We are witnessing a fundamental evolution to our digital workforce. AI agents are no longer just experimental novelties. They are goal-driven achievers that are reshaping how enterprises operate. We have moved well past the early adoption phase and into a critical period of operational risk. In fact, 91% of organizations are already utilizing AI agents according to the 2025 Okta at Work report. Yet only 10% have a well-developed strategy for managing these non-human identities. The gap between adoption and governance isn't just a risk. It's a massive blind spot in enterprise security.
But let’s be honest about the state of security. While adoption is skyrocketing, our governance models have not kept up. According to The Times, nearly half of these organizations lack any formal governance framework. This creates a massive governance gap. We now have an environment where autonomous non-human identities act with high-speed decision-making capabilities, yet they often lack the visibility, authentication, or authorization controls required to keep them secure.
For IT and Security leaders, the reality is stark. You cannot secure what you cannot see, and you cannot govern what you have not identified.
The Risk of Legacy IAM in an Autonomous World
Traditional Identity and Access Management (IAM) was architected for humans. We log in, we start a session, and eventually we log off. AI agents disrupt this paradigm entirely. They act continuously and consume data with an insatiable appetite to satisfy their goals. They are capable of executing complex API chains across systems without any human oversight.
Legacy security models fail these agents in three specific ways:
- Static Credentials and Secret Sprawl: Developers often hard-code API keys or use long-lived tokens to give agents access to tools. This creates a brittle, high-risk attack surface where a single compromised agent can lead to massive privilege escalation.
- Coarse-Grained Authorization: Standard IAM grants access at the application level. However, agents using Retrieval Augmented Generation (RAG) need authorization at the data level. Without fine-grained controls, an agent might inadvertently retrieve and synthesize sensitive executive data for a general employee because it bypassed intended access silos.
- The "Super-Admin" Problem: To function autonomously, agents are often over-provisioned with broad service account permissions. If hijacked, these agents become capable of high-velocity damage across your entire ecosystem.
A Unified Approach: Secure Every Agent, Secure All Agents
Securing our agentic future requires a shift from static gatekeeping to dynamic, identity-centric control. This creates a dual challenge. We must provide builders with the tools to secure agents by design while providing IT and Security teams with a control plane to manage them at scale.
At Okta, we define this architecture through two distinct but integrated lenses.
1. For Builders: Security by Design
Developers need to embed security from the first line of code without slowing down innovation.
- Eliminate hard-coded secrets sprawl: Instead of managing risky long-lived keys, developers can use a secure vault that manages OAuth token lifecycles. This automatically refreshes credentials without ever exposing them to the agent's code or logs.
- Standardized Context Retrieval (MCP): Agents need access to context from multiple sources like CRM data, documents, and calendars. Custom integrations often create security fragmentation. The Model Context Protocol (MCP) provides a standardized way to authenticate and authorize access across all these sources, with your enterprise authorization server enforcing consistent policies. No custom auth logic required.
- Enforce least privilege at the document level: For RAG systems, Fine-Grained Authorization (FGA) enforces relationship-based access control at the point of document retrieval. This ensures that responses are generated only based on data the authenticated user is permitted to see.
- Govern critical actions with a human-in-the-loop: For high-stakes actions, such as authorizing a major purchase, agents can trigger asynchronous approval workflows. This pauses execution until a human explicitly validates the action via mobile push or email.
2. For IT and Security: The Enterprise Control Plane
Once deployed, agents must be treated as first-class identities with full lifecycle management.
- Detect Shadow AI: Discover and Register Unmanaged Agent Identities: The first step in governance is eliminating the security blind spot of shadow AI. Utilize automated discovery and behavioral analytics to surface unmanaged non-human accounts operating across cloud and SaaS platforms. Once identified, a centralized registry must establish an identity profile for every non-human actor, mapping ownership and accountability to transition agents from untracked assets to managed, auditable entities.
- Govern Lifecycle & Access: Enforce Least Privilege and Dynamic Policy: Treat agents as high-velocity employees by implementing automated lifecycle transitions, from provisioning with role-based templates to systematic deprovisioning. This governance extends to defining dynamic authorization policies using modern policy engines to enforce least privilege based on the agent's identity and operational context.
- Control AI agent and app connections: Agents often need to bridge trust domains, such as an internal sales agent accessing a third-party partner portal. Cross App Access (XAA) is a standards-based protocol that enables agents to securely access resources across organizational boundaries while preserving the original user context.
- Vault & Isolate Secrets: Eliminate Hard-Coded Credential Risk: You cannot allow privileged credentials to reside in code or configuration files. Protect sensitive secrets (API keys, service account credentials) by enforcing secure vault storage and requiring automated rotation schedules. This strict isolation and rotation can drastically reduce the attack surface and prevent a single compromised agent from leading to massive privilege escalation.
- Contain Threats: Execute Real-Time Containment: An autonomous agent requires an emergency brake. In the event of anomalous behavior, such as an agent accessing 500 records in minutes, security teams must have an immediate response capability. Universal Logout provides this 'kill switch', instantly revoking all active tokens and sessions across integrated applications to help contain the threat and prevent further damage in real-time.
Identity is the Control Plane
The rise of agentic AI makes identity the only viable control plane. Whether you are building B2B SaaS agents or deploying internal workforce automation, the security of your AI strategy hinges on your ability to authenticate, authorize, and govern non-human identities with the same rigor applied to your workforce.
Here's the reality: every AI agent you deploy without proper identity controls is a potential breach waiting to happen. Every shadow AI agent your teams spin up without IT approval is a blind spot in your security posture. Every over-privileged service account is a skeleton key that attackers are actively hunting.
The governance gap isn't theoretical. With 91% of organizations deploying AI agents but only 10% having well-developed governance strategies, the risk isn't just emerging. It's already here. But there is a clear path forward.
Ready to architect your defense?
Don't let the governance gap become your next security incident.
Download our comprehensive whitepaper: Securing AI Agents From Development to Enterprise Scale.
Inside, you'll discover:
- Complete reference architecture with a detailed 8-phase implementation flow
- Security patterns for both Auth0 (secure by design) and Okta (enterprise control plane)
- Real-world case study: Enterprise sales AI agent secured end-to-end
- How to discover and register shadow AI agents as first-class identities
- Token exchange, MCP security, and human-in-the-loop authorization patterns
Learn more about Okta's approach to securing AI agents at okta.com/solutions/secure-ai
