AI tools are making their way into organisations faster than most security teams can track. Writing assistants, meeting summarisers, data analysis bots, internal chat agents, many of them arrive quietly, adopted by teams looking to save time or work faster.
On the surface, these tools seem harmless. Most promise convenience, productivity, or “smarter” workflows. But once AI systems start touching internal data, the risk picture changes quickly. Unlike traditional software, AI tools absorb and transform data, and sometimes reuse it in ways that aren’t always obvious.
That’s why CISOs are increasingly being pulled into conversations that start with, “We’ve already been using this for a while.” By then, sensitive information may already be exposed, logged, or sent somewhere it shouldn’t have gone.
Before approving AI tools or discovering them after the fact, there are a few basic questions every CISO should be asking.
What Data Does the AI Have Access To?
This is the most important question, and often the hardest to answer clearly. Some tools only process what a user types in. Others automatically pull in documents, emails, chat histories, or system data. In many cases, users don’t fully understand what’s being shared and vendors don’t always spell it out in simple language.
CISOs need to know exactly what data is sent to the AI, whether that data is stored, and how long it sticks around. If the answer sounds vague, overly technical, or evasive, that’s a red flag.
Where Does That Data Go After It’s Processed?
Once data leaves your environment, control becomes difficult to maintain. Is it sent to a third-party cloud? Stored in logs? Used to improve the model? Transferred across regions? These details matter, especially for regulated industries or organizations operating across borders.
If a vendor can’t clearly explain where data is processed and stored, it’s impossible to assess compliance, privacy, or breach impact.
Is the AI Trained On Customer or Internal Data?
Some vendors say they don’t train models on customer data but that statement often comes with fine print.
CISOs should look for explicit guarantees: no training, no reuse, no blending of organizational data into shared models. Anything less creates long-term risk, even if the short-term benefits look attractive. Once data is used to train a model, it’s effectively impossible to take back.
Who Can Access the AI and Its Outputs?
Many AI tools live outside normal access controls. A user logs in with an email address, and that’s it. This raises a simple but important question: Can access be limited? Can it be revoked quickly? Are actions logged? Can you see who asked the AI what?
If sensitive prompts or outputs aren’t auditable, incidents will be harder to investigate and easier to repeat.
Can The AI’s Output Be Trusted In Context?
AI tools are very good at sounding confident, even when they’re wrong. CISOs should ask how the organization plans to use the output. Is it advisory? Does it influence decisions? Is there human review? Are users trained to question results?
The danger isn’t that AI makes mistakes, it’s that people stop double-checking.
What Happens When Something Goes Wrong?
Every system fails eventually. AI is no different. If the tool produces incorrect results, leaks data, or behaves unexpectedly, who notices? Who can shut it off? Who owns the response?
If there’s no clear answer, the organization is relying on luck rather than control.
Who is Responsible if The AI Causes Harm?
Finally, there’s accountability. If an AI tool leads to data exposure, compliance issues, or financial loss, where does responsibility land? On the vendor? On the organization? On the individual user?
CISOs should be wary of contracts that quietly push all liability downstream while keeping vendor responsibility minimal.
AI Adoption Needs Security at the Start, Not the Cleanup Phase
AI changes how data is handled, how decisions are made, and how mistakes propagate. When CISOs are brought in late, the conversation is usually about damage control. When they’re involved early, it’s about setting boundaries that let innovation happen safely.
The goal is to understand AI well enough to use them without surprises. Asking the right questions upfront is still the simplest and most effective way to do that.