4 Common Data Risks Ignored by Enterprises

The Two Birds: Zero Trust & Data Centricity Most organizations still secure data by tagging datasets and relying on the presentation layer or business rules layer to apply access rules, whether that layer sits on the host or on the node. There are already attempts to move toward more granular, data-centric security, but that is still not the norm. A better model would store both the classification and the exposure rules with each data element itself, rather than attaching them only to an entire dataset or table. In that approach, a field, row, or object would carry its own metadata about what can be shown, to whom, and under what conditions. This would reduce dependence on the presentation layer or business rules layer to make the right decision every time. Instead of trusting each consuming application to interpret policy correctly, the data would already know what it is allowed to reveal. Some may assume that moving enforcement to the data layer would slow retrieval down, but in reality it may speed things up. We are not adding a new layer to the stack; we are shifting the access control function to the data itself, which can reduce downstream filtering, repeated policy checks, and unnecessary exposure handling. The advantages are significant. It gives finer-grained control, lowers the risk of accidental overexposure, improves consistency across systems, and makes policies easier to audit. It also supports a stronger zero trust posture, because access is no longer assumed safe just because a request reaches the application layer. The data itself becomes part of the enforcement boundary. This does not need to replace existing access control mechanisms. It can work alongside the current access control layer as a complementary model, where traditional controls decide who is allowed to request the data, and element-level rules decide what can actually be exposed. That is where the architecture becomes more resilient: less reliance on application behavior, more protection built into the data itself, and a security model that better reflects how modern systems are actually used. #data

Every vendor in data security claims 95%+ classification accuracy. Forrester just published a blog that basically tells buyers to stop taking that number at face value. Heidi Shey’s piece on evaluating sensitive data discovery and classification solutions makes a point that doesn’t get enough airtime: the accuracy number itself is almost meaningless without understanding how it’s achieved. And she’s right. A vendor running regex against credit card numbers and SSNs can hit 95% on those patterns all day long. Those formats are predictable. That’s not classification, that’s pattern matching with good marketing. The real question is what happens when the data doesn’t fit a known pattern. Employee IDs. Internal claim numbers. Product SKUs. The stuff that’s unique to your organization and represents the majority of what actually needs protecting. Forrester recommends testing through a proof of concept and digging into the techniques behind the number. What enrichment is happening? Is the system understanding context like data lineage, permissions, who the data subject is? Or is it just matching strings? That’s the conversation I think more security teams should be having during evaluations. Not “what’s your accuracy rate” but “show me how you got there on my data.” The blog also raises a point about pricing models tied to data volume and the trade-off between compute cost and coverage. Worth reading if you’re evaluating solutions in this space right now. https://lnkd.in/e-Vdgvmn #datasecurity #DSPM #classification #dataprotection #forrester

In today’s data-driven world, organizations often assume that having more data automatically leads to better decisions. The reality? Managing data effectively is one of the biggest challenges businesses face. Here are some of the most common hurdles in data management: 🔹 Data Silos Teams often operate in isolation, leading to fragmented data across systems. This makes it difficult to get a unified view of the business. 🔹 Poor Data QualityInaccurate, incomplete, or outdated data can lead to flawed insights and costly decisions. 🔹 Scalability Issues As data grows exponentially, legacy systems struggle to keep up, impacting performance and accessibility. 🔹 Data Security & Privacy With increasing regulations and cyber threats, ensuring data protection is no longer optional—it’s critical. 🔹 Lack of Governance Without clear policies and ownership, data becomes inconsistent, unreliable, and hard to trust. 🔹 Integration Complexities Combining data from multiple sources—cloud, on-prem, third-party tools—remains a technical and operational challenge. The key takeaway: Data is only as valuable as how well it’s managed. Organizations that invest in strong data governance, modern infrastructure, and a data-driven culture are the ones that turn data into a true competitive advantage. 💡 What challenges are you facing in your data management journey? #DataManagement #DataGovernance #DataQuality #DigitalTransformation #DataStrategy

