Big businesses are built on data. It’s the invisible force that fuels innovation, shapes decision-making, and gives businesses a competitive advantage. From understanding customer needs to optimizing operations, data is the key to gaining insights into every facet of an organization.
Over the past few decades, the workplace has undergone a digital transformation, with knowledge work now existing primarily in bits and bytes rather than on paper. Product designs, strategy documents, and financial analyzes all reside in digital files across numerous repositories and enterprise systems. This shift has allowed businesses to access vast volumes of information to accelerate their operations and market position.
However, this data-driven revolution poses a hidden challenge that many organizations are only beginning to understand. As we look deeper into enterprise data, organizations are discovering a phenomenon that is as widespread as it is misunderstood: dark data.
Gartner defines dark data as all information assets that organizations collect, process, and store in the course of 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 breaches and compliance violations. 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 scale of this problem is alarming: according to Gartner, up to 80% of enterprise data is “dark,” representing a vast reservoir of untapped potential and hidden risks.
Let’s take 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, exam drafts, auto- peer reviews and 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 personal data, loss of 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 manage dark data, bringing both significant opportunities and risks. Large language models are now capable of sifting through vast amounts of unstructured data, transforming previously inaccessible information into valuable insights.
These systems can analyze everything from email communications and meeting transcripts to social media posts and customer service logs. They can discover patterns, trends and correlations that human analysts might miss, potentially leading to better decision-making, increased operational efficiency and the development of innovative products.
However, this new ability to access data also exposes organizations to increased security and privacy risks. As AI discovers sensitive information in forgotten corners of the digital ecosystem, it creates new vectors for data breaches and compliance violations. To make matters worse, this data indexed by AI solutions is often behind permissive internal access controls. AI solutions make this data widely available. As these systems become better at bringing disparate information together, they can reveal information that was never intended to be discovered or shared. This could lead to privacy violations 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 it was created by the CFO, shared with the board, and frequently accessed before quarterly earnings calls. This context immediately elevates the importance and potential sensitivity of the document.
The way to gain this contextual understanding is through data lineage. Data lineage tracks the complete lifecycle of data, including its origin, movement, and transformation. It provides a comprehensive view of how data flows through an organization, who interacts with it, and how it is used.
By implementing robust data tracing practices, organizations can understand where their most sensitive data is stored and how it is accessed and shared: combining AI-powered content inspection with context on how how it is accessed and shared (i.e. data tracing), organizations can quickly identify dark data and prevent its exfiltration.
We have compiled a list of the best document management software.
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