Decoding AI Regulation: What Compliance Officers Need to Know Today
- Nova Sinclair
- May 29
- 3 min read
A few months ago, during a strategy session I led for a compliance team navigating digital transformation, someone paused mid-discussion and asked, “What’s the real difference between the new AI laws and everything we just addressed in our IoT strategy?”
It was a good question—and one I think more of us need to be asking.
Because whether we’re talking about AI systems in hiring or smart sensors in supply chains, the message from regulators is becoming clear: Innovation without governance is a liability.
And that’s where we come in.
The Pattern Is Clear: Compliance Must Shift Left
In my April column for CEP Magazine, I explored the explosion of IoT technologies and how compliance teams must proactively engage in device-level innovation—from mapping data flows and securing endpoints to embedding privacy into design. These aren’t just tech problems. They’re strategic compliance challenges that require active engagement, not passive oversight.
What we’re seeing now with AI regulation is a direct extension of that trend.
From the EU AI Act to U.S. Executive Orders and international guidance, compliance is being called—not to understand every algorithm—but to help answer familiar questions:
Who is accountable when automation makes a bad call?
How do we govern high-risk decisions driven by machines?
Can innovation be both fast and responsible?
The answer? Yes—but not without us.
What AI Regulation Is Really About
Let’s get specific.
🔹 The EU AI Act
This landmark law categorizes AI systems by risk. If your business touches Europe and uses AI in recruitment, financial scoring, or biometric ID, you’ll soon need to prove due diligence—bias assessments, human oversight protocols, and transparency mechanisms included.
🔹 U.S. Activity
The U.S. has taken a sector-based approach. Federal agencies like the FTC, EEOC, and DOJ are making it clear: existing laws still apply, even when decisions are automated. The previous administration's Executive Orders emphasize civil rights, consumer protection, and national security as non-negotiables for AI development.
What This Means for Compliance Professionals
Let me say this clearly: Compliance doesn’t need to become an AI center of excellence. It needs to become the conscience and compass of innovation.
Just like with IoT—where printers, projectors, and biometric scanners introduced privacy risks many companies overlooked—AI systems are being embedded in ways that often escape compliance visibility.
Think:
HR software that “scores” candidates
Risk models that trigger automatic audits
Chatbots interacting with customers and collecting sensitive data
Each of these presents a compliance opportunity—or a compliance risk.
How to Lead Right Now
Just as I advised in my IoT column, leading with curiosity and cross-functional collaboration is the key. Here’s how to adapt that same model to AI:
✅ Audit AI Use Cases
Don’t wait for a regulatory audit. Conduct your own. Where is AI being used in your business? Who owns it? How is it being monitored?
✅ Embed Governance into Innovation
Don’t bolt compliance on at the end. Co-create early. Ask: “What are the ethical risks of this model? Who’s accountable for its outcomes?”
✅ Treat Data as a Strategic Asset
Whether from sensors or algorithms, if data is flowing, governance must follow. Align your AI data flows with your privacy and data retention strategies—especially where personal or biometric data is involved.
We’re Not Behind—We’re Being Invited to Lead
The expansion of AI governance isn’t a disruption to compliance—it’s an invitation. An invitation to lead with clarity. An invitation to position compliance not as a barrier to innovation—but as a builder of trust.
That was true when I wrote about IoT. It’s even more true now. We have the language. We have the mindset. And we have the track record of protecting people, systems, and reputations under pressure.
So let’s lean in.
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