AI security isn’t about replacing your existing cybersecurity program. It’s about extending governance, risk management, and security practices to address the unique risks introduced by artificial intelligence.
Over the past few years, nearly every conversation I’ve had with organizations about AI adoption eventually lands on the same question: how do we actually know our AI systems are secure? Teams are excited about what AI can do. Very few can answer that question with confidence.
It’s a fair question, and a harder one than it sounds, because AI introduces risks that most cybersecurity programs were never designed to address. An organization can have mature controls for its networks, cloud environments, applications, and sensitive data, and still have no meaningful coverage for its machine learning models, training data, or AI-driven decision-making. The controls don’t transfer automatically.
Here’s the misconception I run into most often, though: that fixing this requires standing up an entirely new security program. It almost never does. Most organizations already have the building blocks. The work is extending what exists . governance, risk management, compliance — to cover what AI adds, not replacing it. An AI security risk assessment is the structured way to do that, and it’s the approach I’ll walk through here, based on how I actually run these engagements.
AI security is not just cybersecurity with a new label
I usually start client conversations by resetting expectations, because two assumptions come up constantly. The first is that AI security is just application security or cloud security wearing a different hat. The second is that if the AI solution is hosted by a major cloud provider, the security responsibility largely shifts to the vendor.
In my experience, neither assumption holds up
Traditional controls still matter , identity and access management, network segmentation, encryption, vulnerability management, logging, secure development practices. None of that goes away. But AI creates attack surfaces that conventional applications simply don’t have. Attackers can manipulate training data before a model ever reaches production. They can extract proprietary models through repeated queries, exploit prompt injection in LLM-based applications, infer sensitive information from model outputs, or steer model behavior with adversarial inputs. And the shared responsibility model doesn’t save you here: your cloud provider secures the infrastructure, but the data you feed the model, the decisions it makes, and the way your business relies on it remain squarely your problem.
A few years ago these discussions were largely theoretical. Today, they’re techniques security teams need to understand now, because adoption is outpacing governance in almost every organization I see.
This is also where I’d caution against treating AI security as a standalone initiative with its own committee, its own policies, and its own reporting line. That structure rarely survives contact with reality. Extend your existing ISMS. If you’re aligned to ISO 27001 or NIST 800-53, map AI-specific controls onto that foundation. If you’re working toward ISO 42001 or using the NIST AI RMF, even better , but anchor it to what your organization already knows how to operate.
You can’t protect what you haven’t inventoried
The first exercise in any assessment I run is deceptively simple: identify every AI system currently in use.
Organizations consistently underestimate how hard this is. AI adoption almost never starts as a governed program. A business team experiments with a public generative AI tool. A developer wires an AI API into an application. Employees start leaning on AI features that shipped inside productivity software nobody reviewed. Six months later, AI is embedded across the organization and nobody can produce a complete list of where it lives or what data it touches. This is shadow IT all over again, except the shadow systems are making decisions and processing information.
Without that visibility, risk assessment is guesswork. So the inventory comes first, and for each system I want documented: what it is, who owns it, what business purpose it serves, whether it processes sensitive or regulated information (including CUI, if you operate in the defense industrial base), whether it depends on third-party AI services, and whether it supports critical business operations.
That last two items deserve more attention than they usually get. Third-party AI is where most of the real exposure sits for mid-size organizations , you inherit your vendor’s model behavior, their data handling, and their security posture, usually with a contract that says very little about any of it.
Not every AI system deserves the same controls
A mistake I see regularly is treating every AI application as equally critical. They aren’t, and pretending otherwise burns resources on low-risk systems while the genuinely dangerous ones stay under-governed.
An internal chatbot summarizing public documentation and an AI system informing healthcare decisions, processing financial transactions, or handling regulated data are not the same problem, and they shouldn’t be assessed as if they were. I take a risk-based approach, and it rests on three questions for every system in the inventory:
How severe would the consequences be if this system were compromised? How likely is an attack against it, given its exposure and the availability of attack techniques? And what would the business actually lose if that attack succeeded?
Severity is driven by the use case and the data. A model trained on sensitive personal data, regulated information, or anything supporting critical operations sits at the top of the stack, full stop. Likelihood is largely a function of attack surface , externally exposed endpoints, unrestricted query access, and weak rate limiting all raise it, and they’re all controllable. Impact is where I bring the business into the room, because security teams can estimate technical consequences but only the business can tell you what a poisoned model or a stolen one actually costs.
Score honestly, prioritize accordingly, and document the risks you consciously accept. An accepted risk with a compensating control and a signature is governance. An unexamined one is a finding waiting to happen.
Where this leaves you
If you take one thing from this: don’t wait for a perfect AI governance framework to arrive before acting. Start with the inventory, rate what you find, and extend the security program you already have. The organizations that struggle most with AI security aren’t the ones with immature programs , they’re the ones that treated AI as somebody else’s problem until an incident made it everyone’s.
The assessment isn’t a one-time exercise, either. Rerun it annually at minimum, or whenever a significant new AI capability enters the environment. AI adoption inside your organization is not slowing down. Your visibility into it shouldn’t either.
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