Great businesses are built on data. It’s 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 insights into every facet of an organization.
Table of Contents
- What makes dark data that insidious?
- The unintended consequences of AI
- How to combat this growing problem
What makes dark data that 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 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 consider the 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 across various 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 could lead to:
- Legal Liabilities: For mishandling personal data.
- Regulatory Fines: Resulting from compliance violations.
- Damaged Employee Trust: If sensitive information is leaked.
- Potential Lawsuits: From affected individuals.
- Competitive Disadvantage: If strategic plans or salary information is revealed.
- Reputational Damage: Affecting recruitment and retention efforts.
The unintended consequences of AI
AI is changing how organizations handle dark data, bringing both opportunities and significant risks. Large language models 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 uncover patterns, trends, and correlations that human analysts might miss, potentially leading to improved decision-making, enhanced operational efficiency, and innovative product development.
However, this newfound ability to access data is also exposing organizations to increased security and privacy risks. 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 that is 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 bits of information, they may reveal insights that were never intended to be discovered or shared. This could lead to privacy infringements and potential 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’s been used.
For instance, a seemingly innocuous spreadsheet becomes far more critical if we know it was created by the CFO, shared with the board of directors, and frequently accessed before quarterly earnings calls. This context immediately elevates the document’s importance and potential sensitivity.
The way to gain this contextual understanding is through data lineage. Data lineage tracks 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’s used.
By implementing robust data lineage practices, organizations can understand where their most sensitive data is stored and how it is being accessed and shared. By combining AI-based content inspection along with context on how it’s being accessed and shared, organizations can quickly identify dark data and prevent it from being exfiltrated.
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 are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro
Leave a comment