How dark data can be the downfall of your business
Great companies are built on data. 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.
Over the past 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 spread across countless repositories and business systems. This shift has allowed companies to access vast amounts of information to accelerate their operations and market position.
However, this data-driven revolution comes with 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 ubiquitous as it is misunderstood: dark data.
Gartner defines dark data as all information assets that organizations collect, process and store during regular business activities, but generally do not use for other purposes.
Chief Product and Development Officer, Cyberhaven.
What makes dark data so treacherous?
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 lies in the background, unprotected and often forgotten, yet 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 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 on different systems: informal spreadsheets, email conversations, meeting minutes, draft reviews, self-assessments, and peer feedback. This scattered, often forgotten data paints a clear picture of the complex and potentially dangerous nature of dark data within organizations.
A single breach that exposes this information could lead to legal liabilities and fines from regulators for mishandling personal data, damaging employee trust, potential lawsuits, competitive disadvantage if strategic plans or payroll information are leaked, and reputational damage that can impact employee recruitment and retention.
The unintended consequences of AI
AI is changing the way organizations interact with dark data, bringing both opportunities and significant risks. Large language models are now capable of sifting through vast amounts of unstructured data and 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 logs. They can discover patterns, trends and correlations that human analysts may miss, potentially leading to improved decision making, improved operational efficiency and innovative product development.
However, this new ability to access data also exposes organizations to greater security and privacy risks. As AI retrieves 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, which is indexed by AI solutions, is often locked 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 breaches and possible misuse of personal information.
How to combat this growing problem
The key lies in 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 important when we know it was created by the CFO, shared with the board of directors, and often consulted before quarterly results are requested. This context immediately increases the importance and potential sensitivity of the document.
The way to gain this contextual insight is through data lineage. Data lineage tracks the complete lifecycle 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 with context about how it is accessed and shared (i.e. data lineage), organizations can quickly identify dark data and prevent it from being exfiltrated.
We’ve put together a list of the best document management software.
This article was produced as part of Ny BreakingPro’s Expert Insights channel, where we profile the best and brightest minds in today’s technology industry. The views expressed here are those of the author and are not necessarily those of Ny BreakingPro or Future plc. If you are interested in contributing, you can read more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro