Finding Sensitive Cloud Data in all the Wrong Places

Cloud adoption and the explosion of data have boosted business agility, but they’ve also created new headaches for security teams. As companies move sensitive information into multi-cloud and hybrid environments, old security models start to break down. Shuffling data for scanning and classification adds risk, piles on regulatory complexity, and drives up operational costs.

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Zero Data Movement (ZDM) offers a new architectural approach, reshaping how advanced Data Security Posture Management (DSPM) platforms provide visibility, protection, and compliance. This post breaks down what makes ZDM unique, why it matters for security-focused enterprises, and how Sentra provides an innovative agentless and scalable design that is genuinely a zero data movement DSPM .

Defining Zero Data Movement Architecture

Zero Data Movement (ZDM) sets a new standard in data security. The premise is straightforward: sensitive data should stay in its original environment for security analysis, monitoring, and enforcement. Older models require copying, exporting, or centralizing data to scan it, while ZDM ensures that all security actions happen directly where data resides.

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ZDM removes egress risk -shrinking the attack surface and reducing regulatory issues. For organizations juggling large cloud deployments and tight data residency rules, ZDM isn’t just an improvement - it's essential. Groups like the Cloud Security Alliance and new privacy regulations are moving the industry toward designs that build in privacy and non-stop protection.

Risks of Data Movement: Compliance, Cost, and Egress Exposure

Every time data is copied, exported, or streamed out of its native environment, new risks arise. Data movement creates challenges such as:

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• Egress risk: Data at rest or in transit outside its original environment  increases risk of breach, especially as those environments may be less secure.
• Compliance and regulatory exposure: Moving data across borders or different clouds can break geo-fencing and privacy controls, leading to potential violations and steep fines.
• Loss of context and control: Scattered data makes it harder to monitor everything, leaving gaps in visibility.
• Rising total cost of ownership (TCO): Scanning and classification can incur heavy cloud compute costs - so efficiency matters.  Exporting or storing data, especially shadow data, drives up storage, egress, and compliance costs as well.

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As more businesses rely on data, moving it unnecessarily only increases the risk - especially with fast-changing cloud regulations.

Legacy and Competitor Gaps: Why Data Movement Still Happens

Not every security vendor practices true zero data movement, and the differences are notable. Products from Cyera, Securiti, or older platforms still require temporary data exporting or duplication for analysis. This might offer a quick setup, but it exposes users to egress risks, insider threats, and compliance gaps - problems that are worse in regulated fields.

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Competitors like Cyera often rely on shortcuts that fall short of ZDM’s requirements. Securiti and similar providers depend on connectors, API snapshots, or central data lakes, each adding potential risks and spreading data further than necessary. With ZDM, security operations like monitoring and classification happen entirely locally, removing the need to trust external storage or aggregation. For more detail on how data movement drives up risk.

The Business Value of Zero Data Movement DSPM

Zero data movement DSPM changes the equation for businesses:

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• Designed for compliance: Data remains within controlled environments, shrinking audit requirements and reducing breach likelihood.
• Lower TCO and better efficiency: Eliminates hidden expenses from extra storage, duplicate assets, and exporting to external platforms.
• Regulatory clarity and privacy: Supports data sovereignty, cross-border rules, and new zero trust frameworks with an egress-free approach.

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Sentra’s agentless, cloud-native DSPM provides these benefits by ensuring sensitive data is never moved or copied. And Sentra delivers these benefits at scale - across multi-petabyte enterprise environments - without the performance and cost tradeoffs others suffer from. Real scenarios show the results: financial firms keep audit trails without data ever leaving allowed regions. Healthcare providers safeguard PHI at its source. Global SaaS companies secure customer data at scale, cost-effectively while meeting regional rules.

Future-Proofing Data Security: ZDM as the New Standard

With data volumes expected to hit 181 zettabytes in 2025, older protection methods that rely on moving data can’t keep up. Zero data movement architecture meets today's security demands and supports zero trust, metadata-driven access, and privacy-first strategies for the future.

Companies wanting to avoid dead ends should pick solutions that offer unified discovery, classification and policy enforcement without egress risk. Sentra’s ZDM architecture makes this possible, allowing organizations to analyze and protect information where it lives, at cloud speed and scale.

Conclusion

Zero Data Movement is more than a technical detail - it's a new architectural standard for any organization serious about risk control, compliance, and efficiency. As data grows and regulations become stricter, the old habits of moving, copying, or centralizing sensitive data will no longer suffice.

Sentra stands out by delivering a zero data movement DSPMplatform that's agentless, real-time, and truly multicloud. For security leaders determined to cut egress risk, lower compliance spending, and get ahead in privacy, ZDM is the clear path forward.

