A recent report found that 84% of security leaders have seen their roles revised—formally or not—to include securing the exposure from the rampant onslaught of AI and LLMs within the enterprise.
This isn't surprising. While some organizations debate who should "own" AI risk, the reality on the ground is that security teams are already dealing with the fallout.Other executives view AI through different lenses—operational uptime, data architecture, or financial exposure. But the CISO is the only leader whose entire domain is built on managing access, authentication, and policy enforcement at a granular level.
When an AI model requests sensitive data, it isn't an "operational" question. It's an access control problem. This is the CISO's native domain.
For security leaders, this isn't about a new, neatly-defined 'burden.' It’s about recognizing that AI has already rewritten the rules of cybersecurity, and the old models are broken. AI is not just another IT project. It's a new, asymmetric source of exposure. And its primary vector is, and always will be, identity.
For decades, security strategy has focused on the human element. We built castles and moats to protect data from unauthorized people. We managed the identities of our workforce.
AI shatters that model.
We are now introducing thousands of new "identities" into our environments that do not belong to people. AI models, autonomous agents, and API-driven processes are the new "users." They act on behalf of people. They have credentials. They request data. They execute tasks.
This creates a governance black hole. How do you authenticate an LLM? How do you apply a least-privilege policy to a machine-learning model? How do you revoke access from an autonomous agent you can't even find?
This is, fundamentally, an identity and authentication problem.
This is the very essence of a Zero Trust architecture: Never trust, always verify. That non-negotiable principle must now be extended from our human users to the new, non-human identities of AI.
The other side of this coin is the "Shadow AI" problem—a new face on the old "Shadow IT" challenge. Your employees are using unauthorized AI tools. They are feeding them sensitive corporate data: strategic plans, PII, and source code.
This is a classic data exfiltration and governance failure, enabled by a broken identity model.
It's happening because our security stacks have no way to enforce authentication controls between a verified corporate user and an ungoverned, third-party application. We have no way to control what our corporate identities are permitted to "log in" to.
This isn't a training problem. It's a policy enforcement problem.
You cannot govern what you cannot identify.
This is why, regardless of who "owns" the AI strategy on an org chart, the security leader must be at the center of securing it. The foundation for AI safety is not a complex, new AI-specific tool. It is a robust, flexible, and continuous authentication fabric.
We need a control plane that can manage identity and enforce policy for every single request, regardless of its source—human or machine.
Before our organizations can ask, "What can this AI do?" we must be able to definitively answer, "Who (or what) is asking?" and "Should they be allowed to?"
Whichever leader is ultimately tasked with this challenge, they need a clear strategy, leadership alignment, and the right resources. But the security strategy itself must be clear.
Success in the age of AI doesn't start with the algorithm. It starts and ends with identity.
The 84% statistic confirms that security leaders' roles have expanded to cover AI-driven exposure, which is fundamentally an identity and access control problem.
AI introduces a new, non-human attack surface (models, agents, APIs) that traditional security, built for human users, cannot manage.
"Shadow AI" is a Zero Trust failure, where organizations cannot enforce policy on what third-party applications their verified users are accessing.
A modern authentication fabric is the foundational control plane for governing both human and machine access to AI tools, and it must be in place before any other AI security can be applied.