Security teams have been told to ‘respond faster.’ But in the cloud and AI era, the real advantage comes from acting the instant risk emerges without waiting for people to catch up... That’s why we built automation into the core of Sentra’s Data Security Platform. - Real-time discovery - Accurate classification - Context-aware remediation All designed to help teams move from reactive alerts to proactive protection without adding complexity. In our latest blog post, Gilad Golani and I share why automation is now essential to securing sensitive data, how we’ve built it at Sentra, and what it takes to address risk at machine speed. Read it here 👇 https://lnkd.in/dWpHYhk6
How Sentra's Data Security Platform uses automation for proactive protection
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AI is transforming businesses—but is your enterprise AI secure enough to scale? Moving AI from experimentation to production requires more than innovation; it demands enterprise-grade security and governance. Traditional sandbox solutions just don’t cut it when scaling AI to handle sensitive data across hybrid environments. NetApp’s AI Factories are built with zero-trust architectures and cyber resilience to ensure seamless security without slowing innovation. From autonomous ransomware protection to unified hybrid cloud security, we’re enabling organizations to unlock the full potential of AI while staying secure and compliant. 🔒 Security isn’t a barrier—it’s the foundation for AI success. Learn how to build trust into your AI infrastructure and lead in AI-driven markets. #WhyNetApp
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With Satori, #Commvault now offers a modern, cloud-native data and AI #securityplatform that addresses the needs of enterprises adopting AI and managing sensitive data across multiple cloud environments. The platform enables organizations to discover and classify sensitive data, govern access, and enforce policies without altering user workflows. https://lnkd.in/eXPQUR4V
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Sentra adds AI-powered on-premises scanners to secure sensitive data across hybrid and private environments with cloud consistency - https://lnkd.in/gZbjtXAq “Organizations shouldn't have to compromise on data security just because their sensitive information lives in different places,” said Yair Cohen, VP Product and Co-Founder of Sentra.“ #Sentra #DataSecurity #HybridCloud #AIpoweredSecurity #DataClassification #DataGovernance #CloudNative #TechIntelPro
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History doesn’t repeat, but it sure rhymes. I just got back from a busy week on the East Coast meeting with prospects and customers, and the parallels between data & AI security and cloud security are impossible to ignore. When cloud security took off, visibility tools (CSPM) won first. They made posture issues easy to find – but visibility alone didn’t stop breaches. Over time, CSPM commoditized, and the market shifted to end-to-end, runtime protection (CNAPP). We’re watching the same movie in data & AI security. Pure-play DSPM leans on classifying files and structured data at rest. But the way people actually work breaks those labels. In an internal customer study, ~80% of exfiltration involved fragmented, derived snippets of data – not clean, labeled files. And with AI, DSPM moats are shrinking fast and starting to commoditize. Just like cloud democratized compute, AI democratizes access to data. Creation, sharing, and collaboration have moved to end users and endpoints. To secure this new pattern, we need an integrated data security platform where endpoint data observability + endpoint data protection are first-class. That’s the conundrum for pure-play DSPM: Building a mature, scalable endpoint agent with lineage tracking is hard. Agents take years of iteration, while API connectors are easy by comparison. Some vendors are shipping helper tools to “fix” traditional DLP false positives. But garbage in, garbage out: if you don’t track derivative data – where most exfil actually happens – you won’t solve the problem and you won’t fix the performance issues that plagued legacy tools. Worse yet, you’ll have a growing false negative problem, as more and more fragmented data flows through the gaps in traditional DLP controls. If you want to protect AI, replace legacy DLP with modern, lineage-aware, endpoint-capable data protection that understands fragmented data. Putting new rims on a horse carriage won’t make it a car. I share more on this topic in our latest blog: https://lnkd.in/gcxfQtwr #dspm #fragmenteddata #datasnippets #dlp #datalineage
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Visibility without control is just noise. Insightful parallel from CEO Nishant Doshi: like CSPM to CNAPP, DSPM's next act will be the evolution from "labeling-at-rest" to "tracking-and-protecting in motion". As AI proliferation fragments data and breakdown labels, the critical momentum is shifting towards data lineage, endpoint telemetry, and fragment-level controls. In turn, we’re seeing Cyberhaven's ahead of the curve investments now exponentially bearing fruits. Thrilled to be tracking this space, no better time to build and reimagine the future of data loss prevention!