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As organizations scramble to secure their sprawling cloud environments and deploy AI, many are facing a stark realization: handling petabyte-scale data is now a basic security requirement. With sensitive information multiplying across multiple clouds, SaaS, and AI-driven platforms, security leaders can't treat true data security at scale as a simple add-on or upgrade.

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At the same time, speeding up digital transformation means higher and less visible operational costs for handling this data surge. Older Data Security Posture Management (DSPM) tools, especially those boasting broad, indiscriminate scans as evidence of their scale, are saddling organizations with rising cloud bills, slowdowns, and dangerous gaps in visibility. The costs of securing petabyte-scale data are now economic and technical, demanding efficiency instead of just scale. Sentra solves this with a highly-efficient cloud-native design, delivering 10x lower cloud compute costs.

Why Petabyte Scale is a Security Requirement

Data environments have exploded in both size and complexity. For Fortune 500 companies, fast-growing SaaS providers, and global organizations, data exists across public and hybrid clouds, business units, regions, and a stream of new applications.

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Regulations such as GDPR, HIPAA, and rules from the SEC now demand current data inventories and continuous proof of risk management. In this environment, defending data at the petabyte level is now essential. Failing to classify and monitor this data efficiently means risking compliance and losing business trust. Security teams are feeling the strain. I meet security teams everyday and too many of them still struggle with data visibility and are already seeing the cracks forming in their current toolset as data scales.

The Hidden Cost of Inefficient DSPM: API Calls and Egress Bills

How DSPM tools perform scanning and discovery drives the real costs of securing petabyte-scale data. Some vendors highlight their capacity to scan multiple petabytes daily. But here's the reality: scanning everything, record by record, relying on huge numbers of API calls, becomes very expensive as your data estate grows.

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Every API call can rack up costs, and all the resulting data egress and compute add up too. Large organizations might spend tens of thousands of dollars each month just to track what’s in their cloud. Even worse, older "full scan" DSPM strategies jam up operations with throttling, delays, and a flood of alerts that bury real risk. These legacy approaches simply don’t scale, and organizations relying on them end up paying more while knowing less.

Cyera’s "Petabyte Scale" Claims: At What Cloud Cost?

Cyera promotes its tool as an AI-native, agentless DSPM that can scan as much as 2 petabytes daily . While that’s an impressive technical achievement, the strategy of scanning everything leads directly to massive cloud infrastructure costs: frequent API hits, heavy egress, and big bills from AWS, Azure, and GCP.

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At scale, these charges don’t just appear on invoices, they can actually stop adoption and limit security’s effectiveness. Cloud operations teams face API throttling, slow results, and a surge in remediation tickets as risks go unfiltered. In these fast-paced environments, recognizing the difference between a real threat and harmless data comes down to speed. The Bedrock Security blog points out how inefficient setups buckle under this weight, leaving teams stuck with lagging visibility and more operational headaches.

Sentra’s 10x Efficiency: Optimized Scanning for Real-World Scale

Sentra takes another route to manage the costs of securing petabyte-scale data. By combining agentless discovery with scanning guided by context and metadata, Sentra uses pattern recognition and an AI-driven clustering algorithm designed to detect machine-generated content—such as log files, invoices, and similar data types. By intelligently sampling data within each cluster, Sentra delivers efficient scanning while reducing scanning costs.

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This approach enables data scanning to be prioritized based on risk and business value, rather than wasting time and money scanning the same data over and over again, skipping unnecessary API calls, lowering egress, and keeping cloud bills in check.

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Large organizations gain a 10x efficiency edge: quicker classification of data, instant visibility into actual threats, lower operational expenses, and less demand on the network. By focusing attention only where it matters, Sentra matches data security posture management to the demands of current cloud growth and regulatory requirements.

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This makes it possible for organizations to hit regulatory and audit targets without watching expenses spiral or opening up security gaps.Sentra offers multiple sampling levels, Quick (default), Moderate, Thorough, and Full, allowing customers to tailor their scanning strategy to balance cost and accuracy. For example, a highly regulated environment can be configured for a full scan, while less-regulated environments can use more efficient sampling. Petabyte-scale security gives the user complete control of their data enterprise and turns into something operationally and financially sustainable, rather than a technical milestone with a hidden cost. 

Efficiency is Non-Negotiable

Fortune 500 companies and digital-first organizations can’t treat efficiency as optional. Inefficient DSPM tools pile on costs, drain resources, and let vulnerabilities slip through, turning their security posture into a liability once scale becomes a factor. Sentra’s platform shows that efficiency is security: with targeted scanning, real context, and unified detection and response, organizations gain clarity and compliance while holding down expenses.

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Don’t let your data protection approach crumble under petabyte-scale pressure. See what Sentra can do, reduce costs, and keep essential data secure - before you end up responding to breaches or audit failures.

Conclusion

Securing data at the petabyte level isn't some future aspiration - it's the standard for enterprises right now. Treating it as a secondary feature isn’t just shortsighted; it puts your company at risk, financially and operationally.

