The audit trail you can't see is the one that will destroy you

A widow remarked of her husband: "Without these conversations with the chatbot, my husband would still be here."

That quote opens a Harvard Business School working paper on the safety of generative AI. It's describing a real case—a father of two who committed suicide following conversations with an AI chatbot.

Here's my question: can the company that built that chatbot reconstruct exactly what was said in those conversations? Can they prove what their system did and didn't do? Can they demonstrate they met their duty of care?

I suspect they can't. And that's a problem that goes far beyond this one tragedy.

The black box problem

The "deep learning or black box nature" of generative AI models makes it hard to predict their responses. That's from the same Harvard paper. But the black box problem isn't just about unpredictability—it's about accountability.

When something goes wrong, you need to be able to answer:

• What exactly did the user say?
• What exactly did the AI respond?
• Were there warning signs that were missed?
• Did the system behave according to its design?
• What could have been done differently?

Without comprehensive audit trails, you can't answer any of these questions. You're left with "we don't know what happened" while facing grieving families, regulators, and your own conscience.

The regulatory reality

The AI governance market is projected to grow from $228 million in 2024 to over $4.8 billion by 2034—a 35% compound annual growth rate. That growth is driven by regulation.

The EU AI Act requires transparency and audit capabilities for high-risk AI systems. California has passed multiple laws requiring oversight of AI in healthcare contexts. Massachusetts is proposing legislation requiring continuous monitoring by licensed professionals for any AI used in mental health services.

If you're deploying AI in sensitive contexts without robust audit infrastructure, you're building a liability time bomb.

What real audit capability looks like

At NovaHEART, we built the Sacred Registry specifically because we saw this coming.

Immutable logging. Every interaction is recorded in a tamper-evident, hash-chained format. You cannot alter historical records without detection.

Complete reconstruction. We can rebuild any conversation exactly as it happened—what the user said, what the AI responded, what safety systems triggered, what resources were presented.

Forensic timestamps. Not just "when" but precise sequencing that shows the exact order of events, including system decisions made between user messages.

Context preservation. We capture not just the words but the state of the system—what safety scores were active, what policies were in effect, what the AI "knew" at each moment.

The business case for accountability

This isn't just about avoiding lawsuits—though it is about that too. It's about building systems you can actually trust.

Internal audit functions are increasingly demanding AI audit capabilities. Deloitte, EY, and other major consultancies are building entire practices around AI assurance. The question boards are asking isn't "do you have AI?" but "can you prove your AI is safe?"

According to ISACA, there's currently no universal framework for AI auditing. That means organisations deploying AI must build their own accountability infrastructure—or hope regulators don't come asking questions they can't answer.

The organisations that will survive

The companies that thrive in the next decade of AI deployment will be the ones that treat accountability as infrastructure, not afterthought.

They'll be able to:

• Demonstrate to regulators exactly how their systems work
• Prove to insurers that risks are managed
• Show stakeholders that governance is real
• Reconstruct any incident with forensic precision
• Learn from failures systematically

The companies that can't do these things will face increasing liability, regulatory pressure, and loss of trust.

Building for the inevitable

Here's my honest advice: if you're deploying AI systems that interact with people—especially vulnerable people—build your audit infrastructure now.

Not because regulators are requiring it (though they will). Not because your legal team is worried (though they should be). But because when something goes wrong—and with AI at scale, something will go wrong—you need to be able to look at yourself in the mirror and say "we know exactly what happened, and here's what we're doing about it."

That's what accountability actually means. Not a policy document. Not a checkbox. The ability to answer hard questions with hard evidence.

This is why Sacred Registry exists. Because the audit trail you can't see is the one that will destroy you.