The Risks and Impact of Data Leakage: Data Leakage Causes and Prevention

As a business, you’re likely adopting new technologies to drive growth and stay competitive. But as you integrate these powerful tools, it's important to be aware that they can sometimes introduce new security challenges that your existing systems might not be fully equipped to handle. It’s common for issues to arise during the initial setup and learning phases, and it's natural for your team to encounter a few bumps along the road, like accidentally misconfiguring a setting or unintentionally exposing data while they're getting to grips with the new systems. These early-stage learning curves can, unfortunately, create openings for cyberattacks or other threats.

A significant risk to be particularly mindful of is data leakage, which can escalate into serious security incidents or vulnerability exploitation and potentially impact your reputation and bottom line. Understanding what data leakage is and how it can occur is a crucial step in building a strong, multi-layered security strategy that not only protects your valuable assets but also empowers your team to confidently leverage new technologies.

What is data leakage?

Data leakage can also lead to larger security issues or expose vulnerabilities in a company’s systems and operations, especially if bad actors end up with the information. 

3 types of common data leakage

Data leakage can occur in any of the three data states:

• Data in transit: Data is transmitted from one location to another, like through email, application programming interfaces (APIs), or applications. 

• Data at rest: When data is stored somewhere and not being transmitted. This could be data on a server, cloud storage, or a device’s hard drive. 

• Data in use: Data being actively used, edited, or processed by applications or users, like data stored in your computer’s memory. 

Data leakage vs. data breach

While data leakage and data breaches both result in compromising or revealing sensitive information, data leakage is usually unintentional and accidental. In comparison, data breaches are often caused by targeted attacks from bad actors. 

That said, data leakage can result in a data breach if bad actors exploit the sensitive information released in a leak. A data breach from data leakage can cause significant harm to an organization, including financial losses and reputational damage. 

Data leakage in AI systems

Data leakage in AI systems happens when sensitive information is revealed during training, deployment, or usage. 

If the model is trained on sensitive data or the data is accidentally included in the training data, the model could reveal this information later. It could also impact the model’s behavior and decision-making, resulting in unreliable predictions or biased decisions. 

If a model reveals sensitive information, the data leakage can result in larger AI attacks. Additionally, if data isn’t properly encrypted or security measures aren’t in place, AI systems can leak data during model deployment, transfers, or storage. 

Types of data in data leakage

There is a wide range of data that can be exposed by data leakage, including:

• Intellectual property or trade secrets

• User credentials 

• Personally identifiable information, like Social Security numbers and financial information  

• Customer data 

• Internal communication, like emails or private chats

• Medical information or records

Generally, any information that is otherwise unavailable to the public is valuable to bad actors in data leaks, as they can often profit off it through the dark web and further exploit a business. 

4 main causes of data leakage

Most data leaks are preventable with the right training and security protocols, meaning the underlying causes are often quite simple. Common causes of data leakage include:

• Human error: Accidental exposure of data by employees or authorized users. This could be sending an email to the wrong person or losing a work device containing confidential information. 

• Settings or infrastructure misconfigurations: If AI systems, firewalls, or cloud storage settings are configured incorrectly or software isn’t updated regularly, it can lead to exposed or unprotected data. 

• Internal threats: Sometimes, an employee or someone with authorized access will maliciously expose sensitive information. This can also occur if the principles of least privilege aren’t followed or an organization has weak security policies or protocols. 

• Social engineering: Bad actors can steal sensitive information through phishing or deception. This can include other tactics like a watering hole attack or scareware. 

Examples of data leakage

While companies have begun to place more importance on cybersecurity, bad actors continually evolve their techniques, often leading to data leaks or exploitation. Depending on the complexity of a data leak, it could be caught quickly or go undetected for a longer period. 

Some of the most recent data leaks have involved large companies, including Microsoft, Apple, and Volkswagen. 

The Microsoft attack a few years ago was the result of a misconfigured Azure Blob Store, which led to sensitive internal data being exposed. This included user credentials, open source AI training data, and personal information. 

Apple’s data leak in 2022 occurred from a bug in a JavaScript API for data storage. The vulnerability enabled malicious websites to see URLs users recently visited and their Google User IDs. 

In December 2024, Volkswagen faced a data leak due to an AWS misconfiguration. Personally identifiable information, car data, and EV data were released in the leak. Volkswagen was also victim to a leak in 2021, where bad actors exploited an unsecured third-party vendor. The database was unsecured from 2019 to 2021, with 3.3 million people’s information being released. 

How to prevent data leakage

The best way to prevent data leakage is by having and maintaining strong security practices to protect data at any stage. 

• Inventory and monitor assets and data: Many data leaks occur due to unprotected or misconfigured systems, so knowing where all assets and data are, how they’re stored, and the best ways to protect them is vital.  

• Audit and assess third-party vendors: If you use third-party vendors to handle sensitive data, regularly review and audit their security to ensure they’re handling your data properly. 

• Encrypt data: Encryption can prevent bad actors from seeing sensitive or confidential information, as it requires a key to be decoded and unscrambled. 

• Employee training: Awareness and training is also vital to protecting data. As bad actors often employ phishing and social engineering attacks, training can help prevent data leakage or other attacks.

• Least privilege and access controls: Monitoring data access, using multifactor authentication, and enforcing the principle of least privilege can reduce the risk of unauthorized access. 

• Implement a strategy and policies: Having a strong security strategy and policies surrounding data leaks, breaches, and cyberattacks can help prevent the likelihood of an attack. Not only do robust strategies and policies protect data, but they also can identify security gaps and reduce the impact of an attack. 

Tools to prevent data leakage  

A key part of preventing data leakage is having the right tools. Data loss prevention (DLP) tools can safeguard your data and help prevent leakage, mitigating an organization's risk. DLP tools can also help organizations manage security, making it easier to enforce security protocols and policies. 

When looking at a DLP tool, the most important features are:

• Automation: A good tool should automatically scan environments to identify, inventory, and classify sensitive data. It should also automatically enforce policies and compliance to prevent unauthorized access and act on incident response. 

• Continuous monitoring: Continuous, real-time monitoring in all data states can immediately detect unauthorized access or suspicious activity. This also includes monitoring network traffic to prevent data leakage through email or websites. 

• Reporting: There should be detailed logs of any events, user activity, and data access, as well as any reporting required for compliance and security regulations. 

• Endpoint protection: All endpoint devices, like laptops, desktops, or mobile devices, should be monitored to protect data in any state. 

• Cloud protection: As many organizations have migrated to cloud environments, a good DLP should protect data in the cloud by integrating with cloud platforms and applications.

Real-time protection with Snyk

A strong, layered security posture is essential for protecting data and preventing leaks. Along with a DLP tool, adopting a developer-first security tool that spans the entire SDLC ensures data is protected in any state. 

Snyk, a modern, developer-first security platform, provides this protection, extending to all aspects of applications across the SDLC. By integrating directly into developer workflows, Snyk uses automated scanning to identify vulnerabilities and recommend fixes in real time, minimizing the risk of issues that could lead to data leaks. Leveraging the Snyk Vulnerability Database, Snyk can test and scan for vulnerabilities in applications, including open source code and containers to infrastructure as code (IaC).  

Snyk strengthens security through automated scanning, scoring, risk prioritization, and remediation, ensuring the secure adoption of new tools and technologies. As more teams are leveraging AI tools and coding assistants, Snyk can check and secure AI-generated code seamlessly within developer workflows. 

If you’re interested in learning more about adopting Gen AI development tools securely, read Snyk’s ebook, Taming AI: Securing Gen AI Development with Snyk.