Big businesses are built on data. It is the invisible force that drives innovation, shapes decision-making and gives companies a competitive edge. From understanding customer needs to optimizing operations, data is the key that unlocks insight into all facets of an organization.
In recent decades, the workplace has undergone a digital transformation, where knowledge work now primarily exists in bits and bytes rather than on paper. Product designs, strategy documents and financial analyzes all live in digital files spread across numerous warehouses and enterprise systems. This shift has enabled companies to access vast amounts of information to accelerate their operations and market position.
But with this data-driven revolution comes a hidden challenge that many organizations are only beginning to understand. As we look deeper into enterprise data, organizations are uncovering a phenomenon that is 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.
Chief Product and Development Officer, 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 available 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 consider the information from annual performance reviews as an example. While official data is stored in HR software, other sensitive information is stored in different forms and across different systems: informal spreadsheets, email threads, meeting notes, draft reviews, self-evaluations and peer feedback. This scattered, often forgotten data paints a clear picture of the complex and potentially dangerous nature of dark data in organizations.
A single breach that exposes this information can lead to legal liabilities and regulatory fines for mishandling personal data, damaged employee trust, potential lawsuits, competitive disadvantage if strategic plans or salary information is leaked, and reputational damage that can affect recruitment and retention.
The unintended consequences of AI
AI is changing how organizations deal with dark data, bringing both opportunities and significant risks. Large language models are now able to sift through vast amounts of unstructured data and transform 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 uncover patterns, trends and correlations that human analysts might miss, potentially leading to improved decision-making, increased operational efficiency and innovative product development.
But this newfound ability to access data also exposes organizations to increased 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 breaches. To make matters worse, this data being indexed by AI solutions is often behind permissive internal access controls. The AI solutions make this data widely available. As these systems become more adept at piecing together disparate pieces of information, they can reveal insights that were never meant to be discovered or shared. This can lead to privacy violations and potential misuse of personal information.
How to combat this growing problem
The key is to understand the context of your data: where it came from, who interacted with it, and how it has been used.
For example, a seemingly innocuous spreadsheet becomes far more critical if we know it was created by the CFO, shared with the board, and often accessed before quarterly earnings calls. This context immediately elevates the document’s importance and potential sensitivity.
The way to achieve this contextual understanding is through dataline. Data lineage traces the complete life cycle of data, including its origins, 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 robust data lineage practices, organizations can understand where their most sensitive data is stored and how it is accessed and shared: By combining AI-based content inspection along with context of how it is accessed and shared (i.e. data lineage), organizations quickly identify dark data and prevent it from being exfiltrated.
We have compiled a list of the best document management software.
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