Great companies are data-driven. It is the invisible force that drives innovation, shapes decision-making, and gives companies a competitive advantage. From understanding customer needs to optimizing operations, data is the key that unlocks insights into every facet of an organization.
In recent decades, the workplace has undergone a digital transformation, and knowledge work now exists primarily in bits and bytes rather than on paper. Product designs, strategic documents, and financial analyzes reside in digital files distributed across numerous repositories and enterprise systems. This change has allowed companies to access large volumes of information to accelerate their operations and their position in the market.
However, this data-driven revolution comes with a hidden challenge that many organizations are only beginning to understand. As we delve deeper into corporate data, organizations are discovering a phenomenon as widespread as it is misunderstood: dark data.
Gartner defines dark data as any information asset that organizations collect, process, and store during normal business activities, but generally do not use for other purposes.
Director of Product and Development, Cyberhaven.
What makes dark data so insidious?
Dark data often contains a company’s most sensitive intellectual property and confidential information, making it a ticking time bomb for potential security and compliance breaches. Unlike actively managed data, dark data lurks in the background, unprotected and often forgotten, but still accessible to those who know where to look.
The magnitude of this problem is alarming: according to Gartner, up to 80% of enterprise data is “dark,” representing a huge reservoir of hidden risks and untapped potential.
Consider information from annual performance reviews as an example. While official data is stored in HR software, other sensitive information is stored in various forms and in various systems: informal spreadsheets, email threads, meeting notes, draft reviews, self-assessments, and peer feedback. . This scattered and often forgotten data paints a clear picture of the complex and potentially dangerous nature of dark data within organizations.
A single breach exposing this information could result in legal liabilities and regulatory fines for mishandling of personal data, damage to employee trust, potential lawsuits, competitive disadvantage if strategic plans or salary information is leaked, and reputational damage that could impact recruitment and retention.
The unintended consequences of AI
AI is changing the way organizations handle dark data, creating significant opportunities and risks. Large language models are now capable of sifting through large amounts of unstructured data, turning previously inaccessible information into valuable insights.
These systems can analyze everything from email communications and meeting transcripts to social media posts and customer service records. They can uncover patterns, trends, and correlations that human analysts might miss, which could lead to better decision making, greater operational efficiency, and innovative product development.
However, this new ability to access data also exposes organizations to greater security and privacy risks. As AI uncovers sensitive information from forgotten corners of the digital ecosystem, it creates new vectors for data breaches and compliance violations. To make matters worse, this data that is being indexed by AI solutions is often located behind permissive internal access controls. AI solutions make this data widely available. As these systems become more adept at putting together disparate fragments of information, they can reveal insights that were never meant to be discovered or shared. This could lead to privacy breaches and potential misuse of personal information.
How to combat this growing problem
The key is understanding the context of your data: where it came from, who interacted with it, and how it was used.
For example, a seemingly innocuous spreadsheet becomes much more critical if we know that it was created by the CFO, shared with the board of directors, and accessed frequently before quarterly earnings calls. This context immediately elevates the importance and possible sensitivity of the document.
The way to gain this contextual understanding is through data lineage. Data lineage tracks the entire lifecycle of data, including its origin, movements, and transformations. It provides a comprehensive view of how data flows through an organization, who interacts with it, and how it is used.
By implementing strong data lineage practices, organizations can understand where their most sensitive data is stored and how it is accessed and shared: by combining AI-powered content inspection along with the context of how it is accessed and shared (i.e. data lineage), organizations can quickly identify dark data and prevent it from being mined.
We have compiled a list of the best document management software.
This article was produced as part of TechRadarPro’s Expert Insights channel, where we feature the best and brightest minds in today’s tech industry. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing, find out more here:
