Securing the Cloud: Advanced Strategies for Continuous Data Monitoring

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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The recent exposure of customer data through a compromised Gainsight integration within Salesforce environments is more than an isolated event - it’s a sign of a rapidly evolving class of SaaS supply-chain threats. Even trusted AppExchange partners can inadvertently create access pathways that attackers exploit, especially when OAuth tokens and machine-to-machine connections are involved. This post explores what happened, why today’s security tooling cannot fully address this scenario, and how data-centric visibility and identity governance can meaningfully reduce the blast radius of similar breaches.

A Recap of the Incident

In this case, attackers obtained sensitive credentials tied to a Gainsight integration used by multiple enterprises. Those credentials allowed adversaries to generate valid OAuth tokens and access customer Salesforce orgs, in some cases with extensive read capabilities. Neither Salesforce nor Gainsight intentionally misconfigured their systems. This was not a product flaw in either platform. Instead, the incident illustrates how deeply interconnected SaaS environments have become and how the security of one integration can impact many downstream customers.

Understanding the Kill Chain: From Stolen Secrets to Salesforce Lateral Movement

The attackers’ pathway followed a pattern increasingly common in SaaS-based attacks. It began with the theft of secrets; likely API keys, OAuth client secrets, or other credentials that often end up buried in repositories, CI/CD logs, or overlooked storage locations. Once in hand, these secrets enabled the attackers to generate long-lived OAuth tokens, which are designed for application-level access and operate outside MFA or user-based access controls.

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What makes OAuth tokens particularly powerful is that they inherit whatever permissions the connected app holds. If an integration has broad read access, which many do for convenience or legacy reasons, an attacker who compromises its token suddenly gains the same level of visibility. Inside Salesforce, this enabled lateral movement across objects, records, and reporting surfaces far beyond the intended scope of the original integration. The entire kill chain was essentially a progression from a single weakly-protected secret to high-value data access across multiple Salesforce tenants.

Why Traditional SaaS Security Tools Missed This

Incident response teams quickly learned what many organizations are now realizing: traditional CASBs and CSPMs don’t provide the level of identity-to-data context necessary to detect or prevent OAuth-driven supply-chain attacks.

CASBs primarily analyze user behavior and endpoint connections, but OAuth apps are “non-human identities” - they don’t log in through browsers or trigger interactive events. CSPMs, in contrast, focus on cloud misconfigurations and posture, but they don’t understand the fine-grained data models of SaaS platforms like Salesforce. What was missing in this incident was visibility into how much sensitive data the Gainsight connector could access and whether the privileges it held were appropriate or excessive. Without that context, organizations had no meaningful way to spot the risk until the compromise became public.

Sentra Helps Prevent and Contain This Attack Pattern

Sentra’s approach is fundamentally different because it starts with data: what exists, where it resides, who or what can access it, and whether that access is appropriate. Rather than treating Salesforce or other SaaS platforms as black boxes, Sentra maps the data structures inside them, identifies sensitive records, and correlates that information with identity permissions including third-party apps, machine identities, and OAuth sessions.

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One key pillar of Sentra’s value lies in its DSPM capabilities. The platform identifies sensitive data across all repositories, including cloud storage, SaaS environments, data warehouses, code repositories, collaboration platforms, and even on-prem file systems. Because Sentra also detects secrets such as API keys, OAuth credentials, private keys, and authentication tokens across these environments, it becomes possible to catch compromised or improperly stored secrets before an attacker ever uses them to access a SaaS platform.

Another area where this becomes critical is the detection of over-privileged connected apps. Sentra continuously evaluates the scopes and permissions granted to integrations like Gainsight, identifying when either an app or an identity holds more access than its business purpose requires. This type of analysis would have revealed that a compromised integrated app could see far more data than necessary, providing early signals of elevated risk long before an attacker exploited it.

Sentra further tracks the health and behavior of non-human identities. Service accounts and connectors often rely on long-lived credentials that are rarely rotated and may remain active long after the responsible team has changed. Sentra identifies these stale or overly permissive identities and highlights when their behavior deviates from historical norms. In the context of this incident type, that means detecting when a connector suddenly begins accessing objects it never touched before or when large volumes of data begin flowing to unexpected locations or IP ranges.

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Finally, Sentra’s behavior analytics (part of DDR) help surface early signs of misuse. Even if an attacker obtains valid OAuth tokens, their data access patterns, query behavior, or geography often diverge from the legitimate integration. By correlating anomalous activity with the sensitivity of the data being accessed, Sentra can detect exfiltration patterns in real time—something traditional tools simply aren’t designed to do.

The 2026 Outlook: More Incidents Are Coming

The Gainsight/Salesforce incident is unlikely to be the last of its kind. The speed at which enterprises adopt SaaS integrations far exceeds the rate at which they assess the data exposure those integrations create. OAuth-based supply-chain attacks are growing quickly because they allow adversaries to compromise one provider and gain access to dozens or hundreds of downstream environments. Given the proliferation of partner ecosystems, machine identities, and unmonitored secrets, this attack vector will continue to scale.

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Prediction:
Unless enterprises add data-centric SaaS visibility and identity-aware DSPM, we should expect three to five more incidents of similar magnitude before summer 2026.

Conclusion

The real lesson from the Gainsight/Salesforce breach is not to reduce reliance on third-party SaaS providers as modern business would grind to a halt without them. The lesson is that enterprises must know where their sensitive data lives, understand exactly which identities and integrations can access it, and ensure those privileges are continuously validated. Sentra provides that visibility and contextual intelligence, making it possible to identify the risks that made this breach possible and help to prevent the next one.

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