What Is Data Leakage?

Understanding Data Leakage

Any sensitive data that your company shares with unintended parties or accidentally exposes to unauthorized individuals is a data leak. Data leakage can come in many forms, spanning from simply sending an email with a company file to the wrong email to leaving entire company databases without proper identity verification protections.

In general, data leakage has been on the rise over the past few years. This first began with the rise of SaaS and cloud applications in the late 2000s and throughout the 2010s, as companies inadvertently expanded their attack surfaces and had more ground to protect. Leakage rose again post-COVID, when the heightened number of remote workers once again broadened the average attack surface.

Alongside a larger attack surface that security admins need to gain visibility over and protect, Check Point research recently revealed that the accelerating use of Gen-AI has led to more data leakage, with 7.5% of all AI prompts containing sensitive information.

Data Leaks vs. Data Breaches: Understanding the Differences

Although data leaks and data breaches both lead to sensitive data being exposed, they’re not the same thing. The main difference between these two terms comes down to the active vs. passive exposure of data. In a data breach, there is an active (often malicious) party that exposes data using cyberattack strategies. 

On the other hand, a data leak is more passive in that it occurs by accident. Perhaps a user sent a confidential file to the wrong address, or they misconfigured an app’s security settings. Just like data leaks, data breaches are also on the rise, with the October 2025 Check Point report revealing that there has been a 48% YoY growth in breaches.

Data Leakage in Machine Learning

Although machine learning documentation also commonly refers to data leakage, it doesn’t describe the same process or outcome. In machine learning, data leakage is when a model accidentally has access to data that it shouldn’t have, impacting its ability to make predictions. A source model might unintentionally have access to a future dataset with results, meaning that if the base model is asked to predict those results based on earlier data, it could do so with extreme precision. 

But when the model is deployed, it won’t have access to the same future datasets, making it inaccurate. Data leakage in ML is mainly a training issue that leads to poor model accuracy and wasted time in the development cycle.

Types of Data that Can Be Exposed in a Data Leak

A data leak can happen to absolutely any organizational information that’s not effectively protected by access policies and data loss prevention (DLP) tools.

Here are some types of data that could be exposed in a data leak:

• Financial Data: Customer credit card numbers, unredacted bank statements, and payment histories may all be included in data leaks.
• Account Credentials: Usernames, passwords, and other sensitive information to get access to user accounts. 
• Personally Identifiable Information: PII, whether that’s customer names and addresses or even social security numbers, is a common piece of data exposed in leaks.
• Intellectual Property: Any private company’s designs, patents, or trade secrets could be exposed in a data leak.

Common Causes of Data Leaks

Data leaks are almost always an oversight: either on the part of an employee, the security team, or a wider organizational mistake. Understanding the common causes of data leaks is the first step toward locating and mitigating them in your own company. 

Weak Infrastructure

Weak infrastructure refers to any cybersecurity systems, policies, or tools that aren’t properly configured. Any cybersecurity structure aims to keep unauthorized parties out, but can only effectively do that when it is correctly built, deployed, and maintained.

Here is a list of examples of weak infrastructure that your company should check for:

• Misconfigured firewalls
• Weak access controls
• Misconfigured cloud systems
• Policies that don’t apply to third-party vendors
• Open ports
• Poor or lacking permissions structures

Third-Party Vulnerabilities

Third-party vulnerabilities occur when an app, SaaS tool, or cloud provider you partner with has weak security in their own organization. When you connect a SaaS platform to your company, for example, you’re trusting that it doesn’t become an open door for data access. All third-party companies you work with need to have extensive security controls that you can verify.

Social Engineering

Social engineering, most commonly in the form of phishing, is where malicious attackers contact employees and try to trick them into leaking their personal details. Employees may fall for a phishing scam or click on a malicious link that leads to them giving up their account details. 

Human Error

Simple human errors like sending an email to the wrong person or attaching a sensitive file instead of the correct one are all common causes of data leakage. 