5 mistakes killing Data Governance in 🇦🇺 Australia And the regulators have stopped being polite about it. - APRA CPS 234 is now enforced. - Essential Eight is boardroom language. - NIST CSF is the global yardstick. Yet most Australian organisations are still treating governance like a tooling problem. It isn’t. It’s a business maturity problem wearing a technology mask. Here’s what I keep seeing on the ground 👇 The 5 mistakes companies that need it most keep making: 1. Buying Purview or Collibra before defining a single data domain owner. 2. Treating “governance uplift” as an IT project, not an operating model shift. 3. Mapping controls to frameworks on paper, but never testing them in reality. 4. Confusing classification labels with actual risk reduction. 5. Chasing “maturity level 3” for the audit, not for the business. Each one looks like progress. Each one quietly burns budget. Here’s the uncomfortable truth: Microsoft Purview won’t save you. Collibra won’t save you. They are brilliant enablers. Not decision-makers. Purview gives you discovery, lineage, DLP and sensitivity labels across the Microsoft estate. Collibra gives you stewardship, policies and a business glossary that humans actually use. But neither tool can answer: → Who owns this data? → What’s our risk appetite? → What decisions change if this leaks? → Which processes must never fail under CPS 234? Those are business conversations. Not feature toggles. Why this cannot be avoided anymore: 1. APRA CPS 234 demands information security capability proportional to threat. 2. Essential Eight sets the hygiene floor. 3. NIST CSF gives you the language to report maturity upward. Miss the alignment, and you’re not just non-compliant. You’re uninsurable, unfundable, and unsellable to enterprise clients. Regulators aren’t asking if you have the tool. They’re asking if you have the operating discipline. The shift that actually works: Start with business outcomes. Then operating model. Then controls. Then tooling. In that order. Always. Purview and Collibra become powerful the moment the org above them grows up. Not a day sooner. So here’s my question to leaders reading this: Is your data governance program uplifting maturity? Or just buying comfort? If you’re wrestling with CPS 234 alignment, Essential Eight rollout, or a stalled Purview/Collibra program — let’s talk in the comments. I’d love to hear where you’re stuck. ♻️ Repost if your network needs this reminder. 🔔 Message me if you want to discuss about your organistions GRC, data governance programs and solve the complex situations. Key question is if Australian Organisations only need solution to satisfy regulatory bodies OR do they actually need to build controls and adopt the relvant operational changes? #DataGovernance #GRC #APRACPS234 #EssentialEight #MicrosoftPurview #Collibra #AustraliaTech

Data Access Governance has become a broad-level concern, but the tooling landscape is crowded and confusing. Let’s compare the 7 leading data access governance tools and how to pick the right one for your hybrid environment. https://lnkd.in/gNrWX6HN #dataaccessgovernance #datasecurity #dataaccess #compliance

