovers:

• AI agents
• identities
• credentials
• entitlements
• ownership
• data access
• privilege drift
• orphaned credentials

This creates a living inventory that feeds runtime policy and governance.

Yes.

This is one of the biggest architectural shifts.

Rather than embedding API keys or passwords inside the agent:

• the agent authenticates using its workload identity
• Akeyless brokers access through its Gateway
• short-lived credentials are injected only for the approved task
• credentials are never exposed to the agent itself

Even if the agent is compromised, there are no long-lived secrets to steal.

No.

Identity Providers remain essential for:

• authenticating users
• workforce SSO
• federation
• lifecycle management

Runtime Identity complements them by enforcing control after authentication, especially for workloads, machines, and AI agents operating in production. It extends identity into the execution layer rather than replacing existing IdPs.

Most vendors focus on one part of the problem:

• discovering AI agents
• issuing identities
• managing secrets
• providing workload identity
• monitoring runtime behavior

Akeyless combines these capabilities into a unified Runtime Identity Security Platform.

The platform brings together:

• Agentic Identity Intelligence for discovery and visibility
• SecretlessAI™ for ephemeral, credential-free access
• Agentic Runtime Authority for intent-aware enforcement
• Zero Standing Privilege
• Continuous runtime governance
• Full forensic traceability
• A zero-knowledge architecture powered by Distributed Fragments Cryptography (DFC™), ensuring Akeyless never possesses complete customer secrets or encryption keys.

Organizations are rapidly deploying AI agents, often with access to sensitive systems, while many still rely on long-lived credentials and fragmented controls. Akeyless’s 2026 State of AI Agent Identity Security research report found that:

• 94% of organizations use AI agents.
• 84% say AI agents can access sensitive data.
• 83% acknowledge a single compromised credential could affect multiple major systems.
• 77% believe AI agent risk is a current, not theoretical, problem.

As AI agents become autonomous operators, identity decisions made once at login are no longer sufficient. Continuous, runtime enforcement is emerging as the security model for the AI era.