A recent analysis by IBM focusing on data breaches from March 2023 through February 2024 has unearthed significant trends that cannot be overlooked by businesses, especially the rising threat of shadow data.
Shadow data refers to the information that organizations lose track of, which can be more common than one might assume. Each time a new cloud service or Software as a Service (SaaS) application is integrated, data fragmentation increases, leading to a scenario where data becomes dispersed and challenging to manage. This fragmentation complicates data oversight, making shadow data a particularly insidious threat, as organizations may not even realize it exists.
The Risks of Shadow Data
Notably, shadow data contributed to approximately 35% of data breaches reported in 2023 and 2024. The emergence of shadow AI is another pressing issue. As generative AI tools become commonplace, employees frequently incorporate them into their workflows, often without proper authorization. This poses a risk, particularly when sensitive data is inadvertently input into applications like ChatGPT, potentially exposing confidential information to third-party entities.
The risks associated with shadow data—acting as vulnerabilities within data security frameworks—along with the use of generative AI tools, underscore the necessity of addressing these “hidden” threats. Ensuring that data remains secure from such risks is crucial for any organization.
Fortunately, businesses can take proactive measures to mitigate these risks, beginning with the following strategies.
How to Eliminate Shadow Data and Enhance System Security
Identify and Secure All Data
Understanding what data you hold is fundamental to protection. Therefore, the initial step should be to locate, categorize, and secure every file produced by your organization. While this may seem daunting, data security tools are available to streamline and automate the process. For example, data security posture management tools can systematically scan both cloud and on-premises systems, utilizing artificial intelligence and machine learning to classify files based on sensitivity. This allows for an efficient approach to data oversight.
This initial phase also includes evaluating user access permissions and suggests adjustments based on specific roles. Furthermore, these tools can identify duplicate files and assist in necessary clean-up tasks, enhancing overall security posture across the network.
Enhance Your Data Governance Framework
It’s vital to assess the necessity of all existing data. Leveraging the insights gained from data security posture management can guide the removal of redundant, outdated, or trivial (ROT) data from systems. Establishing a robust data governance framework will further minimize future opportunities for ROT data to accumulate. Reducing unnecessary data simplifies tracking, thereby decreasing the likelihood of shadow data becoming a persistent issue.
Adopt Zero-Trust Policies
Implementing zero-trust security measures is essential in today’s landscape. This approach fundamentally assumes that no user or tool has inherent access to any data. Instead, users must meet rigorous criteria to gain access. For instance, under zero-trust protocols, an employee may require additional authorization to access data from an offsite location. This significantly limits potential damage from data breaches, as unauthorized users may find it challenging to access sensitive information, especially if their login does not originate from recognized IP addresses.
By enacting zero-trust policies alongside behavioral monitoring and identity access management frameworks, organizations can bolster their defenses and minimize the risk of widespread breaches.
