For most of its history, RegTech has aimed its intelligence at the outside world. Transaction monitoring watched customers, screening tools checked names, and onboarding systems verified identities, while the compliance officer sat above them all, interpreting policy and making the judgement calls. A newer class of AI-assisted tools inverts that relationship, pointing the intelligence at the compliance function's own work and letting officers resolve questions about policy and permissibility in real time. The example driving the current discussion is a class of always-on AI policy tools set out in an early-2026 analysis from the compliance-software firm MCO (MyComplianceOffice). Beyond simply retrieving a policy, these tools keep a structured log of every interaction, so a firm can reconstruct afterwards who raised a given query, when they raised it, and which internal document the answer was drawn from.
The promise is to convert an activity that has always been informal, the ad hoc question answered in passing and rarely written down, into a documented and repeatable control that a firm can point to later. Put that way, the appeal to a compliance leader is obvious. One of the perennial weaknesses of a compliance function is that its most important product, guidance, is often its least documented. When a regulator or an internal investigation asks what the business understood a rule to be, and who told them, the answer frequently lives only in the compliance officer's memory. A system that records every policy query and the source behind every answer turns that fog into evidence.
This sits within a broader 2026 shift in which AI has moved from proof-of-concept to operational tooling across RegTech. Vendors increasingly point to gains in efficiency and productivity, though much of that evidence is self-reported, which makes independent validation difficult.
The same capability that makes these systems compelling also exposes their central weakness. An audit trail can show that the AI produced an answer, when it did so, and which documents it drew on. It cannot tell you whether the answer was right, or who owns the decision that followed. Picture a compliance officer asking whether a transaction falls within policy. The AI returns a confident response, supported by references from the firm's policy library, and the transaction proceeds. Weeks later the interpretation turns out to be wrong. The system can reconstruct every step in that chain, but it cannot answer the question that matters next: who was responsible for the mistake? These tools make the compliance function's reasoning easier to inspect, yet they leave accountability exactly where it has always sat, with the people expected to exercise judgement.
The meta-compliance problem
This is where the conversation becomes more interesting than the product pitch. Vendors understandably emphasise the visibility these systems create: guidance that once lived in emails, chat messages or hallway conversations becomes searchable and auditable, which is a genuine improvement. But once AI starts answering policy questions at scale, the technology itself becomes part of the control environment, and that changes the discussion. Every compliance control needs an owner, a way to test whether it performs as intended, clear criteria for when it has failed, and a means of showing that weaknesses are found and corrected. An AI assistant should be held to that same standard.
The peripheral questions quickly turn operational. Who reviews the quality of the AI's answers? How often is the underlying policy library refreshed? What happens when guidance rests on a rule that has since changed? If the model reaches the wrong conclusion, was the cause incomplete source material, a flaw in the model's reasoning, or an error in human judgement? Those distinctions matter the moment regulators begin examining how AI supports regulated decisions. They are no longer hypothetical: frameworks such as the EU Artificial Intelligence Act (Regulation (EU) 2024/1689) are steadily shifting attention toward governance, oversight and accountability for AI systems, with obligations on risk management, record-keeping and human oversight for higher-risk uses. As organisations bring AI into compliance workflows, they will increasingly be expected to show not only what the technology can do, but how it is supervised. Compliance functions have spent years building controls to govern the rest of the business; they are now beginning to build controls around their own AI tools. The technology may answer the policy questions, but responsibility for governing it still rests with compliance itself.
Regulatory implications
For chief compliance officers and heads of policy, the practical challenge is to treat these systems as regulated components of the compliance framework rather than as productivity software. That starts with ownership: someone must be accountable for the tool's performance, responsible for validating the quality of its outputs, and empowered to suspend or correct it when problems emerge. Governance cannot be bolted on once the technology is already in production. Maintaining the supporting knowledge base deserves equal attention, because policies evolve, regulatory guidance changes and internal procedures are updated, and an AI assistant is only as reliable as the information it can draw on.
Organisations should also make expectations explicit for staff: an AI-generated answer can support a decision, but it can never replace professional judgement or transfer accountability away from the person making that decision. The audit trail records what happened; it does not confirm that the outcome was correct. For firms operating under the EU AI Act and similar frameworks, these tools should already sit inside broader AI-governance programmes, and procurement teams should look past demonstrations of speed to ask about traceability, validation, model monitoring and post-incident investigation. The goal is for governance to evolve alongside the capability, rather than catching up only after the technology has become part of critical decisions.
Sources
- EU Artificial Intelligence Act, Regulation (EU) 2024/1689 (governance, record-keeping and human-oversight obligations, including Articles 9, 12 and 14): eur-lex.europa.eu
