RegOps: The Trust Layer of the AI Economy

As AI takes on critical decisions, compliance can’t be an afterthought. That’s the role of RegOps: embedding governance directly into workflows so enterprises can move fast and stay within policy. ndaOK, led by CEO James Weir, is showing how RegOps works in practice—transforming contract workflows so speed and trust reinforce each other. Kasra Davar shares more on why RegOps is set to become one of the most important layers of AI infrastructure: https://lnkd.in/gR9gBAdi

Regulation is accelerating. AI is advancing. But the missing layer in #RegTech isn’t another platform or dashboard, it’s structured, machine-readable regulatory data. At #RegGenome, we believe compliance automation won’t scale until regulation itself is transformed into an infrastructure layer: 🔎Granular, obligation-level data consistently tagged and versioned 🔗 Interoperable with RegTech, GRC, and AI pipelines 🧩 Built for integration, not locked inside proprietary platforms This is why AI-ready data matters. Without it, AI outputs are inconsistent, untraceable, and can’t be trusted in compliance. With it, #RegTech providers can deliver defensible, scalable, enterprise-grade automation. We’ve outlined the case - and the ROI for solution providers - in our latest piece: Why AI-Ready Data Is Critical Now: https://lnkd.in/gBBwrPPw 👉 Swipe through the carousel for 4 reasons structured data unlocks RegTech growth. #RegTech #Compliance #AI #GenerativeAI #FutureOfCompliance #AIinFinance #RegulatoryData #ComplianceAutomation

The regulatory data ecosystem doesn’t have a tooling problem. It has a data problem. Unstructured regulation makes AI outputs unreliable. Structured, machine-readable data makes them scalable and defensible. That’s why RegGenome exists.   Why AI-Ready Data Is Critical Now: https://lnkd.in/eapXgv7d

When we talk to RegTech teams, the same issue comes up again and again: before you can build AI features, you’re stuck dealing with fragmented, unstructured regulation. Scraping regulator sites. Manually tagging obligations. Stitching together content from inconsistent sources. It slows roadmaps, blocks scale, and erodes client trust. This is the bottleneck. And it’s why AI-ready regulatory data matters. At RegGenome, we transform raw regulation into:  • Standardised, machine-readable data – consistent across jurisdictions  • Obligation-level tagging & versioning – fully traceable back to source  • Integration-ready formats – built for APIs, AI pipelines, and solution platforms The result?  ⚡️ Faster delivery  🌍 Easier scale 🔒 Defensible outputs  🤖 AI features that actually work in production We’ve laid out the case in our latest blog: Why AI-Ready Data Is Critical Now. https://lnkd.in/e9gzCYfq

Our applied AI is making serious progress towards machine readable rulebooks. if you are interested in financial regulations this is worth taking a look at...

I think Mark Johnston put this very well in his own Linkedinpost. RegTech hasn't got a tooling problem. It's never been easier to build powerful applications or solutions. It does however have a data problem - regulation as authoritative, portable, structured data that is legally safe to use is the missing infrastructure that is keeping so much of regulatory compliance from being automated. Without structured data, all those fancy new GenAI models will remain expensive at large scales, unpredictable or inconsistent in their output and non-transparent. Without information structures built to be jurisidiction agnostic and interoperable to the extent possible, firms will be stuck without a single source of truth, duplicating content in silos. Without regulators publishing at least a base data layer digitally, under reasonable licensing terms, vendors will either have to go through disproportionate pain and expense to source ethically or pay a premium to content providers who already have (RegGenome will happily take your money). All the while regulators will remain locked in an arms race against hard-to-detect, unaccountable scraping shops while LLMs slowly become the de-facto front-end to all their rules. There is a better way to do all of this. Give us a ring and we'll discuss.

Here’s the mindset shift: transparency isn’t a risk to manage; it’s an operating system for AI. The UK’s Algorithmic Transparency Recording Standard (ATRS) Hub shows how to make that real. It gives teams a simple pattern: a tiered public record (plain-English summary + deeper technical detail), clear ownership, and a shared repository of real deployments. Result: faster accountability, fewer FOIs, better supplier conversations, and less folklore in governance. Browse the live records and you’ll see practical AI in production: organ allocation at NHS Blood & Transplant, casework support at DVLA, fraud risk triage and policy assistants in central government. Each entry sets out purpose, data, model details, human oversight, risks and mitigations enough for scrutiny without giving away IP or creating security debt. Why COOs, CTOs, and Legal should care: ATRS is now mandatory for government departments and many ALBs, with a scope and exemptions policy that focuses on tools influencing decisions about people. Even if you’re a university, charity, or research infrastructure, adopting the template voluntarily is low-cost, high-signal: it aligns procurement, clarifies accountability (SROs, SPOCs), and builds public trust before the headline. How to adapt this in your organisation: - Start with a one-page “Tier 1” summary for every AI tool touching users. - Maintain a reusable “Tier 2” pack: rationale, datasets, model/versioning, evaluation, safeguards, DPIA links, and update triggers. - Publish to a single registry, reviewed by your governance forum; link it from service pages. - Bake transparency asks into supplier contracts from day one. If privacy-by-design is your promise, ATRS-style transparency is the proof. It turns “explainability” into a habit you can ship. Have you tried an ATRS-inspired AI register in your team? What made it stick, templates, sponsorship, or supplier leverage? #algorithm #transparency #datagovernance #responsibleAI

