Minimizing your Data Attack Surface in the Cloud

Yesterday’s news that Cyera is acquiring Ryft, a two-year-old startup building automated data lakes for AI agents, is the latest sign of how fast the AI security market is moving. It’s also Cyera’s fourth acquisition in five years, on the heels of Trail Security and Otterize, a clear signal that the company is trying to buy its way into new narratives as quickly as they emerge.

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For security and data leaders, the question isn’t “Is agentic AI important?” It absolutely is. The question is: What’s the real cost of stitching together yet another acquisition into an already complex platform?

The hidden cost of rapid, piecemeal integrations

On paper, adding Ryft gives Cyera a new story around “agentic AI security.” In practice, it creates a familiar set of integration problems:

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• Multiple architectures to reconcile
  Trail Security, Otterize, and now Ryft were all built as independent products with their own data models, UX patterns, and engineering roadmaps. Four acquisitions in five years means customers are effectively buying an integration project that’s still in progress, not a single, mature platform.

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• Gaps, overlaps, and inconsistent controls
  Every acquired module has its own blind spots and strengths. Until they’re truly unified, you get overlapping coverage in some areas, gaps in others, and policy engines that don’t behave consistently across cloud, SaaS, and on-prem.

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• Slower time-to-value for AI initiatives
  AI programs move quickly; integrations do not. Each acquisition has to be wired into discovery, classification, policy, reporting, access control, and remediation workflows before it delivers real value. That’s measured in quarters and years, not weeks.

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• Operational drag on security teams
  When you tie together multiple acquired engines, you often see scan-based coverage, noisy false positives, and limited self-serve reporting that still depends on the vendor’s team to interpret results. That’s the opposite of what already stretched security teams need as they take on AI data risk.

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The Ryft deal fits this pattern. It’s a high-priced bet on an early-stage team with a small set of digital-native customers, not a proven, enterprise-scale AI data security engine. That’s fine as a venture bet. It’s more problematic when packaged as an answer for Fortune 500 AI governance.

Why agentic AI security can’t be bolted on

Agentic AI changes the risk profile of enterprise data:

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• Agents traverse structured and unstructured data across cloud, SaaS, and on-prem.
• They act on behalf of identities, often chaining tools and APIs in ways that are hard to predict.
• The blast radius of a misconfiguration or over-permissioned identity grows dramatically once agents are in the loop.

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Trying to solve that by bolting an AI data lake acquisition onto a legacy, scan-based DSPM engine is risky. You’re adding another moving part on top of a system that already struggles with:

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• Point-in-time scans instead of real-time, continuous coverage
• High false positives without strong prioritization
• Shallow support for hybrid and on-prem environments
• Vendor-controlled workflows instead of customer-controlled, self-serve reporting

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If the underlying platform can’t continuously understand where sensitive data lives, which identities can touch it, and how that access is used, then adding an “AI data lake” on the side doesn’t fix the fundamentals. It just adds another place for risk to hide.

A different path: Sentra’s purpose-built, real-time platform

At Sentra, we took a different approach from day one: build a single, in-place, real-time data security platform, not a patchwork of stitched-together acquisitions.

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A few principles guide the way we think about AI and data security:

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• Real-time, continuous data security
  Sentra monitors data continuously, not in point-in-time scans, so risks are caught as they happen - not at the next scheduled review.

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• One unified architecture
  Sentra is a purpose-built, unified platform, not an assortment of logos held together by integration roadmaps. There’s one architecture, one data model, one roadmap, and one team focused entirely on DSPM and AI data security, rather than a set of acquired point products that still need to be woven together.

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• Proven for real AI workloads today
  Our platform is already securing real AI workloads in production environments, rather than depending on the future maturation of a seed-stage acquisition. AI data security for us is not a sidecar story. It's built into how we discover, classify, govern, and remediate risk across your estate.

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• Higher-precision signal, not more noise
  Sentra delivers higher classification precision (4.9 vs. 4.7 stars on Gartner) and couples that with workflows your team controls, not processes that require vendor intervention every time you need a new report or policy tweak.

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• Complete coverage for complex environments
  Modern enterprises aren’t cloud-only. Sentra provides full coverage across IaaS, PaaS, SaaS, and on-premises from a single platform, built for hybrid and legacy-heavy environments as much as for cloud-native stacks.

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In other words, while some vendors are racing to acquire their way into the next AI buzzword, Sentra is focused on delivering trustworthy, real-time, identity-aware data security that you can put in front of a CISO and a data platform owner today.