𝐒𝐲𝐬𝐭𝐞𝐦 𝐩𝐫𝐨𝐦𝐩𝐭𝐬 𝐰𝐨𝐧'𝐭 𝐬𝐭𝐨𝐩 𝐲𝐨𝐮𝐫 𝐀𝐠𝐞𝐧𝐭𝐬 𝐟𝐫𝐨𝐦 𝐥𝐞𝐚𝐤𝐢𝐧𝐠 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚.   If you are building AI agents or deploying to production, your biggest security threat isn't a complex jailbreak, it's silent, architectural data exfiltration.   According to the 𝐎𝐖𝐀𝐒𝐏 𝐆𝐞𝐧𝐀𝐈 𝐃𝐚𝐭𝐚 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 framework (𝐃𝐒𝐆𝐀𝐈01 - 𝐒𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐃𝐚𝐭𝐚 𝐋𝐞𝐚𝐤𝐚𝐠𝐞), highly sensitive data (PII, PHI, API keys) is bleeding out of enterprise systems because engineering teams are relying on instructions rather than architecture.   Here are the three primary leakage vectors where context and control are being lost:   1. 𝐓𝐡𝐞 𝐋𝐨𝐑𝐀 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐒𝐮𝐫𝐟𝐚𝐜𝐞   Fine-tuned models and adapters are highly efficient, but they memorize rare training examples, like hardcoded secrets or PII, with alarming fidelity. When you deploy these, you are essentially deploying a compressed archive of your sensitive data. Attackers can, and will, systematically extract it.   2. 𝐓𝐡𝐞 𝐑𝐀𝐆 𝐎𝐯𝐞𝐫𝐬𝐡𝐚𝐫𝐢𝐧𝐠 𝐋𝐨𝐨𝐩𝐡𝐨𝐥𝐞   Your RAG pipeline is blind to your intended context; it only understands access. If your vector database ingests legacy SharePoint drives or public Slack channels with loose permissions, the model will faithfully retrieve and leak that sensitive data to unauthorized users. The system is working as designed, but the data surface is overly broad.   3. 𝐓𝐡𝐞 𝐏𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐞 𝐨𝐟 "𝐃𝐞𝐥𝐞𝐭𝐞𝐝" 𝐃𝐚𝐭𝐚   Deleting a raw file does not stop the leak. That sensitive data persists in your vector embeddings, model weights, and telemetry logs. Because true machine unlearning is incredibly difficult, your "deleted" IP is often still semantically searchable.   𝐓𝐡𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐅𝐢𝐱: 𝐏𝐫𝐢𝐯𝐚𝐜𝐲-𝐛𝐲-𝐃𝐞𝐬𝐢𝐠𝐧 Stop relying on "𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵𝘪𝘰𝘯 𝘏𝘪𝘦𝘳𝘢𝘳𝘤𝘩𝘺" to protect your company's secrets. Hardening your pipelines requires defense-in-depth: 1. 𝐒𝐨𝐮𝐫𝐜𝐞 𝐃𝐚𝐭𝐚 𝐌𝐢𝐧𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Tokenize or redact sensitive fields before they ever hit the training set or vector index. 2. 𝐅𝐨𝐫𝐦𝐚𝐭-𝐏𝐫𝐞𝐬𝐞𝐫𝐯𝐢𝐧𝐠 𝐄𝐧𝐜𝐫𝐲𝐩𝐭𝐢𝐨𝐧 (𝐅𝐏𝐄): Allow the model to process the structural context of data (like a credit card format) without exposing the raw, sensitive values. 3. 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐟𝐨𝐫 𝐒𝐲𝐬𝐭𝐞𝐦𝐚𝐭𝐢𝐜 𝐏𝐫𝐨𝐛𝐢𝐧𝐠: Actively red team your APIs to detect extraction and distillation attacks designed to harvest your models or surface PII. 4. 𝐁𝐥𝐨𝐜𝐤 𝐈𝐧𝐝𝐢𝐫𝐞𝐜𝐭 𝐄𝐱𝐟𝐢𝐥𝐭𝐫𝐚𝐭𝐢𝐨𝐧: Disable markdown image rendering to external URLs and sanitize tool callbacks, these remain highly documented leak paths. If your team isn't treating sensitive data leakage as a foundational architectural flaw, you are leaving the vault door wide open. #AISecurity #AgentSecurity #OWASP #Governance #CyberSecurity

Data Governance is often viewed as a "blocker" to speed, but when it comes to security, it's actually the ultimate enabler. In modern enterprises, data masking isn't a "nice-to-have"—it’s the foundational safeguard that allows developers to move fast without risking a breach. Here are 10 operational best practices to keep your data secure and your teams productive: 1️⃣ Discover and Classify First: You can’t protect what you haven't identified. 2️⃣ Static Masking for Non-Prod: Keep real PII out of dev and testing environments. 3️⃣ Dynamic Masking for Roles: Show only what’s necessary based on the user’s job. 4️⃣ Maintain Referential Integrity: Masked data is useless if it breaks your app logic. 5️⃣ Avoid Over-Masking: Don't kill the data's utility in the name of security. 6️⃣ Align with Least Privilege: Masking should reflect your access policies. 7️⃣ Test your Masked Data: Ensure the "fake" data still behaves like the "real" thing. 8️⃣ Continuous Monitoring: Review policies as your schema evolves. 9️⃣ Audit-Ready Documentation: Document your rules for GDPR/CCPA compliance. 🔟 Use Deterministic Masking: Ensure consistency across different systems. When you embed masking into your governance workflow, you don't just reduce exposure, you empower your teams to innovate safely. Read more about each best practice and common mistakes to avoid: https://hubs.ly/Q048k86t0 How is your team handling sensitive data in test environments? #datagovernance #dataprivacy #datamasking

