AI is moving at breakneck speed. Tools are smarter, faster, and creeping into every corner of business life. But here’s the thing no one can ignore - privacy risks are moving even faster.
Think about it. Every time someone feeds sensitive data into a chatbot, or an AI system quietly combines datasets behind the scenes, new exposures appear. Regulators are already paying attention. Customers are starting to ask harder questions. Boards are nervous.

It’s no surprise that surveys show nearly three in four senior leaders now see privacy and security as the single biggest concern when rolling out AI. It’s not the tech itself that’s holding people back - it’s the fear of losing control over personal data.

Where AI projects stumble

We’ve seen the same pattern play out again and again:

•  A pilot system grinds to a halt because no one can prove the training data is free of personal information.
• Vendors argue about whether they are controllers or processors, leaving compliance teams stuck in the middle.
• Staff use unapproved AI tools, pasting in information that was never meant to leave the company.
• Auditors or regulators ask for evidence, and suddenly there’s nothing to show.

These are not theoretical problems. Regulators in Europe have already fined AI providers for weak privacy protections. And when a project is paused late in the process, the cost of fixing the gaps is far higher than doing the groundwork upfront.

Why this is a turning point

It’s tempting to treat privacy as a brake. Something that slows innovation down. But the opposite is true.

When teams have a clear way to identify risks early, map who is responsible, and show evidence of good practice, projects move faster. Leaders stop worrying about “what if” and start making real progress. Customers feel more confident using AI-powered services. And regulators see accountability, not excuses.

The need to be ready

So, what does “being ready” look like?

• Having a repeatable way to check whether an AI use case touches personal data.
• Knowing which vendors are in the chain and what their role is.
• Training staff on what can and can’t be fed into an AI tool.
• Using recognised standards — like ISO/IEC 42005 or the NIST AI risk framework — to back up your assessments.
• Being able to put a one-page summary in front of your board that shows risks, owners, and mitigations.

This isn’t about building a huge new process from scratch. It’s about putting simple guardrails in place now, so every future AI project can move with confidence.

Why this matters for your next board conversation

Right now, privacy is the single biggest board-level blocker for AI. If you can show that your organisation has a way to spot risks early, fix them quickly, and document the results, you’re not just staying compliant. You’re speeding up innovation.

The organisations that succeed won’t be the ones that wait for laws to catch up. They’ll be the ones that prepare now, with clear workflows, evidence, and trust built into every AI initiative.
Want the full picture?

This article only scratches the surface. We’ve pulled together a one pager that breaks down the risks, the new standards, and the practical steps you can take right away.

Download the "AI Impact Assessments: Making AI risks visible" playbook.