Artificial intelligence has long played a role in regulated financial services, with machine learning, automation and pattern recognition already embedded across many operational systems.
According to Red Oak, what has changed is the growing assumption that AI should now sit at the centre of every compliance workflow. In highly regulated environments, that expectation can quickly become problematic when it is driven by enthusiasm rather than a clear understanding of regulatory obligations.
Compliance has never been about prediction or probability. At its core, it demands precision, consistency and auditability. A compliance decision must be repeatable and defensible, producing the same outcome when reviewed tomorrow as it does today. When systems return inconsistent answers, or fail to explain how a conclusion was reached, the result is not innovation but regulatory exposure.
This is where many so-called AI-native platforms fall short. By starting with the model and attempting to layer compliance on top, they reverse the logic required in regulated environments. AI-first thinking may suit experimentation, but it is fundamentally misaligned with the realities of compliance, where the correct approach must always be compliance first.
That does not mean AI has no place in compliance workflows. Used correctly, it can deliver meaningful value. Approximation is often helpful at early stages, such as scanning large volumes of documents, identifying potential disclosures, or surfacing anomalies for further human review. These are areas where speed and pattern recognition enhance efficiency without undermining control.
However, there are clear points where approximation is unacceptable. Final approval decisions, regulatory recordkeeping, books and records obligations, and end-to-end audit trails all require deterministic outcomes. In these contexts, issues such as hallucinations, model drift or inconsistent outputs are not minor technical flaws; they represent direct regulatory liabilities. The challenge is not AI itself, but the absence of clear boundaries and controls around how it is deployed.
Red Oak addresses this challenge through its concept of compliance-grade AI. Rather than systems that “learn” compliance behaviour over time through opaque processes, compliance-grade AI is designed specifically to perform within regulatory frameworks. Every interaction is captured and linked to the compliance record, every output is auditable and reproducible, and every workflow incorporates governance, controls and human validation where required. Importantly, deployments are aligned with a firm’s existing policies, rather than forcing organisations to reshape governance around the technology.
During a recent Red Oak fireside chat, CTO Rick Grashel likened AI governance to aviation safety. No aircraft operates without redundancy, backup systems and a black box, yet many AI tools entering compliance workflows lack comparable safeguards. Without validation steps, configurable workflows and fallback mechanisms, AI can quietly compound risk instead of reducing it.
The most significant risk facing compliance teams today is not failing to adopt AI, but adopting it too quickly under pressure. AI should enhance proven compliance processes, not introduce new forms of uncertainty. For more than 15 years, Red Oak has focused on delivering compliance-grade outcomes, treating AI as another carefully governed tool rather than a shortcut.
As AI becomes an unavoidable part of compliance roadmaps, the critical question is no longer whether to use it, but whether firms can explain, defend and govern it when scrutiny matters most.
Copyright © 2026 RegTech Analyst
Copyright © 2018 RegTech Analyst
