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Data Leak

What is a data leak?

The short answer:

A data leak refers to confidential or sensitive information being unintentionally exposed, either externally or internally, due to insufficient security measures.

The long answer:

A data leak is a security incident where confidential, protected or sensitive data is released to an environment where the data is not meant to exist. It can happen because of various reasons such as system vulnerabilities, improper disposal of data, operational errors, or even malicious insider threats. The data that is leaked could range from personal and financial data such as credit card details, social security numbers, to corporate financial figures or sensitive intellectual property.

This exposure can lead to serious ramifications including damage to a company's reputation, financial loss, and legal consequences. Companies are usually highly invested in preventing data leaks in order to guard their business and customer data. Regular security audits, a reliable data security framework, strong user access control and a proactive cyber security culture are some of the ways that companies can work to prevent data leaks.

Data Leaks in Public Clouds

Cloud environments are often particularly vulnerable to data leaks for two crucial reasons: data volumes and complexity. The cloud enables businesses to store massive volumes of data, often far beyond what could be managed on local servers. Moreover, cloud environments usually consist of multiple services deployed across different regions and many specialized data stores. 

This complexity may result in unclear or inadequate security configurations, making the system more susceptible to inadvertent leaks. In some cases, data may be stored or transferred on the cloud without the appropriate security measures, such as server-side encryption or access controls. These misconfigurations are a common cause of data leaks in the cloud – and has even affected cybersecurity companies, as was the case in the 2021 Cognyte data leak incident.

Even as cloud service providers implement various security measures to protect their platforms, ongoing management and configuration of these environments predominantly falls on the client’s shoulders. Organizations need to invest in effective cloud security controls, methodologies, and well-trained staff to ensure all areas of their cloud presence are secured, reducing the likelihood of data leaks.

Data Breach vs Data Leak

Is there a difference between these terms?

The terms 'data leak' and 'data breach' are frequently used interchangeably. In some contexts, ‘leak’ might be used to describe unintentional exposure of confidential or sensitive information (as was the case in the Cognyte incident above, or other instance of misconfigurations): whereas a data breach might refer to a malicious act of data exfiltration. However, these delineations are not particularly strong in common usage, so you probably shouldn’t get too caught up on them.

Whether intentional or accidental, any case of unauthorized access to data can have dire consequences including financial loss, reputation damage, and punitive penalties from regulatory bodies. 

How Dig Prevents Data Leaks

  • Dig’s Cloud Data Security Platform provides a unified cloud-based data loss prevention (DLP) solution designed for end-to-end protection across multi-cloud environments.
  • Dig uses agentless data discovery to identify sensitive data in diverse and complex environments. This enables them to locate sensitive data in managed and unmanaged databases, and several storage options.
  • After data discovery, Dig classifies the data according to an organization's data security and privacy policies, including specific compliance frameworks.
  • Dig continuously monitors the cloud account for changes in data flows, misconfigurations and new services. It also provides real-time analysis of whether the cloud account is set up according to industry and domain-specific best practices. 
  • Dig’s DDR allows security teams to respond to security breaches in real time. For instance, Dig can identify a mass download of cloud data to a local machine within minutes of its occurrence.