How Businesses Should Be Thinking About AI Security in 2026

jordyguillon
Jan 19
3 min read

AI is already a well-worn topic, and none of this is new. Still, we are already well into 2026, and AI usage continues to accelerate. Many businesses are adopting tools faster than they are thinking through the risks. This is worth revisiting, because the consequences of getting it wrong are still being underestimated.

In most organizations, AI did not arrive through a single decision or strategy. It showed up gradually through features in existing software, browser extensions, and productivity tools. Over time, it became part of everyday work without anyone explicitly owning how it should be used.

That lack of intention is where most of the risk sits.

Why securing AI deployments is now a business issue

Problems with AI rarely start as obvious security incidents. More often, they begin with small decisions made for convenience. Client data gets copied into a tool to save time. Outputs get reused without being validated. AI features get enabled without understanding what data they can access.

These choices usually feel harmless in isolation. The impact tends to surface later, when incorrect information is relied on, confidential data is exposed, or a regulator or client asks uncomfortable questions. At that point, the issue is no longer theoretical.

AI is no longer a side experiment. It is embedded in day-to-day operations, which means it should be treated like any other system that affects risk, compliance, and trust.

The assumption that AI tools are secure by default

There is a quiet assumption that using a well-known AI platform means security is already handled. That assumption does not hold up in practice.

Vendors focus on securing their infrastructure and models. They do not control how your staff uses the tool, what information is entered, or how outputs are applied in real work. They also do not understand your regulatory obligations or client commitments.

This mirrors earlier cloud adoption patterns. The platforms themselves were secure, but poor usage and unclear rules created most of the problems. Securing AI deployments requires recognizing that responsibility sits with the business, not the vendor.

Data boundaries matter more than AI features

The most effective control in any AI deployment is deciding what data the tool is allowed to touch.

Client information, financial records, internal strategy documents, credentials, and sensitive communications all carry different levels of risk. Treating them the same simply because an AI tool is convenient creates unnecessary exposure.

This is not about blocking AI. It is about being deliberate. If a business cannot clearly explain which data is acceptable to use with AI and which is not, the deployment is already on shaky ground.

Clear boundaries do more to reduce risk than most technical controls.

Visibility works better than bans

Some organizations respond to AI risk by trying to prohibit its use altogether. In reality, that approach rarely succeeds.

When AI is banned without realistic alternatives, staff still use it, just without guidance or transparency. That makes risk harder to see and harder to manage.

A more practical approach is visibility. Approved tools, clearly defined use cases, and simple rules around data use create a safer environment than blanket restrictions. This is consistent with how effective IT governance works in other areas of the business.

Human review is still part of a secure deployment

AI output can be helpful, but it is not inherently reliable. Errors are often subtle and presented with confidence, which makes them easy to miss.

In financial, legal, or advisory contexts, those errors can have real consequences. Human review remains a necessary control, not because AI is useless, but because it lacks context and judgment.

Removing review too early does not eliminate risk. It shifts it to places where it is harder to detect.

What this means in practice for smaller firms

Most small and mid-sized businesses do not need elaborate AI governance frameworks.

They do need someone accountable for how AI is used.
They need clarity around which tools are approved and what data can be used with them.
They need basic awareness of where AI is already embedded in their workflows.
They need occasional review to ensure usage still aligns with business goals, client
expectations, and regulatory obligations.

None of this is particularly complex, but it does require intention.