Nikhil Rathi and Christine Lagarde have said what many regulators have been thinking for two years: the traditional rulebook cannot move fast enough to govern AI. Regulation takes years. AI development takes weeks. For mid-market legal, insurance, financial services and accountancy firms in the UK, this statement is not reassurance—it is a warning. The FCA Consumer Duty (PS22/9) and PRA SS1/23 already require you to understand and govern your use of AI systems. But those rules were written for a slower-moving world. The gap between what your firm is deploying and what the regulator can formally oversee has never been wider. That gap is where compliance risk actually lives.

This story is not about regulation failing. It is about the industry choosing to deploy AI faster than it can responsibly govern it. The pattern is now clear across financial services: firms use off-the-shelf LLMs (GPT-4, Claude, Gemini) without understanding model drift or hallucination risk; they treat RAG systems as reliable research tools when they are still fundamentally probabilistic; they assume that because a vendor claims GDPR compliance, their use of client data in training pipelines is safe. Meanwhile, the FCA cannot regulate what it cannot measure. Regulators are signalling not that they will relax oversight—they are signalling that firms must move first. Self-regulation, third-party audit, and governance frameworks (ISO 42001 compliance is coming) are no longer optional. They are the only available control.

Trovix's position is straightforward: firms should deploy AI systems only when they can answer three questions: What decision or task does this system perform? What are the failure modes—hallucination, drift, bias, data leakage? Who audits whether it is performing as intended? Most vendors will not help you answer these questions. Products like Harvey and Legora market themselves as 'AI-native' legal tools, but they do not expose their failure modes or audit trails. Luminance and similar document AI vendors provide better lineage and confidence scoring, but they remain opaque on training data and model updates. This is where a governance-first approach—built into your system design, not bolted on afterward—becomes essential. Trovix Audit exists because regulators will eventually ask: show us your decision log. Show us what your AI said. Show us why you trusted it. If you cannot answer those questions in July 2026, you will not be able to answer them in 2027.

Do this now: audit every AI system your firm has deployed in the last 18 months. Document what it does, who uses it, what data it touches, and what could go wrong. For client-facing systems—Trovix Reach for insurance claims handling, for example—you need an audit trail and a human override that actually works. For internal tools—Trovix Aria for due diligence or contract review—you need confidence scoring and the ability to trace why the system recommended something. For document processing—Trovix Sift for extraction and classification—you need test sets and monitoring for data drift. Then tell your regulator what you have done. The firms that wait for formal AI regulation to land will spend 2027 and 2028 ripping out systems and rebuilding. The firms that govern AI now will keep their systems running because they will have already answered the questions Rathi and Lagarde know are coming.

Source: CNBC