Insider Threats

Disgruntled employees may decide to leak private information if their contract is suddenly terminated. It’s important to remove data access from employees when no longer needed and delete inactive accounts to prevent this from occurring.

Data in Transit

Without extensive encryption and data protection policies, data in transit might be vulnerable to hijacking or inspection. While this is also a cause of data breaches, ineffective encryption could simply be exposing your data via APIs or transfer pathways without you knowing it.

Data at Rest

Data at rest that’s located in storage facilities like data warehouses or lakes could be included in a data leak if it’s not properly protected. For example, if your security team forgets to apply permissions to the datasets, then users may be able to see your entire database instead of the small selection you want them to see.

Data in Use

Data leakage for data in use is when endpoint vulnerabilities, like a laptop without password protection or valuable files stored in a USB stick, lead to exposure. Most of the time, data in use exposure issues stem from human mistakes, like leaving a computer with sensitive company data in a public place, and it being stolen. 

Real-World Examples of Data Leakage

Here are two real-world examples that show data leakage in action:

• Microsoft and AI data leakage: Microsoft’s AI team exposed 38TB of data by accident while training their AI models. Feeding data into the AI model without the proper protections in place meant that all of that data became publicly accessible, leading to a large-scale data leak.
• TalentHook and Exposed Cloud Storage: TalentHook left an Azure storage container open, leaving a total of 26 million user CVs completely open to public access. Malicious groups have then been able to use these public records for spearphishing recruitment scams that target users based on their CVs. 

Consequences of Data Leaks

The main consequences of data leaks directly depend on how extensive the leak was. In some cases, the exposed data may not be sensitive, meaning companies only need to fix the leak. In others, data leaks can cause millions of dollars in damages and lead to major regulatory sanctions.

Some potential consequences of data leaks include the following:

• Reputational Damage: A leak that involves customer data could ruin their trust in your business, damaging your reputation in the long term.
• Financial Loss: If the exposed data was valuable to your business, its leakage could lead to business setbacks that cause financial damage.
• Regulatory Fines: Data is protected under an enormous number of local and international regulations, meaning a data breach could put you in non-compliance with data laws and result in fines for your business.

Best Practices to Prevent Data Leakage

Data leakage almost always comes down to preventable mistakes. Even small causes of leakage, like a misconfigured security profile or an oversight when creating user permissions, could be mitigated with enough foresight. 

Best practices to prevent data leakage in your organization include the following:

• • Ask for SBOMs: Software Bills of Materials (SBOMs) help improve visibility into third-party components and services, helping you to identify if there are any vulnerabilities or dependencies that you need to mitigate. 
  • Use Data Loss Prevention Tools: DLP tools actively monitor how data moves through your organization, helping you to block any data leakage.
  • Enforce Access Controls: Access controls ensure user accounts can only interact with certain files, with correct configurations preventing many cases of data leakage.
• Write a Data Leakage Prevention Policy: Creating an internal outline of how your business aims to gain visibility over its data and prevent leakage can hold security engineers accountable and ensure policy is deployed effectively across all your services.

Secure Your Network with Check Point

Check Point Data Loss Prevention is Check Point’s leading network DLP solution, integrating directly into our Next Generation Firewalls to preemptively stop any sensitive data from leaving your organization. Get real-time alerts, complete visibility into data at rest and in transit, and a fully centralized management console to protect your business from data leaks. 

Especially as the use of AI becomes a more pressing cause of data leakage, it’s more important than ever for businesses to keep their artificial intelligence solutions secure. Check Point’s GenAI Security Solutions, part of Check Point AI, helps to bring data loss prevention to the AI era. By increasing visibility into the use of GenAI tools in your organization and ensuring they meet internal security policy standards, GenAI Protect helps to decrease the potential for data leakage.

Schedule your personalized Next-Generation Firewall demo to see how Check Point secures your network.