Why Enterprises Need Just-in-Time Provisioning to Secure AI at Scale

AI agents are no longer science experiments in the enterprise. They’re becoming actors in critical workflows—making decisions, performing transactions, and chaining together complex API calls across multi-cloud environments.

Gartner calls this the shift from predictive to proactive AI, where agents don’t just respond—they act. But there’s a catch: our identity systems weren’t designed for this new reality. The traditional ways we manage identity—pre-provisioning accounts, assigning static credentials, relying on role-based access control—break down completely when faced with AI’s velocity, scale, and autonomy.

In this post, I want to frame the problem clearly: Why does agentic AI create a new identity crisis for the enterprise? And why do we need Just-in-Time (JIT) provisioning to solve it?

Why Traditional Identity Models Fail in the Age of Agents

1. Static Provisioning Can’t Keep Up

In human IAM, we create accounts ahead of time, assign roles, and govern them through joiner/mover/leaver processes. But AI agents don’t work that way:

• They may exist for seconds or minutes.
• They often spawn dynamically in response to a task.
• You may have hundreds of agents active concurrently in a single workflow.

Manually provisioning identities or using static service accounts at this scale is impractical. It creates operational drag and unnecessary attack surface.

2. Stale and Over-Permissioned Credentials Become the Norm

Without JIT, many organizations resort to pre-shared, long-lived credentials for agents. These secrets:

• Often have excessive privileges to “cover all cases.”
• Sit unused for long periods, expanding the attack surface.
• Are hard to rotate and audit at scale.

This is a ticking time bomb for security teams: credentials proliferate, permissions drift, and you lose visibility into which agents are doing what.

3. Lack of Delegation and Provenance Ties

Agents frequently act on behalf of humans or other systems. But without dynamic identity provisioning:

• There’s no clean way to bind an agent’s identity to its delegator.
• It’s difficult to prove chain-of-trust in audits or respond to incidents.

When something goes wrong, you can’t answer the most basic questions: Who authorized this agent? What was it supposed to do?

4. No Standardized Agent Identity Profiles

Today’s approaches to agent identity are ad hoc:

• Some agents use hardcoded API keys.
• Others might be represented as service accounts with no meaningful attributes.
• Very few enterprises track agent provenance, TTL, or risk level in a consistent, enforceable way.

This fragmentation undermines your ability to enforce Zero Trust or apply consistent policy across your AI ecosystem.

5. Credential Sprawl Creates Operational and Security Debt

Without JIT:

• Admins have to manage sprawling lists of static credentials and agent accounts.
• Deprovisioning becomes unreliable—ghost accounts linger, increasing risk.
• License costs for identity platforms may spiral as every agent requires a pre-provisioned identity.

You end up paying more for an identity system that’s delivering less security and more risk.

6. Scaling IAM for AI Agent Populations Becomes Impossible

Consider this: By 2030, some enterprises will have 80 times more AI agents than human users. If we treat agent identities the same way we treat human accounts:

• The administrative burden becomes unmanageable.
• Identity platforms buckle under the load.
• Security teams can’t keep up with policy enforcement or auditing requirements.

Agent identity at human scale simply doesn’t work for AI.

7. Inadequate Policy Enforcement for Agentic Delegation

When agents act on behalf of users or other agents:

• We need to enforce least privilege dynamically.
• We must ensure fine-grained scopes appropriate to each task and context.
• Without dynamic provisioning, we resort to broad, static entitlements that fail Zero Trust principles.

This creates fertile ground for privilege escalation, lateral movement, and abuse.

8. Agent Actions Are Difficult to Trace and Audit

Without JIT identity provisioning:

• There’s no reliable way to connect agent actions to identity records.
• It’s harder to demonstrate compliance with DORA, NIST 2.0, GDPR, or industry frameworks.
• Incident response teams lack the telemetry they need to investigate anomalous activity.

You lose the ability to “follow the breadcrumbs” when it matters most.

9. Complexity and Cost Spiral Out of Control

When we try to retrofit human-centric IAM to agents:

• We over-provision accounts.
• We accrue credential debt.
• We burn operational resources trying to maintain order.

And we still fail to deliver the agility and security that modern AI systems demand.

The Core Problem: We’re Using Yesterday’s Identity Playbook for Today’s AI Agents

Our IAM systems were designed for people: long-lived, slow-moving, predictable entities.

AI agents are none of those things. They’re:

• Ephemeral.
• Bursty.
• Delegated.
• Autonomous.

Without dynamic, policy-driven identity provisioning at runtime, we’re left with a patchwork of risky workarounds and operational overhead that can’t scale.

Why JIT Provisioning is the Only Way Forward

The solution is clear: Just-in-Time identity provisioning purpose-built for AI agents. This means:

• Provisioning agent identities only when needed.
• Scoping permissions precisely to the task at hand.
• Binding agents to their delegators for full traceability.
• Retiring credentials the moment they’re no longer required.
• Logging every action for continuous compliance.

In the next post, I’ll explore how Strata’s Maverics platform makes this possible—and how enterprises can embrace JIT provisioning to secure agentic AI at scale. Read the continuing blog post: How Just-in-Time Provisioning Creates Artificial Agent Identities on Demand