Data leakage occurs when sensitive information is unintentionally exposed to unauthorized parties. For example, a misconfigured cloud storage server might allow easy access to personally identifiable information (PII) and trade secrets. The most common vectors for data leakage stem from human error such as an employee misplacing their laptop or sharing sensitive information over email and messaging platforms. Hackers can use exposed data to commit identity theft, steal credit card details or sell the data on the dark web.
A data leak differs from a data breach in that a leak is often accidental and caused by poor data security practices and systems. In contrast, a breach is typically the result of a targeted cyberattack by cybercriminals. Once a leak occurs, it exposes sensitive information, making organizations vulnerable to exploitation. A data leak can lead to a data breach, often causing financial, legal and reputational damage.
Data leaks arise from common causes. Inadequately secured cloud storage and misconfigured firewalls are frequent culprits, but other cases include:
Human error
Mismanagement of sensitive data, such as sending emails to the wrong recipients or sharing confidential information without proper authorization, can easily lead to leakage.
Social engineering and phishing
Hackers exploit the human element by tricking employees into revealing personal data, such as SSNs or login credentials, enabling further and possibly larger-scale attacks.
Insider threats
Disgruntled employees or contractors with access to sensitive information might intentionally leak data.
Technical vulnerabilities
Unpatched software, weak authentication protocols and outdated systems create opportunities for malicious actors to exploit leaks. Misconfigured APIs are a growing risk vector, especially with the rise of cloud and microservices architectures and can expose sensitive data unintentionally.
Data in transit
Sensitive data that is being transmitted via email, messaging or application programming interface (API) calls might be vulnerable to interception. Without proper data protection measures, such as encryption, this information can be exposed to unauthorized access. Encryption standards and network segmentation are useful tools for protecting data in transit.
Data at rest
Information stored in databases, servers or cloud storage can be leaked due to faulty security settings or improper permissions. For instance, open access to confidential information such as source code, SSNs or trade secrets can create a security risk. Secure access controls, least-privilege models and continuous monitoring give organizations a deeper understanding of where gaps in security can exist.
Data in use
Data processed through systems or devices can be leaked if there are endpoint vulnerabilities, such as unencrypted laptops or data stored in storage devices such as USBs. This type of exposure can also occur if employees fail to follow security policies.
The consequences of a data leak can be severe, especially when it involves PII or trade secrets. Financial losses, reputational harm and legal repercussions often ensue, as cybercriminals can exploit easily accessed data for ransomware attacks, identity theft or selling the information on the dark web. An organization experiencing a data leak involving credit card information could face substantial fines and a significant loss of consumer trust. Violations of regulations such as GDPR and HIPAA due to a data leak can also result in heavy penalties and legal consequences.
A frequently repeated real-world instance of data leakage is the inadvertent exposure of sensitive PII in unencrypted data storage environments. This data can include phone numbers, social security numbers and credit card details, which hackers can use for identity theft or fraudulent transactions. Leaked data can also be exploited in ransomware attacks, where bad actors encrypt the exposed information and demand payment for its release, often after gaining access through a faulty system or a successful phishing scam.
A Microsoft leak in 2023 (link resides outside of ibm.com) exposed 38 TB of sensitive internal data due to a misconfigured Azure Blob Store, a type of object storage. This data included confidential information such as personal data, private keys, passwords and open source AI training data.
Another prominent incident involved Capita (link resides outside of ibm.com), a group that runs services for the NHS, councils and military in the UK. An Amazon S3 bucket exposed personal and financial data affecting various UK councils and citizens. As a result, Capita experienced a financial loss of approximately USD 85 million and the company's shares fell by more than 12%.
Improperly configured clouds, particularly in services such as AWS and Azure, continue to be a major source of accidental data exposure (link resides outside of ibm.com), often affecting millions of users and revealing sensitive information due to errors in security settings.
While malware and insider threats remain a concern, most data leaks result from operational errors rather than deliberate cyberattacks. By implementing robust data protection frameworks, continuous monitoring and frequent audits, businesses can better secure their sensitive information and minimize the risk of exposure.
A proactive, multilayered security strategy is essential to mitigate risks and safeguard data protection across all stages of data handling.
Implementing data loss prevention (DLP) tools help organizations monitor data access and control the flow of sensitive information. DLP solutions allow data teams to audit their data, enforce access controls, detect unauthorized file movements, block sensitive data from being shared outside the organization and protect sensitive information from exfiltration or misuse.
Assessments and audits of third-party risk are crucial for identifying and mitigating vulnerabilities in vendors or contractors who are handling sensitive data. Third-party risk management software can help minimize the potential for data exposure through external partners.
Employing robust security practices such as data encryption, automated vulnerability scanning, cloud posture management, endpoint protection, multifactor authentication protocols and comprehensive employee security awareness training can reduce the risk of unauthorized access.
A structured ransomware strategy can minimize damage and help organizations quickly contain ransomware, preventing its spread and protecting valuable data. Also, a well-defined plan helps ensure all stakeholders know their roles, reducing downtime and mitigating financial and reputational risks. This approach helps identify vulnerabilities, prevent future attacks and safeguard critical data.
In the context of machine learning, the term "data leakage" has a distinct meaning compared to its general use in data security and loss prevention. Data leakage refers to improperly introducing information from outside the training dataset into the model during its development, which can lead to overoptimistic and misleading results. This type of data leakage occurs when machine learning algorithms are trained on data they should not have access to, resulting in a model that performs exceptionally well in development but fails in real-world applications.
Models affected by leakage often perform well during development, showing high accuracy, but fail to generalize to new, unseen data. This is especially true when deploying machine learning models in financial fraud detection, healthcare diagnostics or cybersecurity, where real-world performance is paramount. Proper cross-validation and the careful handling of sensitive data are critical in avoiding this form of leakage.
Implementing strong data governance practices and model validation techniques, such as cross-validation, to prevent leakage and certify that model generalization is necessary. Avoiding data leakage is fundamental to building reliable, secure models.