Why Data Sovereignty is the Key to Future Digital Success In today’s digital economy, data is more than an asset. It is the backbone of strategy, operations, and customer trust. Yet many organizations focus on collecting and analyzing data without asking the most critical question: Who truly controls it? Data sovereignty is about owning and governing your data across every system, process, and user interaction. It’s not just compliance. It is the foundation for innovation, security, and sustainable growth. Here’s why it matters: •Data Governance: Ensures that data is accurate, traceable, and compliant. Policies, standards, and clear ownership prevent fragmentation, misuse, and costly errors. Without governance, sovereignty is just theory. •DevOps Integration: Modern development cycles demand speed and agility. Data sovereignty ensures pipelines respect policies and maintain auditability, so innovation doesn’t come at the cost of compliance or security. •Zero Trust Security: “Never trust, always verify” becomes actionable when you control data. Access is granted based on identity, role, and context, ensuring that every interaction with sensitive data is monitored and accountable. •SecOps Alignment: Security operations integrated with the data lifecycle reinforce sovereignty. Continuous monitoring, threat detection, and incident response reduce risk from insider errors or external attacks. •Customer Trust & Experience: Users expect transparency and protection. Organizations that clearly control data build confidence and loyalty. When people trust that their data is safe, interactions are smoother, adoption is higher, and relationships are stronger. •Business Agility: Sovereign data enables safe cross-border operations, reliable analytics, and AI applications. Teams can act confidently with the right information at the right time, without legal or operational uncertainty. •Sustainable Performance: Short-term results mean nothing if long-term risk, compliance breaches, or reputational damage follow. Data sovereignty ensures your growth is both ambitious and responsible. The reality is simple: Data you don’t control isn’t data you can trust. Organizations that embrace data sovereignty, coupled with governance, secure operations, and customer-centric design, unlock: ✅ Compliance without compromise ✅ Faster innovation without risk ✅ Stronger customer trust and loyalty ✅ Predictable, reliable business outcomes Ignoring it? You risk breaches, regulatory fines, and loss of trust. Takeaway: •Control drives trust: Data sovereignty ensures security, compliance, and reliable insights. •Behavior beats volume: Governance + Zero Trust + SecOps make data actionable and safe. •Sovereignty enables growth: Confident data use fuels innovation, agility, and customer loyalty.   #DataSovereignty #DataGovernance #ZeroTrust #SecOps #DevOps #UserExperience #DigitalTrust #Innovation #CyberSecurity

I remember sitting across from a CIO who looked completely in control on the surface—but the moment we started talking about data, everything changed. He leaned back and said, “We don’t have a storage problem… we have a visibility problem.” Their environment had grown over years—file shares, emails, cloud drives, archived folders, legacy systems. No one had a clear picture of what existed, where it lived, or whether it even mattered anymore. At first, it sounded manageable. Then we dug deeper. Different teams were saving the same documents in multiple places. Critical files were buried in outdated folders. Compliance requests took weeks because no one could confidently say where sensitive data was stored. And when audits came up, the entire IT team would drop what they were doing just to piece things together. What really hit me was when he said: “We’re making decisions based on data we don’t fully trust.” That’s when the real cost became clear. It wasn’t just storage. It was time. It was risk. It was missed opportunities. Every day, more unstructured data was being created—emails, documents, images, reports—adding to a growing mess that no one could control. That’s where DataBeagle came into the conversation. Not as another tool—but as a way to finally understand what they had. DataBeagle indexed and classified everything—across all their environments. Suddenly: • They could search and find information instantly • Duplicate and obsolete data became visible (and removable) • Sensitive data was identified and governed properly • Audits that used to take weeks started taking hours But the biggest shift? Confidence. The CIO didn’t just have more data. He finally had clarity. Because once you can see your data clearly… you can actually start using it to move the business forward.

Fortune 50 Feedback. "Another team bought a DSPM to help scan for sensitive data. We realized it only does sampling and has little to no continuous auditing capabilities at the object and file layer for structured and unstructured data sets. This has resulted in us in having to buy multiple products to serve a basic audit need + capture basic security controls (i.e. who touched what, who can access what). The DSPM promised they would have it on the roadmap, 18 months later - they still have no product or capability. This is an identified gap with our risk and governance team and security team which puts our firm at risk to regulators and data loss. Furthermore our governance teams has highlighted how this DSPM lacks critical visiblity and mapping to CIS benchmarks that are a must for our business. So yes we can find some data, we just can't protect it. And we now need multiple tools and an insane amount of manual effort to get some of what we need." Introduce the Varonis Data Security Platform -DSPM, DAM, UEBA, DDR, and Automated Remediation all in one -With the ability to cover all of their AI security needs. -ROI saving the client estimated $1.5mm annually in tooling alone -ROI in manual mundane services - hundreds of thousand of dollars annually -ROI in protecting against a breach and regulatory fines - priceless Much better product, much better outcomes. Real Results. Data classification is not data security. It never has been. Try the Varonis difference today.