History doesn’t repeat, but it sure rhymes. I just got back from a busy week on the East Coast meeting with prospects and customers, and the parallels between data & AI security and cloud security are impossible to ignore. When cloud security took off, visibility tools (CSPM) won first. They made posture issues easy to find – but visibility alone didn’t stop breaches. Over time, CSPM commoditized, and the market shifted to end-to-end, runtime protection (CNAPP). We’re watching the same movie in data & AI security. Pure-play DSPM leans on classifying files and structured data at rest. But the way people actually work breaks those labels. In an internal customer study, ~80% of exfiltration involved fragmented, derived snippets of data – not clean, labeled files. And with AI, DSPM moats are shrinking fast and starting to commoditize. Just like cloud democratized compute, AI democratizes access to data. Creation, sharing, and collaboration have moved to end users and endpoints. To secure this new pattern, we need an integrated data security platform where endpoint data observability + endpoint data protection are first-class. That’s the conundrum for pure-play DSPM: Building a mature, scalable endpoint agent with lineage tracking is hard. Agents take years of iteration, while API connectors are easy by comparison. Some vendors are shipping helper tools to “fix” traditional DLP false positives. But garbage in, garbage out: if you don’t track derivative data – where most exfil actually happens – you won’t solve the problem and you won’t fix the performance issues that plagued legacy tools. Worse yet, you’ll have a growing false negative problem, as more and more fragmented data flows through the gaps in traditional DLP controls. If you want to protect AI, replace legacy DLP with modern, lineage-aware, endpoint-capable data protection that understands fragmented data. Putting new rims on a horse carriage won’t make it a car. I share more on this topic in our latest blog: https://lnkd.in/gcxfQtwr #dspm #fragmenteddata #datasnippets #dlp #datalineage
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Fundamentally Insider Risk is about observing and understanding humans interacting with business data and interpreting their behaviour and intent accurately. The technology layer - with its acronym alphabet soup - is simply a mechanism to observe, infer and respond to risk. Yes it is a big data problem - which benefits from data lineage and derivative tracking - and AI has a critical role play, but in a complex world let’s focus on protecting people and their data first.
History doesn’t repeat, but it sure rhymes. I just got back from a busy week on the East Coast meeting with prospects and customers, and the parallels between data & AI security and cloud security are impossible to ignore. When cloud security took off, visibility tools (CSPM) won first. They made posture issues easy to find – but visibility alone didn’t stop breaches. Over time, CSPM commoditized, and the market shifted to end-to-end, runtime protection (CNAPP). We’re watching the same movie in data & AI security. Pure-play DSPM leans on classifying files and structured data at rest. But the way people actually work breaks those labels. In an internal customer study, ~80% of exfiltration involved fragmented, derived snippets of data – not clean, labeled files. And with AI, DSPM moats are shrinking fast and starting to commoditize. Just like cloud democratized compute, AI democratizes access to data. Creation, sharing, and collaboration have moved to end users and endpoints. To secure this new pattern, we need an integrated data security platform where endpoint data observability + endpoint data protection are first-class. That’s the conundrum for pure-play DSPM: Building a mature, scalable endpoint agent with lineage tracking is hard. Agents take years of iteration, while API connectors are easy by comparison. Some vendors are shipping helper tools to “fix” traditional DLP false positives. But garbage in, garbage out: if you don’t track derivative data – where most exfil actually happens – you won’t solve the problem and you won’t fix the performance issues that plagued legacy tools. Worse yet, you’ll have a growing false negative problem, as more and more fragmented data flows through the gaps in traditional DLP controls. If you want to protect AI, replace legacy DLP with modern, lineage-aware, endpoint-capable data protection that understands fragmented data. Putting new rims on a horse carriage won’t make it a car. I share more on this topic in our latest blog: https://lnkd.in/gcxfQtwr #dspm #fragmenteddata #datasnippets #dlp #datalineage
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Excellent blog post by Nishant Doshi and Kasi Annamalai‼️ ⭐ My key takeaways: ⭐ 1️⃣ DSPM vendors’ “second act” is a big ask. Many are attempting to bolt on endpoint or DLP capabilities, but true evolution demands a unified platform combining discovery, posture, protection, and coaching. 2️⃣ Visibility isn’t enough anymore. AI fuels new data forms that evade traditional control models. 3️⃣ Endpoints are essential. To map real-world behavior (copy/paste, screenshots, app-to-app flows), you need mature endpoint telemetry and agents.
