AI assurance 101

Trust matters for all AI systems—lessons from AI assurance

“We cannot deploy AI at scale until we have robust, effective assurance processes, and definition of good processes will give significant speed and quality advantages in AI development..” - Helsing, a German Defense/AI Unicorn.

I like that idea; effective assurance in AI delivers great quality and great quality pairs with more speed and speed in full-scale deployments. People like to make the argument that quality and speed are trade-offs, but seriously when you imagine a terrible product manager that sucks at his job, do you really think he’s gonna be fast?

Ok, so this sentence stuck with me while I was researching “AI assurance” a concept I stumbled over a while ago. I’ve been aware of AI alignment, but not so much assurance. In particular of course, I’m always interested whether there’s something I can learn from a field that’s only in my peripheral vision.

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If you only have 5 minutes: Key Takeaways

• AI assurance is about proving that AI systems are safe, ethical, and effective—a step beyond just designing them well.

• The EU AI Act and other regulations will require companies to certify AI before deployment, making assurance essential.

• Industries like finance and defense already lead AI assurance efforts with real-time testing, audits, and human oversight.

• The "Three Lines of Defense" model (engineers, compliance, and regulators) ensures accountability in AI failures.

• AI assurance differs from AI alignment—alignment is about embedding human values, while assurance verifies if those values hold in practice.

• Transparency builds trust, with tools like model cards helping users understand AI decision-making.

• Ignoring AI assurance can lead to biased systems, public backlash, and regulatory penalties (as seen with Amazon’s biased hiring AI and the UK’s flawed grading algorithm).

• The field is evolving fast—companies that prioritize assurance now will have a competitive edge.

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If you have more time, continue! Here’s what I’ve learned so far.

1. What the hell is AI Assurance anyway?

Google wasn't helpful at first. Mostly vague definitions of AI being safe and ethical. One definition stuck, though: "AI assurance is about building responsible and trustworthy AI."

I guess that's better than nothing.

Let’s see whether I can place this article right there :-)

2. Can we find a clearer definition?

Assurance, generally, means confidence. So, AI assurance is all about how confidently we can guarantee an AI system is safe, ethical, and effective.

The Global Digital Foundation simplifies it to checking systems and providing proof they do what we say they'll do. Different countries, different rules. The UK is doing something different from the US. We’re not yet clear on whether AI assurance is more about monitoring or the tools it takes to deliver those systems.

3. Why does it matter to me?

I’m in Europe, and the EU AI Act is already coming. Long story short: if you're building AI, you’ll have to prove it doesn’t screw people over—technically, ethically, or legally.

On the other side of things is the simple fact that people tend to mistrust automated systems, so even if I’m not building potentially lethal, harmful, or other kinds of “risky” systems, trust matters a ton. Assuring users that AI works “well.”

4. How are companies already "assuring" AI?

Turns out, sectors like finance and defense have been forced into this first. Finance doesn't want rogue algorithms ruining markets, and defense, well, nobody wants a drone going Terminator.

Tools they've come up with:

• Real-time adversarial testing: basically, actively trying to trick AI into making mistakes (like hacking an AI by confusing a stop sign with graffiti).

• Human-in-the-loop protocols: keeping a human involved so there's someone to blame (and to stop disasters).

• Post-execution audits: reviewing what the AI did after the fact, making sure no one got unfairly hurt (think credit scores, but with AI).

5. What are the "Three Lines of Defense"?

Borrowed from finance, the 3LoD model separates responsibilities:

• First line: Engineers and developers (the ones actually building AI).

• Second line: Independent oversight (auditors, compliance folks).

• Third line: Regulators and external auditors (the government, basically).

It sounds bureaucratic, but it at least makes clear who’s accountable if an AI goes rogue. It’s a great model to keep in mind for any AI really. There should be at least 3 lines of defense in any autonomous AI that preserve the trust the user has in the system.

6. Are there people actively trying to break AI Assurance?

Obviously. Every system has attackers:

• Self-driving cars got fooled by stickers on stop signs (true story).

• Chatbots like ChatGPT get tricked into bypassing their own ethical limits (the infamous "DAN" jailbreak).

• Algorithmic trading bots exploit each other.

It's basically a never-ending cat-and-mouse game. Same thing is true for any AI system we’re building today.

7. What’s coming next in AI Assurance?

Regulations are getting serious. NATO is developing its own AI certification. The EU AI Act will force companies to certify high-risk AI (think medical AI, hiring software, credit scoring systems) before they can sell in Europe. Certification will mean showing your AI isn’t biased, unsafe, or ethically sketchy.

8. Wait, isn’t this just AI Alignment?

Close, but not exactly. AI Alignment is about embedding human values directly into AI (teaching it morals, basically). AI Assurance is about verifying these morals actually hold up in the real world.

Think of alignment as telling your kid to behave like you think they should. Assurance is making sure they actually did (and other things!).

9. What happens if companies ignore AI Assurance?

Bad stuff, apparently:

• Amazon had a hiring AI biased against women. PR nightmare.

• The UK government had a grade prediction AI that unfairly downgraded students during COVID. Angry teens stormed the streets. The algorithm was canned.

Ignoring assurance seems risky (to say the least).

10. Why does transparency matter?

People won’t trust AI if it’s a black box. Companies now publish "model cards" (like AI nutrition labels) showing how AI was trained, its biases, strengths, and weaknesses. Transparency = trust. IBM, Microsoft, and Google are already on it.

AI assurance seems like it’s developing a good framework coming from an already high-trust-necessary area that makes the transparency process faster.

So, what now?

Honestly, AI Assurance is still messy, complicated, and evolving fast. But it's clear if you're building AI, you can’t just wing it anymore. Regulators, customers, and even your competitors will soon demand proof that your AI isn't biased, dangerous, or untrustworthy.

I’m going to dig deeper into mapping my own AI projects against frameworks like NIST’s AI RMF. If you’re building AI, too, you might wanna join me.

I do have a much longer brief I condensed to create this blog post, feel free to take a look into the very much longer version of “AI assurance 101.”