How Self-Sovereign Data ensures robust AI governance

By 2025, the world will generate over 180 zettabytes of data. This is not just a flood; it is an ocean of disconnected, siloed, and sensitive information. For enterprises in regulated industries like finance, healthcare, and government, this data deluge presents a dual challenge: immense potential for insight, and immense risk of non-compliance. Simply having data is no longer a competitive advantage. The real value lies in the ability to access, analyze, and act on it with precision and full control. Yet, traditional approaches require centralizing data, a process that is slow, costly, and often a non-starter under regulations like GDPR and HIPAA. This forces a compromise between innovation and security. This is the paradigm Scalytics Connect was built to break. We believe AI should be built where your data lives. Our platform enables enterprises to navigate the data ocean without moving it. Through a privacy-first architecture and federated learning capabilities, you can train models and derive insights across decentralized environments, ensuring data sovereignty is never compromised. At the heart of this capability is our Deep Search engine. It is more than a retrieval tool; it is an agentic, multi-hop reasoning system designed for complex enterprise questions. Deep Search provides verifiable, trustworthy answers grounded in your proprietary data. Every result is delivered with a trust score and a transparent audit trail, transforming your vast data lakes from a liability into a source of grounded, reliable intelligence. Stop navigating the data flood with outdated tools. Start building secure, scalable, and transparent AI that respects your rules and your privacy. Transform your AI infrastructure from a cost center into a strategic asset. Contact us to learn how Scalytics Connect can help you find value in the deluge. https://lnkd.in/gn_A_XnT #BigData #Scalytics #PrivateAI #FederatedLearning #DataSovereignty #EnterpriseAI #Compliance #GDPR #HIPAA

What if AI governance could learn from your organization's unique patterns and automatically adapt its policies? Traditional governance systems are static they implement rules and hope those rules remain relevant. But AI organizations aren't static. They evolve, scale, and pivot. Their governance should too. This is the core innovation behind PolicyCortex, launching Q3 2025: adaptive governance that gets smarter with your organization. Instead of rigid policies that break under pressure, you get intelligent frameworks that evolve with your needs. Imagine governance that: - Automatically updates security protocols based on threat patterns - Adjusts compliance requirements as regulations change - Optimizes resource allocation based on usage patterns - Learns from every deployment to improve future decisions This isn't governance automation governance intelligence. We've been testing these concepts through our consulting practice, helping enterprises build custom adaptive frameworks. The results consistently exceed expectations: governance becomes an enabler of innovation, not an obstacle to it. Ready to experience governance that thinks? PolicyCortex will make this accessible to every organization in Q3 2025. aeolitech.com/policycortex #PolicyCortex #AIGovernance #AdaptiveCompliance #IntelligentInfrastructure #CloudInnovation #TechBreakthrough #AIStrategy #EnterpriseAI #DigitalTransformation

AI in the public sector isn’t just about innovation — it’s about trust, access, and doing it right. On The Ravit Show, Ravit Jain had the opportunity to speak with Chris Brown, Public Sector CTO at Immuta, at the Data + AI Summit — and we delved into what it takes to bring AI to government agencies without compromising on security or compliance. We talked about: - The reality of provisioning secure data access for AI when sensitive data and clearance levels are involved - How to move from manual approvals to automated, policy-based access — especially when “speed of mission” is non-negotiable - Real-world challenges like multi-classification data, contractor access, and inter-agency collaboration - And how Immuta is helping government orgs compress timelines while still meeting the strictest zero-trust requirements Really appreciated Chris’s perspective — thoughtful, practical, and rooted in what government teams actually deal with. Link to the complete interview in the comments ⬇️ #data #ai #DataAISummit #Databricks #immuta #agentic #connectors #theravitshow

In today's hybrid, multi-cloud, and SaaS world, data is constantly moving and being accessed by countless users, applications, and services. This creates a massive attack surface where a single misconfiguration, excessive permission, or compromised account can lead to a catastrophic breach. This is why DSPM is not just a nice-to-have, it's a necessity. DSPM provides the visibility and automated controls you need to: ✅ Discover and classify all your sensitive data. ✅ Identify and correct misconfigurations and risky access policies in real-time. ✅ Remediate vulnerabilities across your entire data landscape. Vamsi Ponnekanti, CISSP Abhishek Banduni Amith Sarma - CISSP Sagar Kanase Bruce Nixon John Cunningham Emily Stuart #Securiti #DSPM #DataAccess #CISO

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.

Operational insights from our live GRC environments consistently demonstrate that drift patterns correlate strongly with environmental changes, which include regulatory updates, organisational restructuring, and market volatility. Here's Part 2 addressing alignment drift in AI, starting with implementation. Technical infrastructure requirements are substantial: multi-dimensional performance tracking systems, historical pattern analysis capabilities, and real-time constraint boundary monitoring demand computational resources that many organisations significantly underestimate during planning phases. From an industry perspective, we're observing early indicators that regulatory frameworks will evolve rapidly to address AI alignment concerns, particularly in governance-critical applications where system drift can create compliance violations or risk management failures. Proactively develop comprehensive alignment preservation capabilities through more reliable AI deployment. #AI #AlignmentDrift #Governance #Compliance #AIimplementation #GRC