AI Act -- Diligence & the First 100 Days in Industrial AI The EU’s AI Act is now in force. Key dates are set: 1 Aug 2024 (entry into force), 2 Feb 2025 (prohibitions & AI-literacy start), 2 Aug 2025 (GPAI obligations), 2 Aug 2026 (full application for most rules), with an extended 2 Aug 2027 date for high-risk AI embedded in regulated products. There is no grace period to “wait and see.” What I examine in industrial deals 1) Value and risk in one pass Map where AI touches safety, quality, and customer trust. Flag “high-risk” use cases early—especially those embedded in regulated equipment or production processes. 2) Model governance and the vendor stack Demand model cards, training-data provenance, and bias/robustness evaluations. Where GPAI is in the stack, verify the Act’s transparency and copyright duties—including the public summary of training content that the Commission now expects from providers. 3) Plant reality In brownfield environments the weak link is usually OT, not the model! Baseline against IEC 62443 before scaling pilots. Your first 100 days: Weeks 1–2: name a single accountable executive; inventory use cases, vendors, and data flows. Weeks 3–6: classify use cases to AI-Act categories; stand up a lightweight model registry; identify/closeOT gaps. Weeks 7–12: issue one board-level policy, a red-line vendor checklist, and rehearse incident/recall per post-market monitoring duties. If you want my one-pager, comment AI-DD and I’ll share the diligence + 100-day checklist. #PrivateEquity #IndustrialAI #AIAct #Governance #OTSecurity

Your AI isn’t “experimental” anymore. When the EU AI Act bites, “we’re just testing” won’t save you. Do this now: ➛ Inventory & classify every use case: Prohibited / High-risk / GPAI / Low. ➛ Assign owners: Business, Model, Data, Risk. No owner = no go-live. ➛ High-risk basics: risk mgmt + QMS, data governance, human oversight, logging, evals, post-market monitoring. ➛ GPAI as deployer: usage cards, prompt/output logging, clear disclosures, copyright & content-safety controls. ➛ Vendors: add AI Act clauses, verify claims with tests, exit triggers, continuous monitoring. ➛ Evidence file: tech docs, data lineage, eval results, sign-offs. Audit-ready by design. ➛ Incidents: drift/security/change control with rapid notification paths. ➛ Plan in horizons: 90 days (inventory, owners, controls), 6–12 months (testing + contracts), phased deadlines thereafter. Quiet help: DM “AI Act” for a 1-page readiness map, or drop your #1 blocker (vendor risk, logging, oversight?) below. #AIGovernance #EU #UK #data #privacy

During my recent discussion with the d.velop team, Martin Testrot and Stefan Olschewski, they mentioned two challenges faced by customers when it comes to AI adoption.   Challenge 1: Data protection. In Europe, especially across regulated industries, customers hesitate to use AI because of compliance risks. Questions like “Will my data leave the EU?” or “Will the model train on my sensitive documents?” are top of mind.   Challenge 2: Unclear possibilities. Many organizations feel they must “do something with AI,” but don’t know what value it should deliver. The fear of missing out collides with uncertainty about practical use cases.   How d.velop addresses this: The image below shows their Retrieval-Augmented Generation (RAG) approach. Instead of sending all documents to an external language model, a retriever first selects relevant documents, checks whether the respective user has access to these documents and then sends this content to the LLM for answer generation. This avoids wholesale data uploads and builds user trust.   In practice, this means: -> Offering choice of AI hosting: Azure Open AI Services with EU-local deployment and contractual no-training clauses, or Open Telekom Cloud where even the LLM stays in a sovereign EU environment. ·-> Using RAG: answers come from permissioned content in the repository, not entire datasets. ·-> Transparent responses: every AI answer links back to the original documents, reducing hallucinations. ·-> Focusing on real use cases: invoice processing, contract risk checks, and assistants embedded in Teams.   This reframes AI adoption from hype into trust, sovereignty, and tangible outcomes.   How is your organization balancing compliance and clarity of value in its AI journey? To discuss with me, connect at: abhishekd@qksgroup.com #QKSGroup #ContentServices #ContentManagement #CSP #ECM #GenAI #ArtificialIntelligence #DataSovereignty #DataProtection #Compliance #RegTech #EnterpriseAI #AIAdoption #DigitalTransformation #ProcessAutomation #FutureOfWork