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The right DSPM architecture brings efficiency, not just raw scale. Sentra delivers real-time, context-rich security posture with far greater efficiency, so your protection and your cloud spending can keep up with your growing business. Security needs to grow along with scale. Rising costs and new risks shouldn’t grow right alongside it.

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Want to see how your current petabyte security posture compares? Schedule a demo and see Sentra’s efficiency for yourself.

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Atlassian tools such as Jira and Confluence are the beating heart of software development and IT operations. They power everything from sprint planning to debugging production issues. But behind their convenience lies a less-visible problem: these collaboration platforms quietly accumulate vast amounts of sensitive data often over years that security teams can’t easily monitor or control.

The Problem: Sensitive Data Hidden in Plain Sight

Many organizations rely on Jira to manage tickets, track incidents, and communicate across teams. But within those tickets and attachments lies a goldmine of sensitive information:

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• Credentials and access keys to different environments.
  
• Intellectual property, including code snippets and architecture diagrams.
  
• Production data used to reproduce bugs or validate fixes — often in violation of data-handling regulations.
  
• Real customer records shared for troubleshooting purposes.
  
  

This accumulation isn’t deliberate; it’s a natural byproduct of collaboration. However, it results in a long-tail exposure risk - historical tickets that remain accessible to anyone with permissions.

The Insider Threat Dimension

Because Jira and Confluence retain years of project history, employees and contractors may have access to data they no longer need. In some organizations, teams include offshore or external contributors, multiplying the risk surface. Any of these users could intentionally or accidentally copy or export sensitive content at any moment.

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Why Sensitive Data Is So Hard to Find

Sensitive data in Atlassian products hides across three levels, each requiring a different detection approach:

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1. Structured Data (Records): Every ticket or page includes structured fields - reporter, status, labels, priority. These schemas are customizable, meaning sensitive fields can appear unpredictably. Security teams rarely have visibility or consistent metadata across instances.
   
   
2. Unstructured Data (Descriptions & Discussions): Free-text fields are where developers collaborate — and where secrets often leak. Comments can contain access tokens, internal URLs, or step-by-step guides that expose system details.
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3. Unstructured Data (Attachments): Screenshots, log files, spreadsheets, code exports, or even database snapshots are commonly attached to tickets. These files may contain credentials, customer PII, or proprietary logic, yet they are rarely scanned or governed.

The Challenge for Security Teams

Traditional security tools were never designed for this kind of data sprawl. Atlassian environments can contain millions of tickets and pages, spread across different projects and permissions. Manually auditing this data is impractical. Even modern DLP tools struggle to analyze the context of free text or attachments embedded within these platforms.

Compliance teams face an uphill battle: GDPR, HIPAA, and SOC 2 all require knowing where sensitive data resides. Yet in most Atlassian instances, that visibility is nonexistent.

How Sentra Solves the Problem

Sentra takes a different approach. Its cloud-native data security platform discovers and classifies sensitive data wherever it lives - across SaaS applications, cloud storage, and on-prem environments. When connecting your atlassian environment, Sentra delivers visibility and control across every layer of Jira and Confluence.

Comprehensive Coverage

Sentra delivers consistent data governance across SaaS and cloud-native environments. When connected to Atlassian Cloud, Sentra’s discovery engine scans Jira and Confluence content to uncover sensitive information embedded in tickets, pages, and attachments, ensuring full visibility without impacting performance.

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In addition, Sentra’s flexible architecture can be extended to support hybrid environments, providing organizations with a unified view of sensitive data across diverse deployment models.

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AI-Based Classification

Using advanced AI models, Sentra classifies data across all three tiers:

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• Structured metadata, identifying risky fields and tags.
  
• Unstructured text, analyzing ticket descriptions, comments, and discussions for credentials, PII, or regulated data.
  
• Attachments, scanning files like logs or database snapshots for hidden secrets.
  
  

This contextual understanding distinguishes between harmless content and genuine exposure, reducing false positives.

Full Lifecycle Scanning

Sentra doesn’t just look at new tickets, it scans the entire historical archive to detect legacy exposure, while continuously monitoring for ongoing changes. This dual approach helps security teams remediate existing risks and prevent future leaks.

The Real-World Impact

Organizations using Sentra gain the ability to:

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• Prevent accidental leaks of credentials or production data in collaboration tools.
  
• Enforce compliance by mapping sensitive data across Jira and Confluence.
  
• Empower DevOps and security teams to collaborate safely without stifling productivity.

Conclusion

Collaboration is essential, but it should never compromise data security. Atlassian products enable innovation and speed, yet they also hold years of unmonitored information. Sentra bridges that gap by giving organizations the visibility and intelligence to discover, classify, and protect sensitive data wherever it lives, even in Jira and Confluence.

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