A Guide to Agentic AI Identity Maturity

Executive Summary

The emergence of agentic Artificial Intelligence (AI) has ushered in a new era of productivity and innovation. These agents can retrieve information, execute workflows, and interact with enterprise applications and systems. But as more AI agents are deployed and embedded in core business operations, companies face a critical turning point in cybersecurity. As agents multiply, so does the attack surface. In order to safely harness the power of AI, agents must be treated as first-class identities. This starts by establishing clear accountability and visibility before agents scale out of control. Secure organizations must be able to answer three questions: 1) Where are my agents?; 2) What can they connect to?; and 3) What can they do? These questions define the operational blueprint for securing AI agents. In order to answer them, organizations must implement the right systems, identity controls, and governance model.

This guide provides a comprehensive identity-driven framework for securing agentic AI. It outlines how organizations can leverage Identity and Access Management (IAM) as the central control plane to mitigate the unique threats posed by AI agents, to help maintain compliance, reduce risk, and enable safe AI-driven innovation.

Introduction

The AI workforce is here, with 91% of organizations already using AI agents1 to take actions across their business. However, the rapid emergence of AI agents introduces complexity as well as significant security vulnerabilities, from hardcoded credentials and over-provisioned permissions to massive governance and compliance gaps. Traditional security models, built for human users, are fundamentally insufficient for governing dynamic, high-velocity AI agents operating at machine speed. Furthermore, new regulations and compliance requirements around AI are being implemented around the world2,3,4. As organizations scale their AI agent implementation, increased maturity levels will be expected by their governing regions and industries. To remain competitive and compliant, organizations must realize a critical reality – agentic AI security is identity security.

To safely deploy AI agents across more use cases, organizations must treat identity as the primary control plane. To securely manage agent identities, organizations must be able to: define who or what the agent is (directory, entry, human owner, verifiable credential), authorize what the agent is permitted to do (scope, delegated user authority, fine-grained entitlements), manage the runtime of how an agent executes (proxy gateway, session enforcement), and govern the lifecycle and accountability of that agent (certifications, audits, de-provisioning). Now, this level of identity maturity is not achieved overnight; it’s a progressive journey. Building upon proven Zero Trust models, this maturity roadmap is structured across four stages:

• Foundational: Meeting essential agent identity needs while creating a strong, reliable foundation for maturation

• Scaling: Expanding a consolidated agent identity footprint to new apps, services, use cases, and agent types

• Advanced: Increasing automation and integration to elevate experiences, improve agility, and strengthen security

• Strategic: Gaining a strategic advantage through initiatives that empower the workforce or user base, optimizing efficiency, and leveraging identity to detect and respond to threats in real time

This guide outlines how organizations can advance their agentic AI maturity through the identity layer, strengthening security and compliance at every step.

Model Context Protocol Roadmap

The Model Context Protocol Roadmap highlights that, "Enterprises are deploying MCP at scale and hitting gaps the protocol does not yet address." It calls for paved paths away from static client secrets towards SSO-integrated flows through emerging standards, like Cross-App Access (XAA). 

Source: Enterprise Readiness, Model Context Protocol Roadmap

Maturity Stages

Stage 1: Foundational - Establishing visibility and control

At the earliest stage of maturity, your organization may have dozens of agents, known and unknown, acting across a limited set of core use cases. The primary goal of this identity maturity stage is to centralize tracking and reporting of AI agents in order to establish basic visibility. You also need to discover agents you don’t know about and bring them into a centralized view, along with your known agents. By centralizing agents in a single source of truth, security teams gain a single audit trail for tracking and reporting on agent access and system events. This is the essential first step to meet compliance requirements, simplify incident investigations, and create a reliable foundation for scaling AI agents in a secure manner from the start. Upon fulfilling these foundational maturity markers, you should be able to answer how many AI agents are operating in your environment, who owns each one, and what they accessed within a specific window of time.

Identity Challenges:

• No single source of truth: Organizations are pressured to quickly deploy and implement AI agents, but with this speed, full visibility and control is often left behind. Agents are scattered across platforms, teams, and tools, with no centralized inventory. Without a registry of every agent and a designated human owner, there's no way to govern, audit, or revoke its access.

• Compliance & incident investigations: Without centralized logs, security teams operate in the dark during an incident investigation. Failing to maintain these logs severely hampers an organization’s compliance readiness and can significantly delay threat resolution times.

• Multi-platform AI agents: Agents might be built on various different platforms, meaning there’s risk of incomplete agent visibility as well as inconsistent onboarding processes.

• MCP server sprawl: Agents increasingly reach tools and data directly through Model Context Protocol (MCP) servers. Without visibility into which MCP servers exist, who authorized them, and which agents are calling them, a growing portion of enterprise AI activity happens outside any audit trail, bypassing the identity provider entirely.

Actions to take & their benefits:

Stage 2: Scaling - Standardizing protection and implementing basic automation

At this stage, organizations may have hundreds of agents deployed across multiple teams and platforms. As they move to scale their AI agent rollout to the entire workforce or user base, their agent identity security practices need to evolve alongside their expansion. The focus shifts from the inefficient manual administration and tracking of AI agents, to consistent and basic automation of security controls across all phases of the AI agent lifecycle. By replacing static credentials with narrowly-scoped access governed by policy, organizations can significantly standardize how agents access resources, improving their overall security posture. This would mean that no AI agent holds a hardcoded credential or a long-lived static token, every agent is provisioned and deprovisioned automatically through your directory, and a single query returns every agent’s current access across every resource.

