The market for agentic AI—the kind of artificial intelligence that can not only analyze data but act on it—is expanding fast. Analysts expect it to grow from $1.83 billion in 2025 to $7.84 billion by 2030, a compound annual rate of nearly 34 percent. Companies are embracing autonomous AI agents to automate complex workflows, respond to customers, and even manage infrastructure in real time.
But this surge comes with a tradeoff. The autonomy that makes these systems effective also makes them unpredictable. Every decision an AI agent makes, every connection it forms, opens a potential path for misuse or exposure that traditional security models aren’t built to handle.
The Rise of Non-Human Identities (NHIs)
Agentic AI depends on a vast web of machine credentials—API keys, service accounts, and authentication tokens—that allow agents to communicate and act on their own. These are known as non-human identities, or NHIs. They’re meant to simplify automation. But at the same time, they’ve created a blind spot security teams may overlook.
As AI agents spin up new connections, call APIs, and chain tasks together, they often generate or inherit access rights with little human oversight. Each of those identities can outlive the task that created it, lingering in systems with broad or persistent privileges. That means more entry points for attackers, more credentials to protect, and far less visibility into who—or what—is actually accessing sensitive data.
The fallout can be serious: exposed keys that leak data to the open internet, orphaned tokens that grant long-forgotten access to production systems, and cascading failures when one compromised agent passes its credentials to another. The sheer speed and scale of these automated relationships have made NHI management one of cybersecurity’s newest and most urgent challenges.
“Every AI agent is an identity with access and permissions,” said Shane Barney, Chief Information Security Officer at Keeper Security. “If you’re not tracking what those identities can do, you’re already behind. The risk isn’t just data loss, but also the potential for loss of control.”
Why Traditional IAM Falls Short
Traditional identity and access management systems were never built for this. They were designed to authenticate employees, contractors, and partners—real people who log in, log out, and can be audited. But autonomous AI doesn’t fit that mold. It creates and uses credentials faster than any human process can track, often without any central oversight or expiration dates.
Conventional IAM tools can’t keep pace with the sheer volume, velocity, and persistence of these NHIs. Manual provisioning and periodic reviews simply don’t work when thousands of agents are interacting, spawning new access tokens on the fly. As a result, security teams end up chasing a moving target, unaware of how many active credentials exist or which ones hold sensitive permissions.
But as Diana Kelley, Chief Information Security Officer at Noma Security, points out, this challenge goes beyond IAM. It’s about weaving agentic AI security into every layer of the enterprise. “That means being able to discover every agent and connectors like MCP (model context protocol) servers across the environment; harden them through continuous assessment, red-teaming, model provenance verification, and AI security posture management; and protect them in real time with continuous monitoring and adaptive policy enforcement,” she said. “These layered controls are what allow enterprises to keep pace with the dynamic and autonomous nature of agentic AI while preserving integrity, accountability, and compliance.”
That’s why security leaders are calling for an identity-first approach tailored to AI. Governance has to start at the access layer, with policies and monitoring built for the way agents actually operate—continuously, autonomously, and at machine speed.
Introducing the Agentic Access Management (AAM) Framework
Oasis Security has stepped into this gap with its new Agentic Access Management (AAM) Framework, the first practitioner-built model for governing the AI access layer. Developed in collaboration with Sequoia Capital and a group of leading CISOs, the framework was designed by security professionals who understand the day-to-day realities of defending enterprise systems against automation-driven risk.
The AAM Framework is vendor-neutral, meaning it applies across platforms and environments, regardless of the tools an organization already uses. Its purpose is to give security and IT teams a structured way to identify, control, and monitor the machine-to-machine connections that power agentic AI.
The framework is organized into maturity levels that help organizations benchmark where they stand and what steps to take next. Each level outlines specific controls and operational best practices, ranging from discovery and inventory of NHIs to continuous monitoring and policy enforcement. Rather than offering abstract principles, the framework translates AI governance into actions that teams can implement immediately.
Bridging Standards and Real-World Security
Rather than trying to replace existing standards, the AAM Framework builds on them. It’s meant to work alongside emerging identity and access models, extending their principles into the realm of autonomous AI. Where traditional frameworks focus on human users and static accounts, AAM adds the layer needed for dynamic, machine-driven access without forcing organizations to overhaul their entire identity stack.
Oasis designed the framework for broad adoption. It invites collaboration among industry leaders, CISOs, and technology partners to create shared governance practices around agentic AI. By aligning with existing identity ecosystems instead of fragmenting them, AAM aims to become the common language for securing the growing AI workforce, one that’s flexible enough to evolve as the technology and its risks continue to change.
The AAM Maturity Assessment
To help organizations put the framework into action, Oasis has paired it with a free AAM Maturity Assessment. The tool gives security and IT teams a baseline view of how prepared they are to manage AI-driven access. By answering a structured set of questions, organizations can see which maturity level they fall into and where their biggest blind spots lie.
The assessment doesn’t just assign a score; it highlights specific governance gaps, from missing visibility into machine credentials to weak lifecycle management for AI agents. The results help teams prioritize improvements and map a realistic path toward stronger oversight. For many, it’s the first clear way to measure progress in an area that until now has been largely undefined.
Building Confidence in the Age of Autonomous AI
Securing the AI workforce has quickly become a boardroom concern. As enterprises deploy autonomous agents to drive operations, they’re also expanding the attack surface in ways few executives fully understand. Every AI agent is now an identity with the power to make decisions, move data, and act independently. That puts access governance at the center of both business strategy and risk management.
Agentic AI will continue to reshape how organizations operate, acting as a partner that accelerates work, but also as a potential threat vector if left unchecked. Frameworks like AAM give leaders a starting point for bringing structure, accountability, and visibility to this new digital workforce. Trust in intelligent machines begins with clear boundaries for how they work alongside people.
