Data Leakage in 2025: Types, Causes, and 5 Defensive Measures

What Is Data Leakage? 

Data leakage refers to the unauthorized transmission, exposure, or disclosure of sensitive information to an external or untrusted environment. This exposure can occur deliberately or unintentionally and often involves confidential business data, intellectual property, personal details, or financial records. 

Data leakage introduces a major risk for organizations because once data leaves a secure environment, regaining control is almost impossible. The ramifications include regulatory penalties, reputational damage, and financial losses. A key aspect of data leakage is its subtle nature.

Unlike an outright cyberattack, data leaks may happen quietly over time, making them more difficult to detect. Sources of leakage range from employee carelessness, misconfigured databases, or vulnerable software to malicious insiders. In many cases, data leakage is only identified after the damage becomes apparent, highlighting the need for prevention, detection, and response strategies.

This is part of a series of articles about DLP

Data Leakage vs. Data Breach 

Although the terms data leakage and data breach are often used interchangeably, they refer to different security events. 

A data breach is typically defined as a confirmed incident where unauthorized individuals gain access to data, often through hacking, malware, or exploitation of vulnerabilities. Breaches are usually intentional and result from direct attacks where information is actively extracted from systems.

Data leakage often lacks the overt intrusion associated with breaches. Leakage may result from poorly configured systems, human error, or inadvertent sharing via emails or cloud platforms. While both have severe consequences, leakage is characterized by passive unsanctioned exposure rather than active attack. 

Organizations must develop nuanced policies to address both breaches and leaks, as their origins and risk mitigation strategies differ significantly.

Types of Data Leakage 

Primary types of data leakage include:

1. Accidental Exposure Through Misconfiguration

Accidental data leakage frequently arises from system or application misconfigurations. Examples include publicly accessible cloud storage buckets, unsecured web servers, or default passwords left unchanged on critical infrastructure. When administrators fail to set proper permissions, sensitive information like databases containing customer details can be indexed by search engines or discovered by threat actors using basic network scanning tools. Organizations, especially those rapidly adopting cloud services, face increased risk as the complexity of configurations grows.

The challenge with accidental exposure is that it typically results from oversight rather than malice. Routine maintenance, migrations, or changes to network architecture can inadvertently expose sensitive assets if security checks are skipped. Without automated tooling or procedures for regularly auditing configurations, many organizations may remain unaware of a leak until sensitive data appears on public forums or is weaponized by attackers. This underscores the need for monitoring and automated misconfiguration detection in all IT environments.

2. Insider Threats and Intentional Leaks

Insider threats involve individuals within an organization such as employees, contractors, or business partners abusing their legitimate access to sensitive data for malicious purposes. Intentional leaks can happen when a disgruntled employee exfiltrates intellectual property to a competitor or when personal data is sold to third parties. These cases are particularly challenging to detect since insiders often know how to bypass monitoring tools and evade security policies.

Organizations may also face intentional leaks prompted by financial motives, coercion, or ideological reasons. In some cases, insiders may exploit privileged access, downloading and distributing large data sets before security teams notice unusual activity. Defensive strategies require not only technical measures like least-privilege access and real-time activity monitoring but also fostering a culture of accountability and regular audits to identify behavioral red flags before they escalate into a damaging leak.

3. Third-Party and Supply Chain Risks

Partnering with vendors, consultants, and subcontractors exposes organizations to third-party and supply chain risks. Sensitive data often flows between internal systems and external partners for business operations, application development, or support. If third-party organizations have inadequate security practices, even a single supplier’s vulnerability can lead to wider data leakage. High-profile incidents, like large retail data leaks, have traced the root cause to breaches in partner networks with weaker protections.

These risks are compounded by complex IT ecosystems with multiple layers of subcontracting and cloud-based integrations. Lack of visibility into external security controls can leave organizations unaware of how their data is managed once it leaves their direct oversight. As the attack surface widens, due diligence such as third-party security assessments, contractual security clauses, and strict data handling policies is critical for mitigating supply chain-driven data leakage.

4. Software Vulnerabilities and Zero-Days

Exploiting software vulnerabilities is a common route for attackers to instigate data leakage. Zero-day vulnerabilities, which are flaws not yet discovered or patched by the vendor, present an opportunity for threat actors to silently extract data from compromised applications or operating systems. When these security holes are exploited, sensitive information can be exfiltrated without triggering standard threat detection mechanisms, allowing uninterrupted leakage for extended periods.

Repeated exposure to vulnerabilities occurs in both custom and off-the-shelf software. Threat actors target systems running outdated or unpatched applications, and the proliferation of open-source components has expanded the potential for vulnerability-driven leaks. Regular vulnerability scanning, aggressive patch management, and application security testing are essential to reducing the window of opportunity for attackers leveraging software weaknesses to expose confidential data.

