OpenAI’s recent announcement of Frontier – an enterprise platform for building, deploying, and managing AI agents – is another clear signal that AI systems are moving beyond simple chatbots to autonomous, long-running agentic services. These AI agents can plan, take actions, call tools, and operate across many disparate systems on a user’s behalf.
For many teams, the opportunity is clear: AI agents can unlock real innovation and productivity. As these agents become more capable, however, the core question shifts from “How smart or accurate are they?” to “How do we safely let them do things in production?”
If you can’t prove trust, accountability, and control, securely deploying agents becomes a major roadblock.
What’s more, effective AI governance requires a clear boundary between agent development and access control. Achieving this level of governance requires a vendor-neutral identity layer that enforces access controls across all AI and SaaS environments, irrespective of what platform these agents are built on (e.g., ChatGPT, Claude, Gemini, Copilot). This prevents the lock-in and blind spots inherent in platform-specific tools.
A reality enterprises can’t ignore: agents expand the attack surface
OpenAI’s announcement validates a core thesis: Identity is key to managing AI deployments in production. The decision to build identity and governance into the agent platform underscores a fundamental truth: you cannot have secure agentic workflows without verifiable, managed identities.
Without this foundation, the transition from pilot to production exposes significant risks that traditional tools were not designed to handle:
- Over-privileged access: Agents often inherit broad permissions, especially when tied to shared service accounts.
- Credential sprawl: API keys, OAuth tokens, and other secrets are often hardcoded into configs, notebooks, prompt templates, or tool servers.
- No guardrails: Agents act autonomously and will often call tools you didn’t expect or access data they aren’t authorized to see.
- No accountability: “Who did what?” becomes hard to answer when actions are taken by an agent acting on behalf of a user across multiple apps and APIs.
This visibility gap highlights why so many teams get stuck before production: building an agent is only half the battle. Equally important is controlling what it can access and proving who it acted for, once it starts taking real actions across enterprise apps and data.
Architectural considerations for enterprise AI security
While OpenAI’s Frontier provides a framework to manage agents, it introduces architectural challenges that transcend the operational boundaries of a single AI platform:
- Independent governance standards: Relying on a model provider to also serve as the primary security auditor can create internal oversight challenges. To maintain consistent compliance standards, organizations require a security layer that operates independently of the underlying AI model.
- Interoperability across the multi-model stack: Frontier is optimized for the OpenAI ecosystem. As enterprises adopt a multi-model strategy (e.g., Claude, Gemini, or open-source alternatives), they require a vendor-neutral identity layer to maintain visibility and control across the entire environment.
- Unified identity lifecycle management: While a platform may manage permissions within its own ecosystem, agents interact with the broader enterprise, including HR systems and cloud infrastructure. Managing these agents as first-class identities within a centralized system helps ensure consistent policy control across your stack.
Why identity becomes the control plane for autonomous agents
Governing access across complex environments is not a new challenge. For decades, identity providers have focused on delivering a central control plane used to secure every access policy for human users, apps, and resources. And now, that extends to AI agents. As AI agents proliferate, the same principles apply, just with new requirements:
- Strong identity for agents
Agents need distinct identities (not shared accounts) so you can assign ownership, apply policy, and audit activity. - Least-privilege authorization, continuously enforced
It’s not enough to grant access once. Agent permissions need to be scoped, time-bound, and reevaluated as risk changes. - Secure token handling
If agents are going to call tools and APIs, token handling must be centralized, reducing leakage and eliminating long-lived secrets. - Human-in-the-loop for sensitive actions
Some actions should require step-up auth or asynchronous approval, especially when an agent is operating autonomously.
These agents need the same lifecycle controls as human identities (provisioning, policy, governance, monitoring, and deprovisioning), plus tighter guardrails because they act faster and more broadly.
Where this leaves technology leaders
Although OpenAI’s announcement has further proven that the agentic era is here, it should not come at the cost of control. When identity is tied exclusively to the platform where agents are built, you lose the independent oversight required for comprehensive governance. This creates a critical visibility gap, leaving the door open for 'shadow agents' built outside the OpenAI ecosystem to operate undetected and unmanaged.
To scale with confidence, IT and security leaders must leverage a neutral identity layer to securely manage these production-ready AI agents across an environment that is increasingly distributed, multi-cloud, and multi-vendor.
How do we secure AI?
Okta and Auth0 give you everything you need to secure every agent and all agents. We provide a two-pronged solution that treats AI agents as first-class identities, allowing organizations to govern the entire agent lifecycle from the first line of code to enterprise-wide retirement.
Connect with an Okta expert to learn more and see the demo.
