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Exciting news 🎉 K2view Version 10 is our biggest release ever, and it’s coming out today. Watch it in action 👉 https://lnkd.in/emeVXx3V This release makes enterprise data compliance faster, smarter, and safer than ever. It combines agentic automation, AI guidance, governance, and ease-of-use into one role-based platform for synthetic data generation, test data management, and enterprise data masking.

Most data compliance conversations focus on policies, but the actual problem is much more operational: data copies. In our 2026 State of Enterprise Data Compliance survey, companies reported keeping an average of 29 full copies of production DBs across non-prod and analytics environments. And in companies with 10K+ employees, that number rises to 55 copies (!) That means sensitive data isn't sitting in one controlled place. It's spread across dev, test, analytics, and other downstream environments. Crucially, the report highlights that these environments consistently lack the rigorous controls applied to production. The issue isn't just overexposure. It's also cost, complexity, and loss of control. Every extra copy expands the attack surface, strains privacy controls, and makes compliance harder to prove. See exactly how enterprise leaders are fixing this compliance gap. Link to the report in the 1st comment 👇 #DataCompliance #DataPrivacy #EnterpriseData #DataSecurity

Only 2% of enterprises say their AI environments fully meet data privacy requirements. That is the lowest score of any environment in our 2026 State of Enterprise Data Compliance survey. AI is moving into the enterprise fast. But compliance is not keeping up once sensitive data leaves core production systems. And this is not a theoretical concern. In the same research, only 13% say they have enforced technical controls to prevent sensitive data from entering GenAI or LLM systems. That leaves a large gap between AI adoption and actual data protection. You cannot govern enterprise AI with policy alone. If technical controls do not follow sensitive data into AI workflows, compliance starts to break down. See the full benchmark data here: https://lnkd.in/ePePbyJy

AI is making development faster. But test data is becoming the new bottleneck. As teams adopt AI coding assistants and accelerate software delivery, one challenge is becoming harder to ignore: how do you validate faster when the right test data still takes too long to access, prepare, and trust? That’s the AI Velocity Paradox. Faster code generation doesn't mean much if QA teams are still waiting for realistic, compliant, and consistent test data to test complex business flows properly. Ahead of EuroSTAR Conference in Oslo this June, we’re sharing our perspective on why test data is now becoming the critical path for software quality in the AI era, and what teams can do about it. Here is the full article ➡️ https://lnkd.in/ggjJTeFj

When operational GenAI fails in production, it’s rarely the LLM. It’s the data architecture. Get Gartner's guide to AI-ready data architecture

When operational GenAI fails in production, it’s rarely the LLM. It’s the data architecture. Gartner explains what needs to change.