Skyflow

If AI is leaking your data, the problem isn’t AI. It’s your data architecture. Most teams assume they’re safe because: • “We have contracts with OpenAI.” • “It runs in our environment.” • “We’ve locked things down with access controls.” That comfort is false. What actually breaks is how data gets used once AI and agents are involved. Access controls are coarse. AI needs fine-grained, runtime data control. Most architectures were never designed for that. In this clip, Skyflow CPO, Amruta Moktali, and CMO, Rob Hewardt, explain why AI data leaks aren’t surprises, they’re symptoms of a data stack built for static systems, not autonomous ones. This is why so many “secure AI” pilots stall in production.

Most 🇮🇳 teams know they need to be #DPDP compliant. Fewer feel confident about how to do it. When personal data moves across apps, analytics, and AI workflows, compliance isn’t just a legal problem—it’s a system design problem. In this upcoming session with Express Computer, we’ll talk through what actually works when it comes to DPDP execution. Join Amruta Moktali, Chief Product Officer at Skyflow and Adv. Puneet Bhasin, Leading cyber law and data protection expert for a discussion on how teams are closing that gap — and turning DPDP requirements into real execution. 🇮🇳🇮🇳🇮🇳 🗓 Jan 29, 2026 ⏰ 3 PM IST 🔗 Register here: https://lnkd.in/gWYkGuBA

The future of AI care depends on one question: can you unlock sensitive data without putting people at risk? One of the most compelling AI projects we’ve seen isn’t a chatbot or a demo. It’s a virtual AI nurse designed to help people feel better, faster. To build it, the team had to work with the most sensitive inputs imaginable: patient notes, handwritten doctor scribbles, and recorded doctor-patient conversations. Real data. Real people. Real consequences. The goal wasn't a novelty. It was better care by asking better questions: Did you take your medication? How are you feeling today? Can we catch issues earlier and respond faster than today’s system allows? None of that is possible if sensitive data can’t be used safely. That’s where the model has to change. Detect and de-identify sensitive data, yes. But more importantly, keep it controlled as it moves through training, inference, and the live data pipeline. Not just for experimentation, but in production. When you do that, sensitive data stops being something you lock away out of fear. It becomes something you can responsibly unlock to improve outcomes with AI. Hannah Simon explains how this works and why it matters in the clip below.

DPDP Rule 6 has the highest penalty of non-compliance. Given the sprawling nature of PII, protecting infrastructure doesn’t mean protecting PII. So, how to solve it correctly? Register for this exclusive webinar. No theory, understand real-world patterns across enterprises in India. Skyflow Hariram Sankaran Neha Jamwal https://lnkd.in/du6-x7Ca

Live from NRF! 🎉 Come see us at booth #2635. We’re demoing Skyflow’s Runtime AI Data Security layer, built to help teams confidently run MCP and agentic workflows across personalization, customer support, and fraud/risk, while protecting customer and transaction data by default. Stop by for a quick demo and to meet the team.

The true power of Agentic AI has been blocked. Every agent needs data to be impactful. Yet, most security teams are afraid of agents running wild without proper AI governance in place. As a result, security teams have defaulted to one idea of security: “Let me just block access to the customer data.” That approach keeps AI secure, but that does not make AI powerful. This isn’t a model problem. It’s an access, a governance and most importantly, an architecture problem. In this clip, Skyflow Chief Product Officer, Amruta Moktali explains why blocking access to data keeps AI safe, but dulls the impact. It's time for a new approach to AI data governance.

If you’re doing everything right and still getting breached, the problem isn’t execution. It’s the architecture. Most teams aren’t careless. They encrypt data. They lock down access. They write policies and audit. And yet sensitive data still ends up everywhere: apps, logs, analytics pipelines, GenAI workflows, internal tools. Once that happens, data governance turns into whack-a-mole. The assumption that you can secure data after it’s copied, shared, and embedded across systems doesn’t hold anymore. Especially in a world of GenAI and agents operating at runtime. What works is flipping the model. Control sensitive data at the data layer. Centralize it. Enforce policy once. Expose it safely everywhere it needs to be used without letting it sprawl. That’s the idea behind a runtime data control layer and why rethinking data privacy from the ground up actually changes outcomes, not just checklists. Hear from Hannah Simon as she shares her thoughts on how to treat sensitive data differently.

We’re headed to NRF! 🎉 Stop by Skyflow booth #2635 to learn how our Runtime AI Data Security layer helps teams safely run MCP and agentic workflows for personalization, customer support, and fraud/risk, while protecting customer and transaction data. #NRF2026

Is your organisation ready for the new era of data privacy? In this eye-opening session, Anshu Sharma explains why data privacy must be embedded into business strategy — not treated as an afterthought. From consent and breach disclosures to privacy-first architecture, this discussion offers practical insights for enterprises navigating India’s DPDP regime Watch Now : https://lnkd.in/gyHA6fxW Neha Jamwal | Skyflow #CIO #DataProtection #DPDPAct #DigitalTransformation #TrustByDesign

“Your personal data probably lives in a hundred places… even though you only gave it once.” That simple truth — shared by Deepak Annamalai, Head of Sales, APAC at Skyflow — hits differently when you think about how digital life has quietly reshaped us as consumers. Deepak walked onto the ETCISO stage with two hats: one of a regular user, and one of an executive inside the engine room of digital businesses. And through both lenses, he revealed a reality many organizations still underestimate. Here are the ideas that stood out: Digital convenience creates digital footprints — everywhere. Whether it’s grocery orders, insurance history, bank balance clues, or our favorite travel routes, companies know more about us than we realize — and this data sprawls across apps, tools, pipelines, CRMs, AI models, and storage systems. Collecting data is easy. Protecting it is the real operational nightmare. Especially under DPDP: consent must be enforced across systems, deletion must be provable, and “reasonable security” now means protecting data at its core, not just guarding the perimeter. Tokenization isn’t a feature — it’s a philosophy. Deepak’s analogy was simple: we don’t keep diamonds in the kitchen. A data privacy vault centralizes sensitive data, replaces it with harmless tokens everywhere else, and ensures that even if attackers get in, they get nothing of value. Trust, once broken, takes years to rebuild. Technology may evolve, regulations may tighten, but trust remains the real moat. His closing point lingered: “A privacy vault isn’t just compliance. It’s customer lifetime trust.” Know more about ETCISO: https://lnkd.in/grwAMhDU #ETCISO #ETCISODPDP #DataPrivacy #CyberSecurity #DigitalTrust #PrivacyEngineering #Tokenization #DataProtection #DPDPAct #SecurityArchitecture #FutureOfSecurity #CISOCommunity