Automating Sensitive Data Classification in Audio, Image and Video Files

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.

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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Unstructured data is the fastest-growing, least-governed, and most dangerous class of enterprise data. Emails, Slack messages, PDFs, screenshots, presentations, code repositories, logs, and the endless stream of GenAI-generated content — this is where the real risk lives.

The Unstructured data dilemma is this: 80% of your organization’s data is essentially invisible to your current security tools, and the volume is climbing by up to 65% each year. This isn’t just a hypothetical - it’s the reality for enterprises as unstructured data spreads across cloud and SaaS platforms. Yet, most Data Security Posture Management (DSPM) solutions - often called DSPM 1.0 - were never built to handle this explosion at petabyte scale. Especially legacy vendors and first-generation players like Cyera — were never designed to handle unstructured data at scale. Their architectures, classification engines, and scanning models break under real enterprise load.

Looking ahead to 2026, unstructured data security risk stands out as the single largest blind spot in enterprise security. If overlooked, it won’t just cause compliance headaches and soaring breach costs - it could put your organization in the headlines for all the wrong reasons.

The 80% Problem: Unstructured Data Dominates Your Risk

The Scale You Can’t Ignore - Over 80% of enterprise data is unstructured
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• Unstructured data is growing 55-65% per year; by 2025, the world will store more than 180 zettabytes of it.
• 95% of organizations say unstructured data management is a critical challenge but less than 40% of data security budgets address this high-risk area. Unstructured data is everywhere: cloud object stores, SaaS apps, collaboration tools, and legacy file shares. Unlike structured data in databases, it often lacks consistent metadata, access controls, or even basic visibility. This “dark data” is behind countless breaches, from accidental file exposures and overshared documents to sensitive AI training datasets left unmonitored.

The Business Impact - The average breach now costs $4-4.9M, with unstructured data often at the center.

• Poor data quality, mostly from unstructured sources, costs the U.S. economy $3.1 trillion each year.
• More than half of organizations report at least one non-compliance incident annually, with average costs topping $1M. The takeaway: Unstructured data isn’t just a storage problem.

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Why DSPM 1.0 Fails: The Blind Spots of Legacy Approaches

Traditional Tools Fall Short in Cloud-First, Petabyte-Scale Environments

Legacy DSPM and DCAP solutions, such as Varonis or Netwrix - were built for an era when data lived on-premises, followed predictable structures, and grew at a manageable pace.

In today’s cloud-first reality, their limitations have become impossible to ignore:
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• Discovery Gaps: Agent-based scanning can’t keep up with sprawling, constantly changing cloud and SaaS environments. Shadow and dark data across platforms like Google Drive, Dropbox, Slack, and AWS S3 often go unseen.
• Performance Limits: Once environments exceed 100 TB, and especially as they reach petabyte scale—these tools slow dramatically or miss data entirely.
• Manual Classification: Most legacy tools rely on static pattern matching and keyword rules, causing them to miss sensitive information hidden in natural language, code, images, or unconventional file formats.
• Limited Automation: They generate alerts but offer little or no automated remediation, leaving security teams overwhelmed and forcing manual cleanup.
• Siloed Coverage: Solutions designed for on-premises or single-cloud deployments create dangerous blind spots as organizations shift to multi-cloud and hybrid architectures.

Example: Collaboration App Exposure

A global enterprise recently discovered thousands of highly sensitive files—contracts, intellectual property, and PII—were unintentionally shared with “anyone with the link” inside a cloud collaboration platform. Their legacy DSPM tool failed to identify the exposure because it couldn’t scan within the app or detect real-time sharing changes.

Further, even Emerging DSPM tools often rely on pattern matching or LLM-based scanning. These approaches also fail for three reasons:

• Inaccuracy at scale: LLMs hallucinate, mislabel, and require enormous compute.
• Cost blow-ups: Vendors pass massive cloud bills back to customers or incur inordinate compute cost.
• Architectural limitations: Without clustering and elastic scaling, large datasets overwhelm the system.

