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Great businesses are built on data. It is the invisible force that fuels innovation, shapes decision-making and gives companies a competitive edge. 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, with knowledge work now existing primarily in bits and bytes rather than on paper. Product designs, strategy documents, and financial analyzes are all in digital files spread across numerous repositories and enterprise systems. This change has allowed companies to access large volumes of information to speed up their operations and market position.
However, with this data-driven revolution comes a hidden challenge that many organizations are only beginning to understand. As we look deeper into the enterprise dataorganizations have discovered a phenomenon that is as pervasive as it is misunderstood: dark data.
Gartner defines dark data as any information asset 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 insidious?
Dark data often contains a company’s most sensitive intellectual property and confidential information, making it a time bomb for potential security breaches and compliance violations. Unlike actively managed data, dark data sits 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 company data is “dark”, representing a vast reservoir of untapped potential and hidden risks.
Let’s consider information from annual performance reviews as an example. While the official data is kept in HR softwareOther sensitive information is stored in different forms and in different systems: informal spreadsheets, email threads, meeting notes, 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 in organizations.
A single breach that exposes this information could lead to legal liability and regulatory fines for the mishandling of personal data, damaged employee trust, potential lawsuits, competitive disadvantage if strategic plans or salary information is leaked, and damage to the reputation that could impact. recruitment and retention.
The unintended consequences of AI
AI is changing the way organizations deal with dark data, bringing significant opportunities and risks. Great language patterns are now capable of sifting through vast troves 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 logs. They can discover patterns, trends and correlations that human analysts might miss, potentially leading to better decision making, enhanced operational efficiency and innovative product development.
However, this new ability to access data also exposes organizations to growth 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 that is indexed by AI solutions is often behind permissive internal access controls. AI solutions make this data widely available. As these systems become more adept at bringing together disparate pieces of information, they can reveal insights that were never meant to be discovered or shared. This could lead to privacy violations and the potential misuse of personal information.
How to combat this growing problem
The key lies in understanding the context of your data: where it comes from, who interacts with it, and how it has been used.
For example, a seemingly harmless spreadsheet it becomes much more critical if we know that it was created by the CFO, shared with the board of directors, and often accessed before quarterly earnings calls. This context immediately raises the document’s importance and potential sensitivity.
The way to gain this contextual knowledge is through the data line. The data pipeline traces the complete life cycle 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 robust data pipeline 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 on how it is accessed and shared (valid ie data lineage), organizations can 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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