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Every file shared with the wrong person, every public Google Drive link left open for months, every OAuth app quietly siphoning data in the background -- these are the moments where data leakage happens. And in a world where most critical data lives inside SaaS applications, the old playbook for protecting sensitive data no longer works.
This guide breaks down what data leakage prevention actually looks like in modern environments, what causes data leaks, and how security teams can build practical, scalable defenses across their SaaS stack.
What is data leakage?
Data leakage is the unauthorized or unintended exposure, transfer, or disclosure of sensitive information.
It covers both accidental data leaks like:
- copy and pasting sensitive data into ChatGPT, Claude, or Gemini
- oversharing Google Drive folder by using ‘Anyone with the link can access’ sharing policy
And deliberate incidents where someone intentionally moves confidential data outside approved channels, like:
- sharing a strategy document to a personal email before submitting their two weeks
- accessing company data nobody removed them from after leaving for a competitor
Data leakage differs from a data breach (confirmed unauthorized access by a threat actor) and data exfiltration (deliberate theft of data), but these concepts overlap significantly.
Leaks often precede or enable breaches, and exfiltration is essentially malicious leakage at scale.
Modern data leakage spans endpoints, networks, cloud environments, and especially SaaS applications like Google Workspace, Microsoft 365, Slack, and Salesforce.
For security teams, preventing data leakage is a core responsibility that directly impacts regulatory compliance, customer trust, and the bottom line, and it must be covered at every level where data lives within your business.
Common causes of data leakage in today’s environments
Understanding what actually causes data leaks day to day is the first step toward preventing them. And here’s the uncomfortable truth: very few start with a sophisticated hack. Most start with a trusted identity, a legitimate tool, and a gap nobody was watching. Here are the most common culprits across enterprises and mid-market organizations:
1. Human error and social engineering.
Phishing remains a top attack vector, but everyday mistakes are just as dangerous: personal email shares, overly broad permissions, pasting sensitive information into unapproved AI tools. In fact, 95% of security breaches involve human error or insider threats.
And it only takes one person. In April 2026, attackers used social engineering to deceive a single Carnival employee and take over their account. Carnival’s security team spotted the activity on April 14, but it took another eight days to confirm customer data had already been exfiltrated. One employee, one deception, one compromised account.
2. Insider threats.
Careless insiders violate policy unintentionally through weak passwords or oversharing. Malicious insiders deliberately exfiltrate proprietary data for personal gain, and they rarely look malicious while they’re doing it.
A former Google engineer quietly moved more than 1,200 confidential documents (roughly 14,000 pages) of AI-related material out of the company over 12 months. He pasted the content into a note-taking app, converted it to PDFs, and uploaded them to a personal cloud account. Nothing about it looked like a hack. It looked like work. He has since been convicted of economic espionage and trade secret theft.
3. Weak or stolen credentials.
Once credentials are compromised, attackers can reach everything behind them: email, file shares, and SaaS apps. At Madison Square Garden Sports, hackers phished a low-level employee over the phone, got them to approve 2FA prompts and start a self-service password reset, and walked straight into Entra ID. From there, they went to SharePoint.
4. Data extortion and double-extortion ransomware.
Since roughly 2020, ransomware groups have increasingly paired encryption with the threat of leaking or selling stolen data. Groups like ShinyHunters often skip encryption entirely; “pay or leak” is the business model. In the MSG breach, attackers exfiltrated ~46 GB of data and 26M+ records and set a June 15 deadline.
5. Misconfigurations and poor access governance.
Over-permissive sharing settings create persistent, silent leakage channels: “anyone with the link” in Google Drive, public Slack channels with guest access, stale permissions for former contractors.
An example of this is Scale AI. Shortly after a deal with Meta valued the company at $14.8 billion, it came out that Scale AI had been storing highly sensitive client work in Google Drive with no sharing guardrails in place. Many files were simply set to “anyone with the link can view.” Thousands of documents were exposed, including AI training materials from customers like Meta and Google, internal employee ratings, pay information, and contractor emails.
