Vijay Raina stands at the intersection of enterprise technology and regulatory precision, offering a veteran’s perspective on how the “SaaSpocalypse” is reshaping the software landscape. As a specialist in software architecture and enterprise tools, he navigates the delicate balance between the rapid efficiency of generative models and the rigid demands of global compliance standards. This discussion explores the transformation of RegTech from simple workflow tools into deep systems of record where the ability to prove a result matters more than the result itself. We delve into the dangers of internal builds, the necessity of maintaining version control across thousands of iterations, and how the industry is moving toward a layered ecosystem where AI serves as the interface for maintained regulatory intelligence.
Generative AI can now replicate basic SaaS functionality like summarizing dense regulatory text or drafting impact assessments in minutes. How are specialist vendors pivoting to add value beyond these tasks, and what specific metrics define a “controlled regulatory operating capability” versus a simple conversational interface?
While it is true that AI can now summarize dense text in minutes, that speed is merely a slick surface that can hide a lack of depth. Specialist vendors are pivoting by focusing on the “hard part” that AI cannot solve alone: the ability to guarantee that every relevant regulator is being monitored across multiple jurisdictions. A controlled regulatory operating capability is defined by its ability to confirm which specific document versions were used and exactly who validated those findings. It is the difference between a fast, conversational guess and a defensible truth that can withstand the cold scrutiny of a multi-jurisdictional audit. The real value now lies in owning the context and the governance that surrounds the data, rather than just shuffling information between different systems.
While building an initial AI-generated tool has become significantly easier, the real challenge lies in safely operating subsequent versions every single day. What are the step-by-step requirements for maintaining a defensible audit trail, and how do you ensure that provenance is preserved when data moves through general-purpose models?
The initial thrill of launching an AI tool often masks the grueling reality of long-term maintenance; building version one is getting easier, but safely operating version 1,001 every single day is where the true operational cost sits. To maintain a defensible audit trail, a firm must be able to reproduce an independent chain of evidence that shows exactly how a result was derived months after the fact. This requires rigorous monitoring of sources and the preservation of specific document versions to ensure that fluency does not replace actual provenance. You must ensure that every piece of data moving through a general-purpose model is anchored to a system of record that captures the “why” and “how” of every decision. Without these steps, the “slick” answer provided by an AI is essentially useless when an auditor asks for proof a year down the line.
Organizations often consider building internal AI assistants to replace specialist software, effectively choosing to become their own platform operators. What hidden operational costs typically emerge during this transition, and how do firms ensure they are monitoring every relevant jurisdiction without the support of maintained regulatory intelligence?
When a company decides to build an internal assistant, they aren’t just creating a tool; they are choosing to become a full-scale platform operator, which brings a heavy burden of hidden maintenance and liability costs. They quickly realize that they must now own the context across every single jurisdiction and legal entity themselves, which is a monumental task. The stress of trying to monitor constant global changes without a foundation of maintained regulatory intelligence can lead to massive gaps in compliance. It is a transition that often underestimates the sheer volume of domain data and the constant updates required to keep the system from becoming a liability. Firms that go this route often find themselves buried under the weight of managing the very infrastructure they hoped to bypass.
The future of RegTech appears to be a layered ecosystem where AI serves as the interface while GRC platforms remain the systems of record. How do you manage the integration between these layers to ensure human sign-offs are captured, and what anecdotes can you share about the risks of thin “workflow-wrapping” in regulated industries?
In this emerging layered ecosystem, we use AI as the intuitive, conversational interface while keeping GRC platforms as the uncompromising, rigid systems of record. This integration ensures that every human sign-off is captured and locked into a framework that defines clear ownership and governance. Thin “workflow-wrapping” is a dangerous trap where software looks modern but lacks the hard-to-reproduce assets needed for high-stakes compliance. I have seen instances where firms rely on these shallow wrappers, only to find they have no way to reconstruct the evidence trail when a regulator demands a detailed history of a specific impact assessment. AI makes the building process cheaper, but it makes the trusted, defensible operation of that software more valuable than it has ever been.
AI is currently being used to sharpen classification and mapping, which raises the bar for what is considered acceptable software. In an environment where fluency does not equal accuracy, how should firms validate the findings of an AI agent, and what specific evidence must be reproducible for an auditor a year later?
Fluency should never be mistaken for accuracy, and firms must validate AI findings by treating the model as a powerful assistant rather than a final authority. Validation requires concrete evidence of monitoring sources and controlling workflow approvals to ensure the agent has not missed a critical update or hallucinated a requirement. For an auditor to be satisfied a year later, the evidence must be entirely reproducible, showing exactly which document version triggered a specific action and who in the organization gave the final approval. It is about creating a digital paper trail that feels as solid and immutable as the physical ledgers of the past. If you cannot reconstruct the “who, what, and why” of a decision twelve months later, then your software has failed its most basic regulatory purpose.
What is your forecast for RegTech?
I believe we are entering a period where AI will effectively kill off “bad SaaS” built on thin workflow-wrapping, but it will simultaneously make specialized, defensible platforms more essential than ever. We will see a permanent shift toward layered architectures where general AI assistants serve as the front-end, while specialist vendors provide the underlying, maintained intelligence. This evolution will raise the bar for software accuracy, moving the industry away from simple information shuffling toward high-governance systems. Ultimately, the future of RegTech belongs to those who can marry the incredible speed of AI with the slow, deliberate requirements of global regulatory proof.