What to ask your vendors now

If you’re evaluating Cyera (or any vendor riding the latest AI acquisition wave), a few concrete questions can cut through the noise:

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1. How many acquisitions have you done in the last five years, and which parts of my deployment depend on those integrations actually working?
2. What’s fully integrated and running in production today vs. what’s still on the roadmap?
3. Are my AI and non-AI data risks handled by the same platform, policies, and reporting, or by separate acquired modules?
4. Do you provide continuous coverage and identity-aware controls across cloud, SaaS, and on-prem, or am I still relying on periodic scans and partial visibility?

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The AI security market doesn’t need more logos; it needs fewer moving parts, better signals, and real-time control over how data is used by humans and agents alike.

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That’s the standard Sentra is building for and the lens through which we view every new acquisition announcement in this space.

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Walk into any advanced manufacturing, aerospace, defense, or industrial design shop and you’re just as likely to see Solidworks as you are AutoCAD. The models, assemblies, and drawings built in Solidworks are the digital blueprints for everything from turbine blades and medical devices to satellites and weapons systems.

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Earlier this year we announced native support for AutoCAD DWG files, making an entire class of previously opaque CAD data visible to security and compliance teams for the first time. Now we’re extending that same deep visibility to Solidworks 3D CAD files, so you can protect the IP and regulated technical data hiding inside your .sldprt, .sldasm, and related content—without slowing engineering down.

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And as AI accelerates design cycles, that visibility is no longer optional.

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AI is Supercharging Design – and Expanding the Blast Radius

Design teams are pushing faster than ever:

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• Generative design tools propose entire families of parts and assemblies.
• Copilots summarize requirements, suggest changes, and draft documentation off CAD models.
• PLM-integrated agents automatically create downstream artifacts—quotes, NC programs, service manuals—based on 3D designs.
• RAG-style internal assistants answer questions using a mix of project docs, CAD files, and simulation outputs.

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All of this is powerful. It also multiplies the ways sensitive CAD data can leak:

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• Entire assemblies uploaded to unmanaged AI tools “just to explore options.”
• Export-controlled models referenced in prompts and ending up in long‑lived AI data lakes.
• Supplier and customer CAD shared into external copilots with little visibility into who—or what agent—can access it.
• Rich metadata from CAD (usernames, project codes, server paths, partner names) silently turned into reconnaissance material.

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If you don’t understand what’s inside your CAD, where it lives, and which identities and AI agents can reach it, AI doesn’t just speed up design—it speeds up IP disclosure, compliance failures, and supply‑chain exposure.

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CAD Has Been a Blind Spot for Security

Most traditional DSPM and DLP tools still treat specialized engineering formats as a big binary blob: “probably sensitive, treat with caution.” That may have been acceptable when CAD lived on a handful of on‑prem engineering servers.

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It’s not acceptable when:

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• Decades of CAD history have been lifted and shifted into S3, Azure Blob, or SharePoint.
• ITAR/EAR “technical data” now lives side‑by‑side with everyday project files in cloud object stores.
• Those same repositories feed downstream systems—PLM, MES, AI assistants—where traditional security tools have little or no visibility.

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We built native DWG parsing into Sentra to break that stalemate, making CAD content as transparent to security teams as a Word document. Solidworks 3D CAD support is the next logical step.

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What’s Really Inside a Solidworks 3D CAD File?

Like DWG, a Solidworks file is far more than geometry. It’s a container for rich metadata, text, and structural context that describes both what you’re building and how it fits into regulated programs and commercial IP. Our Solidworks support is designed to surface that security‑relevant context—without requiring CAD tools, manual exports, or data movement.

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Similar to what we do for DWG, Sentra can extract and analyze key elements, including:

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• Document properties
  Authors, “last saved by,” creation and modification timestamps, total editing time, and revision counters—signals that help you understand who is touching sensitive designs and when.

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• Custom properties and configuration metadata
  Project IDs, part and assembly numbers, revision codes, program names, business units, and export‑control or classification markings encoded as custom properties or notes.

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• Text content and annotations
  Notes, callouts, PMI, and embedded text that often contain material specifications, tolerances, customer names, contract IDs, and phrases like “COMPANY CONFIDENTIAL,” “EXPORT CONTROLLED,” or ITAR statements.

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• Assembly structure and component names
  Which parts roll up into which assemblies, and how those components are named—critical when you need to understand which physical systems a given sensitive model belongs to.