Many of them were clearly labeled “confidential.” No hacker, no exploit, no breach. Just links that anyone could open.
Types of data leakage: accidental, malicious, and third-party
Different types of data leakage require fundamentally different detection and response strategies. Lumping them together leads to policies that are either too rigid or too loose.
Accidental leakage covers overshared documents, public file links created without realizing the implications, incorrect permissions on shared drives, and sharing sensitive data to personal Gmail or Dropbox accounts. These are accidental data leaks driven by convenience and lack of awareness – not malice.
Malicious leakage involves deliberate data exfiltration. Employees taking customer lists or source code before leaving, sharing confidential files to personal accounts, or using USB devices to walk out with critical data. Insider threat detection is critical for organizations relying on SaaS applications, and insider risk management programs need to correlate behavior signals - like a spike in downloads right before a resignation - with identity context to catch these events early. Identity-based threats are among the hardest to detect because the actor has legitimate credentials.
Third-party and supply chain leakage happens when vendors or contractors with elevated permissions misuse their access. Shadow apps and non-human identities (NHIs) like service accounts and OAuth apps are frequently overlooked channels for unapproved data transfers. NHIs now outnumber human identities in many enterprise SaaS environments, and they often retain access long after their original purpose has expired.
Data leakage prevention in SaaS
SaaS collaboration tools are now the dominant source of business data – and therefore the dominant source of data leakage risk. Customer data, financial information, HR records, intellectual property, and trade secrets all live inside platforms like Google Workspace, Slack, and more.
Typical SaaS leakage scenarios include:
- Overshared Google Drive folders exposing confidential files to anyone with the link
- Contractors retaining access to sensitive data long after the business relationship ends
- Employees pasting sensitive data into LLMs like ChatGPT, Claude, and Gemini
- Public Slack channels where guests or external users can access data they shouldn't
- Unmanaged OneDrive/SharePoint links configured for external sharing by default
Generic network or endpoint DLP simply can't see what's happening inside SaaS applications. When an employee shares a Google Drive folder externally or a contractor downloads thousands of Salesforce records, traditional DLP tools are blind. That's why SaaS-native data loss prevention – SaaS DLP – exists.
SaaS DLP works by connecting via APIs into applications like Google Workspace, Microsoft 365, Slack, and Salesforce.
It scans and classifies assets, analyzes permissions, and monitors user actions in real time – covering both new activity and historical content that may have been exposed for months or years. DLP solutions typically operate at this API layer to maintain deep visibility without disrupting user workflows.
Data access governance is the operational counterpart: continuously right-sizing permissions, enforcing least privilege, removing stale access, and cleaning up public links across cloud apps.
Typical SaaS DLP policies include blocking external sharing of HR documents, restricting downloads of sensitive files to unmanaged devices, limiting sharing to approved domains, and blocking sensitive data from leaving the organization through unauthorized channels.
What makes modern SaaS DLP effective is identity context. Knowing that someone just changed roles, was flagged by HR, or is accessing files outside their normal behavioral baseline dramatically reduces false positives and helps security teams focus on real risk.
SaaS DLP solutions help organizations mitigate insider threats and protect sensitive communications - but only when they incorporate role, department, employment status, device posture, and behavioral baselines into their detection logic.
Best practices for preventing data leakage across the enterprise
Data leakage prevention in AI and generative AI workflows
The surge of AI adoption has created entirely new data leakage vectors that most organizations aren't prepared for. AI systems handle large volumes of sensitive information, and data leakage prevention is essential for AI system security.
Sensitive data – PII, source code, trade secrets, financial data – can leak into AI training datasets, prompts, logs, and model outputs.
Employees pasting confidential data into public LLMs, uploading confidential files to AI-powered productivity tools, or fine-tuning models on production customer data all create exposure.
Data leakage in AI can lead to significant financial losses, regulatory penalties, and competitive harm.
Risks include prompt injection attacks, data extraction attacks on model outputs, and inadvertent uploads of regulated content to unmanaged models.