5. Social Engineering and Phishing

Social engineering remains a leading cause of data leakage, primarily through tactics such as phishing. Attackers impersonate trusted parties or craft persuasive messages to trick employees into revealing credentials, sensitive files, or direct access to data repositories. Phishing emails may include attachments with malware, malicious links, or requests for confidential documents, taking advantage of human error rather than technical vulnerability. Even with technical controls, an organization’s security is only as strong as its employee awareness.

Social engineering bypasses perimeter defenses by directly targeting individuals, relying on psychological manipulation. The sophistication of modern attacks like spear-phishing customized to specific individuals or departments makes traditional signature-based detection ineffective. Consistent employee training and simulated phishing exercises are crucial in building resilience to these persistent attacks and minimizing successful data leakage via human vectors.

Common Causes of Data Leakage 

Data leakage can stem from a variety of operational, technical, and human factors. Understanding the most common causes is essential for implementing targeted security measures and reducing the likelihood of accidental or unauthorized data exposure.

• Human error: Mistakes such as sending emails to the wrong recipients or using insecure tools can unintentionally expose sensitive data.
• Weak access controls: Overly broad or misconfigured permissions allow unauthorized access to confidential information.
• Shadow IT: The use of unapproved applications and services bypasses official security protocols, increasing the risk of uncontrolled data exposure.
• Unsecured endpoints: Lost or stolen devices, or connections over public networks, can leak data if proper encryption and security policies are not enforced.
• Third-party integrations: Poorly secured partners or services with access to organizational data can become weak links in the security chain.

8 Types of Information That Might Be Exposed in a Data Leak 

Data leaks can expose a wide range of sensitive information, depending on the systems and processes involved. The impact varies based on the nature of the data and the context in which it’s used, but the following categories are commonly affected:

1. Personally identifiable information (PII): Names, addresses, phone numbers, email addresses, dates of birth, Social Security numbers, and other personal identifiers that can be used for identity theft or social engineering.
2. Financial data: Bank account details, credit card numbers, transaction records, and tax information that can be exploited for fraud or theft.
3. Health records: Medical histories, treatment plans, insurance details, and prescription data protected under laws like HIPAA. Leaks can result in privacy violations and regulatory penalties.
4. Authentication credentials: Usernames, passwords, API keys, tokens, and other access credentials that can be used to escalate attacks or access additional systems.
5. Intellectual property (IP): Proprietary algorithms, source code, product designs, research data, or trade secrets that provide competitive advantage and are often targeted in corporate espionage.
6. Business communications: Emails, internal memos, contracts, and strategic documents that can reveal organizational operations, negotiations, or legal matters.
7. Operational and configuration data: Network diagrams, server configurations, environment variables, and deployment credentials that can help attackers navigate and compromise infrastructure.
8. Customer and client data: CRM records, order histories, service usage logs, and contact details that can damage customer trust if disclosed.

The Impact of BYOD on Data Leakage Risks

Bring-your-own-device (BYOD) policies increase flexibility and reduce hardware costs, but they also expand the attack surface for data leakage – if the right security solution is not in place. Personal devices often lack the security controls enforced on corporate-managed endpoints, such as full-disk encryption, centralized patching, and endpoint detection tools. 

When employees use personal laptops, tablets, or smartphones to access corporate resources, sensitive data can be stored in unsecured locations, synchronized with personal cloud services, or shared across applications outside IT oversight. Lost or stolen devices further heighten the risk, especially if they are not protected with strong authentication and remote wipe capabilities.

Another concern is the coexistence of personal and corporate data on the same device. Without secure enclave technology or mobile device management (MDM), it becomes difficult to separate work-related files from personal applications that may lack proper security. Malicious apps, untrusted Wi-Fi connections, and insufficiently secured backups can all lead to inadvertent data exposure. To mitigate these risks, organizations need to enforce strong BYOD policies that mandate encryption, access control, and endpoint monitoring, while also considering secure workspaces that isolate business data from personal use.

The Rising Threat of Data Leakage in AI and Machine Learning 

In artificial intelligence, data leakage refers to situations where information that should not be available at the time of prediction is inadvertently used during model training. This undermines the model’s ability to generalize to new data, resulting in inflated performance during testing and poor results in production. The issue is particularly severe because it often goes unnoticed until the model fails in real-world applications.

One of the most common forms is target leakage, where training data includes features that are proxies for the target variable. For example, using a “payment status” column to predict loan default introduces future information that would not be available when making real-time predictions. This leads to models that appear accurate during validation but perform poorly in practice.

Train-test contamination is another frequent problem. This happens when test data inadvertently influences the training process, often due to improper splitting of datasets. A typical case is seen in time-series data where future observations leak into the training set, violating the temporal ordering required for accurate evaluation.

