Shedding Light on Dark Data: Mitigating IT Security Risks in Today’s Business Landscape

Reading Time: 2 minutes

In today’s fast-paced business landscape, where the volume and intricacy of data is continuously expanding, it has become apparent that several businesses are neglecting a crucial category of data that is pivotal to their data management and protection practices.

On average, over half of a company’s data is considered “dark,” meaning it is stored in repositories without any known value or purpose. This can lead to substantial expenses, with an average cost of $26 million per year for storage alone.

Furthermore, dark data poses significant risks to an enterprise’s security and compliance efforts, underscoring the importance of addressing the root causes that lead to it.

Dark data is a term used to describe data that an organization collects, processes, and stores but does not actively use or analyze. This data can include everything from customer information to system logs and metadata. Dark data is a significant challenge for IT security professionals because it can contain sensitive information that is at risk of being compromised.

The challenge with dark data lies in its storage in unstructured or semi-structured formats, rendering it arduous to search and analyze. Consequently, dark data is more susceptible to security risks, including data breaches, theft, and unauthorized access.

An instance of dark data in IT security is log files, which IT systems generate in large quantities. This log data is instrumental in tracking system events and identifying potential security threats. Unfortunately, it is often disregarded due to its complexity, requiring a substantial amount of time for review and analysis.

Similarly, unstructured data such as emails, instant messages, and documents can contain sensitive information that is not actively managed. This data can be accessed by hackers or internal bad actors who can use it to steal valuable information or launch cyber attacks.

Another challenge with dark data is the increasing use of shadow IT systems. Employees may use unauthorized software or cloud services to store or process data, creating additional sources of dark data that IT security professionals may not be aware of or able to manage.

To illustrate, machine learning algorithms can be employed to detect patterns in log data, which may indicate a potential security threat. Moreover, cutting-edge data analytics tools can be used to scrutinize unstructured data and pinpoint potential security risks.

Aside from these technical remedies, companies must also prioritize the establishment of a data security-conscious organizational culture amongst their employees. This entails regularly providing training on data security best practices, implementing robust password policies, and fostering a vigilant culture concerning data protection.

To conclude, dark data represents a significant challenge for IT security experts. Nevertheless, by adopting a proactive approach to data management and investing in appropriate tools and technologies, businesses can alleviate the risks linked with dark data and safeguard their crucial information assets from cyber threats.

Read Next: