For years, we have talked about AI as a force multiplier for security. Faster detection, better prioritization, automation at scale.

All true.

But regulators are now asking a different question: how well do we understand, govern, and control AI when it fails?

That shift is subtle, but it changes what accountability looks like. Waiting for enforcement dates is a risk most organizations can’t afford. What matters now are the fundamentals: knowing what AI is in use, who owns it, how risk is assessed, and how AI-driven analytics are used to build governance that is defensible and scalable before scrutiny arrives.

Across the EU, the AI Act is setting expectations for accountability, risk classification, transparency, and documentation. NIST has stepped in with the AI Risk Management Framework, offering a practical lens on managing AI risk across its lifecycle. Financial regulators are also already treating AI models as part of traditional model risk management.

Regulators are aligning around a core idea: if you deploy AI, you are accountable for how it behaves.

That includes:

• How decisions are made

• What data influences those decisions

• How outcomes are monitored and validated over time

Experience has taught me that waiting for final enforcement dates is not a strategy. It is how organizations lose control of the narrative, reacting instead of shaping how their AI risk posture is understood.

Most organizations don’t lack AI capability. They lack visibility.

What I see consistently is not a technology gap—it’s an awareness gap. Organizations are struggling because they cannot answer basic questions with confidence:

• What AI models do we actually have in production?

• Who owns them?

• What data do they rely on?

• What happens if they behave unexpectedly?

• And more importantly, can we explain any of this to a regulator, customer, or board member without scrambling?

In many cases, the answer is no. Not because teams aren’t capable, but because no one has stitched the full picture together.

AI risk doesn’t fit neatly inside the security function, and treating it that way creates blind spots.

CISOs can and should classify the AI they directly operate, and the security tools they rely on, but enterprise AI risk cannot be delegated to security. It sits with anyone making decisions about how AI is built or used.

The real challenge is influence. CISOs have to bring stakeholders together around a common language for AI risk, shared expectations for ownership, and simple processes that make participation unavoidable. That means embedding AI inventory and impact assessments into core business processes so governance happens by design, not as an exception.

This is where CISOs need to lean in early.

The work that matters now is not writing perfect policies or predicting every regulatory nuance. It is doing the unglamorous fundamentals, starting with your own organization.

1. Build a Real AI Inventory: Not a static list, but a living view of where AI exists across internal development and third-party tools. If it influences decisions, it belongs in scope.

2. Define Ownership Clearly: Every model, system, or AI-enabled process should have an accountable owner and not just a technical contact, but someone responsible for outcomes.

3. Classify Based on Impact: Not all AI carries the same risk. Focus on where decisions affect customers, finances, operations, or safety and not just where models are most complex.

4. Run Meaningful Impact Assessments: Go beyond check-the-box exercises. Evaluate bias, resilience, explainability, and operational dependency. Ask: what happens if this fails?

5. Integrate Into Existing Processes: AI shouldn’t sit outside governance, it should be absorbed into it. Model risk, incident response, third-party risk—these already exist. Extend them.

AI-driven analytics and automation can help, but only if we are honest about their role. They are the means to operationalize discipline at scale, enabling continuous monitoring, testing of assumptions, detection of drift, and the production of evidence when scrutiny arrives.

What they can’t do is own accountability. AI can surface risk. It cannot decide how much risk is acceptable. That remains a human decision, one that organizations need to make explicitly, not implicitly.

If cyber resilience used to be about how fast we could recover, AI regulation is forcing a harder question: Are we designing systems we can stand behind, explain, and defend when things go wrong?

CISOs who start answering that question now will be far better positioned when regulators, boards, and customers start asking it loudly. AI governance isn’t a future requirement. It’s a present expectation.

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