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• File dependencies and paths
  References to drawings, configurations, libraries, and external resources that routinely expose server names, share paths, usernames, and department structures—goldmine context for attackers, but also for incident response and insider‑risk investigations.

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For organizations operating under ITAR and EAR, this is where truly export‑controlled technical data actually lives—not in the folder name, but in the title blocks, annotations, and metadata attached to models and drawings.

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Turning Solidworks Models into Actionable Security Signals

By parsing Solidworks 3D CAD files in place, inside your own cloud accounts or VPCs, Sentra can now treat them as first‑class citizens in your data security program—just like we do for DWG and other specialized formats.

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That unlocks concrete use cases, such as:

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• Finding export‑controlled or highly sensitive designs in cloud storage
  Automatically surface Solidworks files whose metadata, annotations, or custom properties contain ITAR statements, ECCN codes, proprietary markings, or customer‑confidential labels—so you can focus remediation on the drawings and models that are actually regulated.

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• Mapping who (and what) can access critical designs
  Combine CAD‑aware classification with Sentra’s DSPM and DAG capabilities to answer:
  Where are our most sensitive Solidworks assemblies stored, and which identities, service principals, and AI agents can currently reach them?

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• Monitoring AI and collaboration workflows for IP exposure
  Track when Solidworks files that contain regulated or high‑value IP are moved into AI data lakes, shared via collaboration platforms, or accessed by non‑human identities—so DDR policies can flag, quarantine, or route for review before they turn into public incidents.

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• Building a defensible audit trail for CAD‑resident technical data
  Maintain an inventory of Solidworks files that contain export‑control markings or IP‑critical content, tie each file to its exact storage location and access controls, and surface any out‑of‑policy placements—so when auditors ask “Where is your technical data?”, you can answer with data, not slideware.

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Closing the Gap Between “Stored” and “Understood” for 3D CAD

As workloads like EDA, PLM, simulation, and AI‑assisted design move deeper into the cloud, the number of specialized formats in your environment explodes. Most tools still only truly understand emails, office documents, and a narrow slice of structured data.

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The reality is simple: you cannot secure data you don’t understand. Understanding means being able to answer, at scale, not just “Where is this file?” but “What is inside this file, how sensitive is it, and how is AI amplifying its risk?”

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For organizations whose crown‑jewel IP and export‑controlled technical data live in Solidworks 3D CAD, that’s the gap Sentra is now closing.

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If you want to see what’s actually hiding inside your own Solidworks models and assemblies, the easiest next step is to run a focused assessment: pick a few representative buckets or repositories, let Sentra scan those CAD files in place, and review the inventory of regulated and high‑value designs that surfaces.

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Chances are, once you’ve seen that map—and how it connects to your AI initiatives—you’ll never look at “just another CAD file” the same way again.

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Fiverr’s recent data breach/data exposure left tax forms, IDs, contracts, and even credentials publicly accessible and indexed by Google via misconfigured Cloudinary URLs.

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This post explains what happened, why data sprawl across third-party services made it inevitable, and how to prevent the next Fiverr-style leak.

The Fiverr data breach is a textbook case of sensitive data sprawl and misconfigured third‑party infrastructure: highly sensitive documents (including tax returns, IDs, health records, and even admin credentials) were stored on Cloudinary behind unauthenticated, non‑expiring URLs, then surfaced via public HTML so Google could index them—remaining accessible for weeks after initial disclosure and hours after public reporting. This isn’t a zero‑day exploit; it’s a failure to understand where regulated data lives, how it rapidly proliferates and is shared across services, and whether controls like signed URLs, authentication, and proper indexing rules are actually in place.

In practical terms, what happened in the Fiverr data breach?

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– Sensitive documents (tax returns, IDs, contracts, even credentials) were stored on Cloudinary behind unauthenticated, non-expiring URLs.

– Some of those URLs were linked from public HTML, allowing Google and other search engines to index them.

– As a result, private Fiverr user data became publicly searchable, long before regulators or affected users were notified.

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What the Fiverr Data Breach Reveals About Third-Party Data Sprawl‍

What makes this kind of data exposure - like the Fiverr data leak - so damaging is that it collapses the boundary between “internal work product” and “public web content.” The same files that power everyday workflows—tax filings, medical notes, penetration test reports, admin credentials—suddenly become discoverable to anyone with a search engine, long before regulators or affected users even know there’s a problem. As enterprises lean on third‑party processors, media platforms, and SaaS for collaboration, the real risk isn’t a single misconfigured bucket; it’s the absence of continuous visibility into where sensitive data actually resides and who—human or machine—can reach it.

