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How deep can data be dangerous for your company?

Great businesses are built on data. It is the invisible force that powers 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 every aspect of an organization.

Over the past few decades, the workplace has undergone digital transformation, with knowledge work now existing primarily in bits and bytes rather than on paper. Product designs, strategy documents, and financial analysis all reside within digital files spread across multiple repositories and enterprise systems. This change has enabled companies to access large amounts of information to accelerate their operations and market positioning.

However, with this data-driven revolution also comes a hidden challenge that many organizations are just beginning to understand. As we look deeper into the corporate data Organizations are highlighting a phenomenon that is as widespread as it is misunderstood: dark data.

Gartner defines dark data as any information assets that organizations collect, process, and store during routine business activities but do not typically use for other purposes.

Nishant Doshi

Chief Product & 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, 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 storehouse of untapped potential and hidden risks.

Let’s consider information from annual performance reviews as an example. While official data is stored hr software Other sensitive information is stored in a variety of forms and in a variety of 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 within organizations.

A single breach exposing this information can lead to legal liabilities and regulatory fines for misuse of personal data, damaged employee confidence, potential lawsuits, competitive harm if strategic plans or salary information is leaked, and reputational damage. Recruitment and retention.

Unintended consequences of AI

AI is changing how organizations handle dark data, posing both opportunities and significant risks. large language models They are now able to sift through vast stores of unstructured data, turning previously inaccessible information into valuable insights.

These systems can analyze everything from email communications and meeting transcripts social media Posts and customer service logs. They can uncover patterns, trends, and correlations that human analysts might overlook, potentially leading to improved decision making, increased operational efficiency, and innovative product development.

However, this new ability to access data is also increasing risks for organizations security and privacy risk. As AI unearths 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 being indexed by AI solutions is often behind permissible internal access controls. AI solutions make this data widely available. As these systems become more efficient at linking together disparate pieces of information, they may reveal insights that were never intended to be discovered or shared. This may lead to a breach of privacy and possible misuse of personal information.

How to deal with this growing problem

The key lies in understanding the context of your data: where it comes from, who has interacted with it and how it has been used.

For example, a seemingly innocuous spreadsheet If we know it was created by the CFO, shared with the board of directors, and accessed frequently before the quarterly earnings call, it becomes even more important. This immediately increases the importance and potential sensitivity of the reference document.

The way to gain this contextual understanding is through data genealogy. Data lineage tracks the entire life cycle 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 strong data lineage practices, organizations can understand where their most sensitive data is stored and how it is being accessed and shared: AI with the context of how it is being accessed and shared (i.e. data lineage) By adding content based monitoring, organizations can quickly identify dark data and prevent it from being taken out.

We’ve 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 the technology industry today. The views expressed here are those of the author and not necessarily those of TechRadarPro or Future plc. If you’re interested in contributing, 

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