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What are the Financial Costs of Data Loss?

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According to an IBM report using data from over 553 breaches in 16 different countries, the average cost of a data breach in 2023 was USD 4.45 million. The financial costs of data loss demonstrate a need for increased cybersecurity.

What is data loss?

To begin, we must define a data breach. Any security incident in which an entity gains access to another entity’s information without authorization can be considered a data breach. When these breaches occur, there is the potential for data loss. Both internal and external entities can be responsible for causing a data breach and these breaches are not always intentional in nature. For instance, if a user accidentally sends protected data to an incorrect email address, they have committed an accidental data breach. Similarly, if a user were to access confidential client or company data they are not authorized to view, regardless of whether they have done so intentionally or not, this is considered a data breach. 

Breaches like this are a significant issue for companies in industries like energy, life sciences, pharmaceuticals, financial services, and manufacturing because they are often most at risk for intellectual property (IP) and trade secret theft. In addition to theft of IP and trade secrets, internal communications and private exchanges with partners and suppliers, decades of research, bills-of-materials, strategic planning and pricing documents, product testing information and more are at risk. 

For example, in the manufacturing industry, product lifecycle management (PLM) environments are particularly vulnerable to IP theft because PLM software concentrates a lot of data in one place. 

What are the financial effects of data loss?

Data loss prevention (DLP) is crucial for organizations aiming to safeguard sensitive information, regardless of where that data is stored. DLP solutions are designed to identify, monitor, and protect confidential data from both intentional and unintentional disclosures. These solutions play a vital role in securing intellectual property by detecting unauthorized access to sensitive information and ensuring compliance with regulatory requirements. 

Effective DLP tools enable organizations to track who accessed confidential data—such as trade secrets, financial records, or employee information—when the access occurred, and what actions were taken (e.g., printing, downloading, or sharing). By analyzing these activities, organizations can gain valuable insights into potential threats and anomalies surrounding sensitive data. 

To effectively prevent data loss, enterprises must first identify and classify sensitive data. Leveraging key technologies can then strengthen their overall data security posture: 

  • Data Classification: Identifies sensitive data, categorizes it, and assigns appropriate security levels based on sensitivity to ensure proper handling. 
  • Fine-Grained Access Control: Attribute-Based Access Control (ABAC) ensures users can only access the data they are authorized for, granting only the necessary permissions for their specific tasks. 
  • Data Segregation: Logical data segregation is the practice of logically separating data based on specific criteria, such as sensitivity, access requirements, or functional requirements. It involves implementing measures to control access, visibility, and security of data based on its classification, user roles, or other relevant factors. 
  • Data Masking: Upon user’s access, dynamic data masking can mask the data following pre-designed policies and delivers only authorized levels of data to the user. The unauthorized portion will be masked without being altered. 
  • Smart Encryption: Protects data at rest, in transit, and in use by converting it into a secure format that can only be accessed by authorized users, reducing exposure in case of unauthorized access. 
  • Digital Rights Protection: Applies security controls to sensitive files shared internally and externally, including classification, encryption, and policy enforcement, ensuring data remains protected throughout its lifecycle. 

Preventing Data Loss with NextLabs

By enforcing Zero Trust principles, NextLabs’ Data-Centric Security solutions safeguard sensitive data and prevent unauthorized access or sharing via: 

  • CloudAz – a unified policy management platform that enables centralized policy enforcement with NextLabs Dynamic Authorization Policy Engine 
  • SkyDRM – provides persistent protection of critical files and documents at rest, when they are shared and, on the move, 
  • Application Enforcer – a set of enforcers that natively integrate with enterprise applications, enhancing security and compliance without custom coding. 
  • Data Acess Enforcer (DAE) –enforces policies at the data access layer, applying data segregation and obfuscation to prevent unauthorized access to data. 

NextLabs Zero Trust Data Security is a comprehensive Data-Centric Security (DCS) solution based on zero trust architecture to enforce access rights and protect structured and unstructured data throughout its entire lifecycle: at rest, in transit, and in use; regardless of where data resides – whether it is in application, file, file repository, or database on-premises, or in the cloud.

Takeaway

The financial impact of data loss continues to climb, with average breach costs exceeding $4.45 million and downtime reaching $9,000 per minute for large organizations. Beyond immediate losses, breaches can compromise intellectual property, damage reputations, and trigger long-term operational disruptions. 

Addressing these risks requires a data-centric approach—starting with identifying and classifying sensitive data, enforcing contextual access controls, and applying layered protections such as encryption, masking, and usage controls.  

As data continues to move across users, systems, and environments, consistent enforcement of security policies becomes critical to minimizing exposure and maintaining business resilience. 

Explore how a Zero Trust approach can strengthen your data loss prevention strategy and support the intelligent enterprise in the modern world. Learn more. 

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