Identity Challenges:

• Hardcoded & long-lived credentials: The potential and risk for embedded static secrets and API keys in agent code creates massive security liability (e.g., prompt injection attacks).

• Lateral movement risk: Adversaries can manipulate agents with overly broad permissions to move laterally across critical infrastructure if their host system or credentials are compromised.

• Inconsistent security posture: Siloed teams might apply different or inadequate security controls to their AI agents.

• Expansion of AI agent rollout: As organizations scale agent rollout to more employees or users, manual onboarding and registering of AI agents and their access places additional burden on IT teams to manage at the scale needed to drive the business. 

• Consent sprawl: When every employee grants OAuth consent individually to third-party AI tools, organizations don’t have a centralized audit trail or way to revoke the agent across environments

• Unmanaged MCP connections: Most MCP servers ship with static API keys or bearer tokens and no native OAuth. Without a standard way to broker MCP access, every new MCP server reintroduces the secret-sprawl problem this stage is meant to solve.

Actions to take & their benefits:

Stage 3: Advanced - Automating for continuous governance across the lifecycle

At the advanced stage, organizations might have thousands of AI agents, many of which are ephemeral or task-scoped, with connections to multiple platforms or even agent-to-agent connections. The focus of this stage shifts to consistent governance workflows that enforce least-privilege access across both human identities and AI agents. Agents are unable to exceed the authority of the human who delegated to it, and no human has to manually approve the same agent twice for the same task. When a human owner leaves the organization, their agents are decommissioned alongside their human owner’s exit. 

Identity Challenges:

• Traditional governance was built for humans, not AI agents: Legacy IGA access review processes can’t scale to the volume of agents in the enterprise, when agents outnumber humans by orders of magnitude, and may leave gaps if it is not able to appropriately tie agent behavior to the human it is acting on behalf of. 

• Shadow AI blind spots: Proliferation of unmanaged AI agents with unknown, often excessive, permissions.

• Privilege creep: The gradual accumulation of overly-permissive permissions over time as an agent's role evolves.

• Manual governance overhead: The manual effort and time required for IT and security teams to conduct periodic access reviews for a rapidly growing number of agents at the speed required for these agents to run. 

• MCP catalogs evolve faster than review cycles: Tools are added to and removed from MCP servers continuously. Access reviews that only look at which agents can reach which server miss the real question of, “Which tools on this server can this agent still invoke?”

Actions to take & their benefits:

Stage 4: Strategic - Enforcing dynamic, risk-aware controls

At the highest level of maturity, organizations have integrated and operationalized AI agents across their entire workforce or user base. Agents vastly outnumber humans and are created and retired at machine speed. Their identities have become a fully governed, automated, and strategic function across their lifecycle. The focus of this stage is a highly automated model for monitoring AI agent behavior, such as anomaly detection and automated threat response. Anomalous agent behavior is detected and contained in minutes rather than weeks. You should be able to revoke every token, session, and downstream access for any agent in the enterprise with a single action.

Identity Challenges:

• Agents are nondeterministic: Actions are unpredictable, so tightly scoped, short-lived and user-delegated access is a critical line of defense.

• Static, over-provisioned access: Agents might retain broad permissions indefinitely, even after their operational needs have changed.

• Inability to respond to real-time threats: A lack of automated mechanisms leaves organizations unable to adapt to emerging threats or anomalous agent behavior.

• New frontier of security threats against enterprise identities: Agents face a new class of attacks that traditional security was not built for. Even the best model-layer defenses like DLP, prompt filtering, and content moderation will sometimes fail. When they do, identity and authorization become the last line of defense that determines how far an attacker gets.

• Tool-level authorization: A single MCP server can expose dozens of tools at different sensitivity levels (read only vs. delete a record vs. transfer funds). Session-level authorization is often too coarse; a compromised session or token shouldn't automatically grant access to every available tool. Controls should be scoped by tool, action, and resource sensitivity.

Actions to take & their benefits:

Conclusion: Enabling innovation with secure AI agents

The rise of agentic AI represents a massive leap forward in organizational capability, but this widespread adoption fundamentally challenges how organizations need to approach security. Organizations must evolve towards a modern, identity-first security model.

By progressing through the Agentic AI Identity Maturity Model, from establishing foundational visibility to enabling strategic, zero-touch governance, organizations can move beyond reacting to the risks of shadow AI. Instead, they can build a secure, Zero Trust foundation that empowers developers and business units to innovate responsibly and in accordance with regulatory requirements. When identity is effectively leveraged as the control plane, AI agents transform from a massive security liability into a governed, trusted, and strategic advantage.

Find out how exposed your agents are and learn what to fix first.

Sources:

1 Okta Businesses at Work 2026 report

2 EU AI Act

3 SB24-205 Consumer Protections for Artificial Intelligence | Colorado General Assembly

4 The 10 guardrails | Department of Industry Science and Resources  

5 Identity Assertion JWT Authorization Grant

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