Organizations must control access to prevent data leakage in AI through specific controls:
- Redaction and anonymization tools that strip sensitive data before it enters AI workflows
- Strict access controls on training data with separate environments for test versus production
- Logging of all prompt interactions for audit and compliance
- Vetting AI vendors for safeguarding sensitive information and regulatory alignment
- Governance policies covering which AI apps with risky OAuth permissions are permitted
AI-related data leaks can violate regulations and contractual obligations if regulated or customer data is ingested into unmanaged models - a risk that many organizations only discover after the damage is done.
How DoControl prevents data leakage in SaaS environments
DoControl is a SaaS security and data protection platform purpose-built to address the data leakage challenges that traditional DLP tools miss.
Rather than just alerting on policy violations, DoControl delivers identity-driven, context-aware, automated data leak prevention inside the SaaS applications where your data actually lives, and now in the browser, where your people actually work with it.
Core capabilities include:
- Automated discovery and classification of SaaS assets across Google Workspace, Microsoft 365, Slack, Salesforce, and other connected platforms, covering both new activity and historical content where sensitive data resides.
- Exposure analysis and risk prioritization that identifies overexposed folders, public links, stale permissions, and high-risk OAuth apps, helping you protect data based on actual risk rather than guesswork.
- Identity and role context using HRIS and IdP signals to understand role changes, offboarding events, department, and behavioral baselines. This reduces false positives and lets security teams focus on genuine threats.
- Deep visibility into human and non-human identities, including service accounts, OAuth-connected applications, and API keys, which are frequent but overlooked channels for data leakage.
- Browser-level data protection, protecting your data from SaaS to now the browser. As part of Spin.AI, DoControl customers can extend protection beyond the SaaS layer with enterprise browser security, extension risk assessment backed by an engine that has evaluated 1M browser extensions, applications, and AI tools. That means visibility into the risky extensions and AI tools your workforce uses, so sensitive data stays protected even after it leaves the app.
- Automated remediation workflows that revoke public links, remove external collaborators, block anomalous downloads, revoke risky shadow apps, time-box access after role changes, and escalate to managers via Slack or email. These workflows run 24/7 without manual intervention.
The result: fewer data exfiltration incidents, fewer manual tickets, tighter control over who can access data, and better alignment for audits, all while preserving the productivity authorized users need to do their jobs.
DoControl protects Google Workspace, Microsoft 365, Slack, Salesforce, and other SaaS platforms, all the way through to the browser, by closing oversharing gaps without blocking legitimate collaboration.
Next steps: find your data leakage before someone else does
Data leakage prevention in a SaaS-first world isn’t a project with a finish line. It’s an ongoing discipline. And every incident in this post, from Scale AI’s public Google Docs to Intel’s 18,000 “Top Secret” files, has one thing in common: the exposure existed long before anyone noticed it.
So here’s the real question: what’s exposed in your environment right now?
Most security teams can’t answer that with confidence. Public links pile up. External collaborators never get removed. Former employees and contractors keep access long after they’re gone. None of it triggers an alert, because none of it looks like an attack. You can’t protect data you can’t see.
Start with a free SaaS risk assessment. DoControl’s Free Risk Assessment gives you a clear picture of your SaaS data exposure in minutes:
- Your external sharing risks: which files are shared publicly, with personal accounts, or with external domains.
- Your sensitive data exposure: where confidential data lives and who can access it.
- Who still holds the keys: including former employees and contractors who still have access to company data.
- Your SaaS security score: a baseline to measure progress against.
Then act on what you find:
- Lock down your sharing configurations. Tighten Google Workspace defaults, audit external collaborators, and remove public links on confidential files, starting with the highest-risk exposure your assessment surfaces.
- Align DLP policies with how your people actually work. Policies written for a pre-SaaS world create friction without reducing risk.
- Build a 12-month DLP roadmap covering technology deployment, process changes, and recurring security awareness training.
Bottom line? The organizations that avoid the headlines aren’t the ones with the most alerts. They’re the ones that know exactly where their data is, who has access, and what’s exposed, and they fix it before attackers find it first.
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