Artificial intelligence is moving faster than our ability to keep up with it – let alone regulate it. Every week brings a wave of new tools that promise to make life easier, faster and smarter. However, as AI systems become more integrated into how we live, work and govern, they raise an urgent question: are we protecting the data that powers them or simply hoping for the best?

As a retired FBI Cyber Special Agent and now CEO of a public safety tech company, I’ve seen firsthand the full range of what AI can do. It can streamline systems, improve emergency response and help law enforcement act with more clarity and context. I’ve also seen how, when handled carelessly, AI can open the door to serious data privacy concerns which can erode public trust and increase skepticism in our society.

Let’s be clear: AI isn’t magic. It’s math fueled by data. And that data often includes some of the most sensitive, personal details of our lives, such as location history, mental health data, financial information and more. If we don’t treat data privacy as a foundational principle now, AI could become one of the greatest threats to digital privacy we’ve ever seen.

We Don’t Have a Data Problem. We Have a Stewardship Problem.

AI on its own isn’t the issue – it’s how it’s being implemented. Right now, far too many organizations—private companies and government agencies alike—are rushing to adopt AI without first answering basic questions: Where did the data come from? Who owns it? Is it accurate? Is it being used fairly, responsibly, and with consent?

When those questions go unasked or unanswered, it’s not just a missed opportunity—it’s a recipe for harm. Flawed algorithms make flawed decisions. And the very systems that are supposed to help us end up doing harm instead. If we want to get this right, we need to think of data stewardship as a responsibility, not just a technical challenge. That means:

• Only collecting the data that’s truly necessary
• Securing it with end-to-end encryption and modern cloud infrastructure (like AWS GovCloud)
• Ensuring users understand what data is being used – and that they’ve agreed to it

Transparency, consent and accountability need to be embedded into every phase of data use, not added in later as an afterthought. If any organization violates those principles, whether public or private, there should be consequences. People’s rights are on the line.

What Law Enforcement Can Teach Us

Public safety platforms like Flock Safety, FirstTwo, and ForceMetrics are all part of a growing ecosystem of tools that aim to help law enforcement agencies adopt AI technologies more responsibly. From AI-assisted dispatch to real-time location awareness, these platforms offer different solutions to help officers focus on high-impact situations without relying on overly broad enforcement. But even in that context, privacy isn’t a secondary concern – it’s central.

The potential for impact is significant. These tools can improve response times, reduce unnecessary use-of-force encounters and streamline operations across departments. But they also raise serious ethical and operational questions. How is data being shared across agencies? What kinds of third-party vendors are involved? Who has access to what, and when?

Many departments still rely on fragmented data systems and legacy software that leave data vulnerable to misinterpretation or exposure. Without the right safeguards, data can easily be misread, misapplied or leaked. That’s why the most effective implementations emphasize secure integration and transparency just as much as performance. If the goal is to improve public safety, trust needs to be part of the equation.

What Needs to Happen Next

The decisions we make now will shape how AI impacts our world for years to come. If we want AI to serve the public good – not erode it – we need to embed strong privacy principles into every layer of development. That means:

• Clear regulation: Clear standards at a national level around how personal data is handled and faster responses when violations occur. Europe’s GDPR is a useful benchmark, but it’s not enough on its own.
• Privacy-first design: Companies must adopt a mindset of proactive responsibility, not reactive compliance. If a product handles personal data, privacy shouldn’t be an add-on – it should be a foundation. Shared responsibility: Government leaders, developers, law enforcement and community advocates all need a seat at the table. Responsible AI isn’t just a technical challenge. It’s a societal one.

The Bottom Line: Data Privacy = Freedom

This isn’t just about technology. It’s about trust. In today’s world, data privacy is tied directly to our dignity and freedom. If we lose control of how our data is used, we risk losing much more than convenience – we risk losing autonomy.

We can do better, and we must. The longer we wait, the harder it will be to build systems that serve the public without compromising our rights. Let’s act now to build a future where innovation and privacy go hand in hand. That’s the future worth fighting for.

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Article via The AI Journal