Preprocessing leakage occurs when operations like normalization or imputation are applied to the full dataset before splitting it into training and test sets. This causes statistical information from the test data such as means or standard deviations to influence the training process, giving the model unfair insight into future data.

Feature leakage involves engineered features that rely on future or otherwise unavailable information at prediction time. A common example is creating a feature based on average customer spending over the past year using data from after the prediction point, effectively leaking future behavior into the training process.

Platforms like AWS SageMaker are also susceptible to these risks if data pipelines are not carefully managed. Improper notebook practices or misconfigured data flows can easily lead to unintentional leakage, particularly when working with large-scale or automated workflows.

Addressing ML data leakage requires strict controls over dataset splitting, careful feature engineering, and disciplined preprocessing. Failing to do so can give stakeholders a misleading sense of model accuracy and result in significant operational and financial consequences when deployed in real-world systems.

Key Types of Data Leakage Detection and Prevention Tools 

1. Network DLP

Network Data Loss Prevention (DLP) solutions monitor traffic moving across an organization’s network, detecting unauthorized transmissions of sensitive data. These tools analyze patterns in outbound network traffic and apply policy-based controls to block, quarantine, or log suspicious activity. By inspecting data as it enters, leaves, or moves within a network, network DLP helps prevent accidental or intentional leaks through web uploads, file sharing services, email, or other online channels.

Deployment of network DLP typically covers perimeter points such as firewalls, internet gateways, and proxy servers, providing visibility into data flows. With machine learning and analytics, modern network DLP solutions can even identify attempts to obfuscate data or bypass conventional filters. This capability allows organizations to enforce compliance with regulatory requirements and quickly respond to risky data movements in real time.

2. Endpoint DLP

Endpoint Data Loss Prevention focuses on securing sensitive data at the device level, whether on desktops, laptops, or mobile devices. Endpoint DLP agents monitor files being copied to removable storage, printed, or otherwise moved off the device. These tools can block or encrypt transfers to USB drives, detect suspicious screenshot attempts, and enforce policies restricting what users can do with sensitive data on each endpoint.

Given the proliferation of remote work and bring-your-own-device (BYOD) environments, endpoint security has become especially important. Malicious insiders, malware, or simple human oversight can result in unauthorized data transmissions from endpoints. Endpoint DLP solutions support policy enforcement even when devices operate offline or outside the corporate network, adding a crucial layer of protection for highly mobile or distributed organizations.

3. Cloud DLP

As organizations migrate their operations and data storage to cloud platforms, Cloud DLP tools are essential for reducing leakage risks specific to cloud environments. These solutions scan cloud storage buckets, collaboration apps, and SaaS platforms for sensitive information that is accidentally exposed or improperly shared. Cloud DLP policies help detect files that contain PII, credentials, or regulated data, and can automatically remediate risks by restricting access or encrypting exposed content.

Integration with cloud-native tools and APIs allows cloud DLP to operate seamlessly across multi-cloud and hybrid environments. Continuous visibility into cloud data movement helps organizations enforce compliance, prevent inadvertent sharing, and meet audit requirements in dynamic and rapidly growing cloud architectures. By automating detection and response, cloud DLP significantly reduces the window between accidental exposure and containment.

4. Remote Workforce Protection

The rise of remote work and bring-your-own-device (BYOD) practices has made traditional data protection methods less effective. Remote employees and contractors often access sensitive corporate resources from unmanaged or personal devices, increasing the potential for data leakage. To address these challenges, organizations are adopting solutions that isolate work activity from personal use while enforcing strict data loss prevention (DLP) policies, without the complexity of traditional virtual desktop infrastructure (VDI).

Modern approaches rely on creating secure enclaves directly on the user’s device. These enclaves restrict access to sensitive data and applications, allowing only authorized actions while preventing data exfiltration. This method enables organizations to maintain full control over corporate data without needing to manage the entire device. Sensitive data remains encrypted and confined to the secure workspace, and corporate policies are enforced in real time.

Effective remote workforce protection also includes centralized administration, allowing IT teams to onboard or offboard users quickly and gain visibility into access patterns, device compliance, and potential policy violations. By focusing on securing the work context instead of the full device, organizations can reduce costs, preserve user privacy, and improve the remote work experience while maintaining a high level of security.

5. Email Security Tools

Email remains a major vector for data leakage, making robust email security tools critical components of modern DLP strategies. These solutions analyze email attachments, message content, and recipient addresses to prevent transmission of confidential information outside the organization. Features such as content filtering, outbound encryption, and policy-based blocking help intercept misdirected or inappropriate sharing before it reaches external parties.

