The Red Hat survey published this April should alarm every managing partner, compliance officer and finance director in UK regulated practice. 87% of business IT leaders have deployed agentic AI systems. Only 25% have governance in place that would satisfy the SRA Code, FCA Consumer Duty PS22/9, or basic insurance intermediary standards. Worse: less than half know where their data actually lives once it enters these systems. For a law firm handling privileged client information, an insurer processing claims data, or an accountancy practice managing tax records, this is not a governance gap. It is regulatory exposure dressed up as digital transformation.

This pattern reveals something the industry has spent two years learning the hard way: AI adoption and AI management have nothing to do with each other. Firms deployed Copilot, Harvey, Luminance, or in-house chatbots because they solved an immediate pain point—contract review, legal research, document processing. Those tools work. But working tools and supervised tools are not the same thing. The result is a technology landscape where agentic systems are making decisions about data classification, client communication, and regulatory reporting while sitting in governance black boxes. The ICO's position on UK GDPR compliance has tightened. The EU AI Act's definition of 'high-risk' now extends to systems handling financial, legal and insurance decisions. The PRA's SS1/23 expects board-level understanding of model risk. None of this is optional. None of it can be retrofit into a Copilot instance deployed by an eager business unit six months ago.

Here is what Trovix believes needs to happen: you need to know exactly what your AI systems are touching, where that data goes, what they decide with it, and how you would explain that to a regulator or court. That requires three things most firms lack. First: data lineage visibility that actually works—not spreadsheet audits or quarterly compliance reviews, but real-time understanding of how information flows through agentic systems. Second: separation between the AI tools your teams use (which solve real problems) and the governance infrastructure that keeps them from becoming liability factories. Third: evidence. When the FCA or SRA asks you to demonstrate AI oversight, you cannot reach for a vendor's marketing deck. You need documentation of your controls, your testing, your escalation logic, your human review points. Trovix Audit addresses this directly because it was built by people who understand what 'demonstrate compliance' actually means in a regulated firm context. It is not about stopping people using AI. It is about making sure that when they do, you have a record of what happened and why.

If you manage a mid-market practice—500 to 2,000 fee-earners—you have six months before this becomes a board conversation you cannot avoid. Start now by mapping which AI systems your teams are actively using. For each one, ask: who decided to buy this, what data is it processing, who reviews its output before it touches a client file, and what happens if it makes a mistake? You will probably find gaps. That is normal. What matters is that you find them before a regulator does. If your current AI stack includes document processing, RAG-based knowledge systems, or client-facing chatbots, you need visibility of the entire chain. Trovix Sift and Trovix Aria solve specific technical problems, but they are useless without governance architecture. That is the work that matters now.

Source: Computer Weekly