History doesn’t repeat, but it sure rhymes. I just got back from a busy week on the East Coast meeting with prospects and customers, and the parallels between data & AI security and cloud security are impossible to ignore. When cloud security took off, visibility tools (CSPM) won first. They made posture issues easy to find – but visibility alone didn’t stop breaches. Over time, CSPM commoditized, and the market shifted to end-to-end, runtime protection (CNAPP). We’re watching the same movie in data & AI security. Pure-play DSPM leans on classifying files and structured data at rest. But the way people actually work breaks those labels. In an internal customer study, ~80% of exfiltration involved fragmented, derived snippets of data – not clean, labeled files. And with AI, DSPM moats are shrinking fast and starting to commoditize. Just like cloud democratized compute, AI democratizes access to data. Creation, sharing, and collaboration have moved to end users and endpoints. To secure this new pattern, we need an integrated data security platform where endpoint data observability + endpoint data protection are first-class. That’s the conundrum for pure-play DSPM: Building a mature, scalable endpoint agent with lineage tracking is hard. Agents take years of iteration, while API connectors are easy by comparison. Some vendors are shipping helper tools to “fix” traditional DLP false positives. But garbage in, garbage out: if you don’t track derivative data – where most exfil actually happens – you won’t solve the problem and you won’t fix the performance issues that plagued legacy tools. Worse yet, you’ll have a growing false negative problem, as more and more fragmented data flows through the gaps in traditional DLP controls. If you want to protect AI, replace legacy DLP with modern, lineage-aware, endpoint-capable data protection that understands fragmented data. Putting new rims on a horse carriage won’t make it a car. I share more on this topic in our latest blog: https://lnkd.in/gcxfQtwr #dspm #fragmenteddata #datasnippets #dlp #datalineage
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You can’t protect what you don’t see and that’s where zero trust fails at the data layer. #Cyera has partnered with #Coalfire in the latest Product Applicability Guide making a clear case: true resilience demands a data-first zero trust posture. For large enterprises, that means: • Visibility as a strategic asset — discover and classify sensitive data wherever it lives (cloud, SaaS, on-prem) • Automated risk control — enforce policy and prioritize alerts intelligently to reduce operational overhead • AI guardrails — prevent high-risk data from leaking into AI/ML pipelines If your current zero trust efforts skip data, you’re leaving your organization exposed. Let’s talk: 🔹 What’s your biggest barrier to full data visibility today? 🔹 Want to explore how to build data-centric controls at scale? 🔗 Follow for more C-level strategies on AI, Data Risk and securing what matters most. 📄 More on our full Product Applicability Guide 👉 https://lnkd.in/ei27Gr4C #EnterpriseAI #ZeroTrust #DataSecurity #CLevelStrategy #FutureOfWork #Innovation #Cyera Yotam Segev Sol Rashidi, MBA Jason Clark Guy Gertner Tamar Bar-Ilan
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Anomali has officially unveiled its powerful AI-Powered Security and IT Operations Platform in the UAE, strategically hosted on Amazon Web Services (AWS) to meet the growing demand for advanced cloud-native security solutions in the region. This launch signifies a major commitment to enhancing the cyber defenses of critical sectors in the UAE, including the oil and gas, banking, government, utilities, and telecommunications industries. The platform represents a significant shift in security operations by unifying a full technology stack, including threat intelligence (TIP), security information and event management (SIEM), extended detection and response (XDR), security orchestration, automation, and response (SOAR) capabilities, into a single, high-speed, cloud-native data lake architecture. A core component of this innovation is Anomali Copilot, an advanced multi-lingual Generative AI assistant embedded across the entire security workflow. Read more here - https://lnkd.in/gevtVVfP #Anomali #Cybersecurity #AWS #UAE #SecurityOperations #AICopilot #CloudNative #ThreatIntelligence #DigitalTransformation #ITOperations
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AI is transforming businesses—but is your enterprise AI secure enough to scale? Moving AI from experimentation to production requires more than innovation; it demands enterprise-grade security and governance. Traditional sandbox solutions just don’t cut it when scaling AI to handle sensitive data across hybrid environments. NetApp’s AI Factories are built with zero-trust architectures and cyber resilience to ensure seamless security without slowing innovation. From autonomous ransomware protection to unified hybrid cloud security, we’re enabling organizations to unlock the full potential of AI while staying secure and compliant. 🔒 Security isn’t a barrier—it’s the foundation for AI success. Learn how to build trust into your AI infrastructure and lead in AI-driven markets