This is exactly where Cyera and legacy tools struggle - and where Sentra’s SLM-powered classifier thrives with >99% accuracy at a fraction of the cost.

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The New Mandate: Securing Unstructured Data in 2026 and Beyond

GenAI, and stricter privacy laws (GDPR, CCPA, HIPAA) have raised the stakes for unstructured data security. Gartner now recommends Data Access Governance (DAG) and AI-driven classification to reduce oversharing and prepare for AI-centric workloads.
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What Modern Security Leaders Need - Agentless, Real-Time Discovery: No deployment hassles, continuous visibility, and coverage for unstructured data stores no matter where they live.

• Petabyte-Scale Performance: Scan, classify, and risk-score all data, everywhere it lives.
• AI-Driven Deep Classification: Use of natural language processing (NLP), Domain-specific  Small Language Models (SLMs), and context analysis for every unstructured format.
• Automated Remediation: Playbooks that fix exposures, govern permissions, and ensure compliance without manual work.
• Multi-Cloud & SaaS Coverage: Security that follows your data, wherever it goes.

Sentra: Turning the 80% Blind Spot into a Competitive Advantage

Sentra was built specifically to address the risks of unstructured data in 2026 and beyond. There are nuances involved in solving this.  Selecting an appropriate solution is key to a sustainable approach. Here’s what sets Sentra apart:
 

• Agentless Discovery Across All Environments:Instantly scans and classifies unstructured data across AWS, Azure, Google, M365, Dropbox, legacy file shares, and more - no agents required, no blind spots left behind.
• Petabyte-Tested Performance:Designed for Fortune 500 scale, Sentra keeps speed and accuracy high across petabytes, not just terabytes.
• AI-Powered Deep Classification:Our platform uses advanced NLP, SLMs, and context-aware algorithms to classify, label, and risk-score every file - including code, images, and AI training data, not just structured fields.
• Continuous, Context-Rich Visibility:Real-time risk scoring, identity and access mapping, and automated data lineage show not just where data lives, but who can access it and how it’s used.
• Automated Remediation and Orchestration: Sentra goes beyond alerts. Built-in playbooks fix permissions, restrict sharing, and enforce policies within seconds.
• Compliance-First, Audit-Ready: Quickly spot compliance gaps, generate audit trails, and reduce regulatory risk and reporting costs.     

During a recent deployment with a global financial services company, Sentra uncovered 40% more exposed sensitive files than their previous DSPM tool. Automated remediation covered over 10 million documents across three clouds, cutting manual investigation time by 80%.

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Actionable Takeaways for Security Leaders 

1. Put Unstructured Data at the Center of Your 2026 Security Plan: Make sure your DSPM strategy covers all data, especially “dark” and shadow data in SaaS, object stores, and collaboration platforms.

2.  Choose Agentless, AI-Driven Discovery: Legacy, agent-based tools can’t keep up. And underperforming emerging tools may not adequately scale.  Look for continuous, automated scanning and classification that scales with your data.

3.  Automate Remediation Workflows: Visibility is just the start; your platform should fix exposures and enforce policies in real time.

4.  Adopt Multi-Cloud, SaaS-Agnostic Solutions: Your data is everywhere, and your security should be too. Ensure your solution supports all of your unstructured data repositories.

5.  Make Compliance Proactive: Use real-time risk scoring and automated reporting to stay ahead of auditors and regulators.

Conclusion: Ready for the 80% Challenge? ‍

With petabyte-scale, cloud-first data, ignoring unstructured data risk is no longer an option. Traditional DSPM tools can’t keep up, leaving most of your data - and your business - vulnerable. Sentra’s agentless, AI-powered platform closes this gap, delivering the discovery, classification, and automated response you need to turn your biggest blind spot into your strongest defense. See how Sentra uncovers your hidden risk - book an instant demo today.

Don’t let unstructured data be your organization’s Achilles’ heel. With Sentra, enterprises finally have a way to secure the data that matters most.

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