Advanced email security tools also incorporate phishing detection, authentication of senders, and anomaly monitoring to identify spear-phishing or business email compromise attempts. They provide granular controls for compliance with data protection laws and support detailed auditing for incident response. By integrating with other DLP and security solutions, email security tools ensure comprehensive coverage of one of the most exploited digital communication channels.

Related content: Read our guide to DLP policy

Data Leakage Prevention Best Practices 

1. Data Classification and Governance

Implementing data classification is foundational for effective data leakage prevention. Classifying data according to its sensitivity such as public, internal, confidential, or highly restricted allows organizations to apply proportional protections and monitoring. This process also clarifies ownership and responsibility over data assets, defining who can create, modify, or destroy records at each classification level. Clear governance structures facilitate consistent enforcement of access and usage policies.

Additionally, formal data governance frameworks help organizations comply with regulatory mandates and audit requirements. Governance frameworks should include processes for monitoring data lifecycle events—creation, transfer, storage, archival, and deletion—to minimize exposure. Regular reviews and updates to classification schemes ensure they keep pace with evolving business operations, emerging threats, and new regulatory obligations.

2. Encryption and Anonymization

Encryption transforms readable data into an unreadable format using cryptographic algorithms, making it inaccessible to unauthorized individuals. End-to-end encryption, in transit and at rest, guards against interception or compromise during storage, transmission, and processing. Effective key management practices such as role-based access and hardware security modules are essential to prevent unauthorized decryption and limit exposure in the event of a breach.

Anonymization techniques—removing or obfuscating identifying details from datasets—further reduce risks associated with data handling, sharing, and analysis. For example, anonymizing customer data before using it for analytics or machine learning prevents sensitive information from leaking, even in case of dataset exposure. Together, encryption and anonymization provide layered defenses that render leaked data significantly less exploitable should prevention mechanisms fail.

3. Secure Data Preprocessing and Sanitization

Proper data preprocessing and sanitization are critical for preventing unintentional exposure of sensitive information during data engineering activities. Secure preprocessing includes removing or masking direct identifiers, eliminating unnecessary data fields, and converting raw data into safe formats before storage or analysis. This practice is particularly important when sharing datasets with third parties or feeding them into analytics and machine learning pipelines where privacy concerns are heightened.

Sanitization processes also involve regular audits and quality checks to ensure that data does not inadvertently retain confidential elements. Automated tools and techniques such as tokenization, redaction, and differential privacy can help remove or obscure sensitive aspects of data, even when handled at scale. Enforcing standardized preprocessing and sanitization policies ensures consistency and mitigates the risk of accidental data leakage during routine business processes.

4. Strong Access Control and Authentication

Strict access control restricts data availability to authorized individuals based on role, necessity, and security clearance. Implementation of the principle of least privilege ensures that users and applications only access the data required for specific functions and nothing more. Granular access control lists, user provisioning, and regular review of permissions minimize unnecessary exposure and prevent privilege escalation.

Multi-factor authentication (MFA) complements access control by requiring multiple methods of verification before granting access to sensitive information. MFA can significantly reduce the risk of credential theft leading to data leakage. Centralized identity and access management (IAM) solutions offer comprehensive visibility and control, making it easier to enforce and audit authentication policies, particularly in hybrid and multi-cloud environments.

5. Employee Awareness and Training

Technical controls alone are insufficient without ongoing employee awareness and training programs. Employees are often the last line of defense against data leakage, making it vital that they understand company policies, security procedures, and the latest tactics employed by attackers. Regular training sessions should cover topics such as secure data handling, password hygiene, recognizing phishing attempts, and safely reporting incidents.

Simulated security exercises and awareness campaigns reinforce good habits and help identify knowledge gaps that require targeted intervention. Organizations should also encourage a culture of security where employees feel comfortable reporting suspicious activity or policy violations without fear of retaliation. Keeping security education current with the latest threats and attack techniques is essential for reducing the risk of data leakage resulting from human error or manipulation.

Preventing Data Leakage in BYOD Environments with Venn

Venn prevents data leakage in BYOD environments by keeping corporate applications and data fully isolated on personal devices.

Similar to an MDM solution but for laptops – work lives in a company-controlled Secure Enclave installed on the user’s PC or Mac, where all data is encrypted and access is managed. Work applications run locally within the Enclave – visually indicated by Venn’s Blue Border™ – protecting and isolating business activity while ensuring end-user privacy.

Key Features include:

• Seamless MFA integration: Works with Okta, Azure, and Duo for smooth, secure authentication
• Encrypted workspace: Protects all data and applications with robust encryption
• Context-aware access controls: Enforces policies based on user, device, and environment
• Comprehensive session logging: Tracks all activity with full audit visibility
• Unified Zero Trust solution: Combines endpoint protection, remote access, and Zero Trust security
• Faster, scalable alternative: Optimized performance compared with legacy VPNs and VDI

Schedule a demo of Blue Border™