According tom 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.

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.

With data breaches come significant financial costs. The loss of critical information can lead to lack of customer confidence, extended downtime, missed project deadlines, and decreased efficiency, all of which contribute to tangible revenue losses. In addition to data loss, data can also be rendered inaccessible due to destructive attacks and ransomware, causing operational disruptions and reduced productivity. Moreover, the potential exposure of valuable intellectual property and sensitive customer data exposes companies to heightened risks of loss of competitive advantage, legal consequences, and regulatory fines.

Rebuilding customer trust and restoring a damaged reputation require substantial investments in marketing efforts and customer outreach. The costs associated with data recovery services, compliance measures, legal proceedings, and increased insurance premiums further compound the financial burden. Thus, data loss reverberates through various facets of a company, underscoring the urgency for robust data protection strategies to safeguard both assets and financial stability.

Data loss prevention is the practice of identifying and protecting sensitive information in an organization, regardless of where that information is stored. DLP solutions can detect, monitor, and prevent both intentional and unintentional disclosure of confidential data. As such, DLP solutions offer protection for intellectual property by detecting unauthorized access to sensitive information. These solutions can be used to identify who accessed confidential data such as trade secrets or employee records, when they did it, what was printed after accessing the documents and more helpful details about suspicious activity surrounding confidential data.

To start, enterprises should decide which information, if it were stolen, would pose the biggest threat. Classifying data by context is an efficient, scalable method. This entails attaching a category to the database, source application, or user who originally produced the data. Understanding why protocols are being made and developing measures to minimize data risk can eliminate the common, problematic challenges in data loss. Organizations can create more specialized, fine-tuned controls to lower risks as the DLP program evolves.

To learn more about data loss prevention, read our blog: What is DLP?