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Sentra is built to restore that visibility and hygiene baseline across the entire data estate, including cloud storage, SaaS platforms, AI data lakes, and media services like the one at the center of this incident. By running discovery and classification in‑environment—without copying customer data out—Sentra builds a live inventory of sensitive assets, from tax forms and IDs to health and financial records, even in unstructured PDFs and images brought into scope via OCR and transcription. On top of that, Sentra continuously identifies redundant, obsolete, and toxic (ROT) data, so organizations can eliminate unnecessary copies that amplify the blast radius when something does go wrong, and set enforceable policies like “no GLBA‑covered data on unauthenticated public endpoints” before the next Cloudinary‑style exposure ever materializes.

If you’re asking “How do we avoid a Fiverr-style data breach on our own SaaS and media stack?”, the starting point is continuous visibility into where sensitive data lives, how it moves into services like Cloudinary, and who or what (including AI agents) can access it.

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How to Prevent a Fiverr-Style Data Leak Across SaaS, Storage, and Media Services

Where traditional controls stop at the perimeter, Sentra ties data to identities and access paths, including AI agents, copilots, and service principals. Lineage‑driven maps show how data moves—from a storage bucket into a search index, from a document library into a media processor—so entitlements can follow data automatically and public or over‑privileged links can be revoked in a targeted way, rather than taking an entire service offline. On that foundation, Sentra orchestrates automated actions and remediation: quarantining exposed files, tombstoning toxic copies, removing public links, and routing rich, contextual tickets to owners when human judgment is required—all through existing tools like DLP, IAM, ServiceNow, Jira, Slack, and SOAR instead of standing up a parallel enforcement stack.

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Doing this at “Fiverr scale” requires more than point tools; it demands a platform that is accurate, scalable, and cost‑efficient enough to run continuously and scale across multi-hundred petabyte environments. Sentra’s in‑environment architecture and small‑model approach have already scanned 8–9 petabytes in under 4–5 days at 95–98% accuracy—an order‑of‑magnitude faster and cheaper than extraction‑based alternatives—while keeping customer data inside their own accounts. That efficiency means enterprises can maintain continuous scanning, labeling, and remediation across hundreds of petabytes and multiple clouds without turning governance into a budget‑breaking project, and can generate audit‑grade evidence that sensitive data was governed properly over time—not just at the last assessment.

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Incidents like the Fiverr data breach are a warning shot for the AI era, where copilots, internal agents, and search experiences will happily surface whatever the underlying permissions and data quality allow. As AI adoption accelerates, the only sustainable defense is a baseline of automated, continuous data protection: accurate classification, durable hygiene, identity‑aware access, automated remediation, and economically viable, always‑on governance that keeps pace with rapidly expanding and evolving data estates. You can’t secure AI—or avoid the next “public and searchable” headline—without first understanding and continuously governing the data that AI and its surrounding services can see. As AI pushes boundaries (and challenges security teams!), there is no time like now to ensure data remains protected.

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Fiverr data breach FAQ

• Was my Fiverr data exposed in the breach?
  Fiverr and independent researchers have confirmed that some user documents—including tax forms, IDs, invoices, and credentials—were publicly accessible and indexed by Google via misconfigured Cloudinary URLs. Whether your specific files were exposed depends on what you shared and how Fiverr stored it, but the safest assumption is that any sensitive document shared on the platform may have been at risk.
  
  
• What made the Fiverr data breach possible?
  The root cause wasn’t a zero-day exploit; it was data sprawl across third-party infrastructure plus weak controls: public, non-expiring Cloudinary URLs, public HTML linking to those URLs, and no continuous visibility into where regulated data lived or who could reach it.
  
  
• How can enterprises prevent similar leaks?
  By continuously discovering and classifying sensitive data across cloud storage, SaaS, and media services; cleaning up ROT; enforcing policies like “no GLBA-covered data on unauthenticated public endpoints”; and tying access to identities so public links and over-privileged routes can be revoked automatically. 

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Read more about the Fiverr Data Breach

Detailed news coverage of the Fiverr data breach and Cloudinary misconfiguration (Cybernews)

Independent analysis of the Fiverr data exposure via public Cloudinary URLs (CyberInsider)

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