Sensitive personal data is among the most valuable information attached to us; it’s so valuable that there are international regulations for its maintenance, storage, and management. It is data that contains essential details about us, like Social Security Numbers (SSNs), bank accounts, tax IDs, health insurance data, and all the other “unique-to-one” credentials.

With the growing dependence of businesses on digital platforms for storing and processing sensitive information, the threat of data exfiltration has become a pressing issue. The evolution of exfiltration techniques has given rise to threat actors who exploit insecure devices and engage in suspicious activities to steal valuable data. These activities often involve unauthorized physical access or social engineering attacks to obtain login credentials with malicious intent.

This is Part 7 in my ongoing project to cover 30 cybersecurity topics in 30 weekly blog posts. The full series can be found here. Far too many organizations place Data Loss Prevention (DLP) and Data Protection at the bottom of their priority list due to the perceived difficulty in its deployment. When there are in fact some easy approaches to getting started with protecting your data.

Regardless of the industry, the risks associated with data loss are significant. Financial services, healthcare, government, technology, and many other fields – all rely heavily on sensitive data. The Identity Theft Resource Center (ITRC) reports that the number of data compromises in the United States hit a record high of 1,802 in 2022. This emphasizes the alarming increase in data security incidents.

We’re thrilled to announce that Nightfall was selected as the “Data Security Solution of the Year” in the 2024 Data Breakthrough Awards. With enterprises scrambling to stay on the cutting edge of innovation, it’s all too easy to lose sight of data stewardship. In addition to SaaS apps, email, and endpoints, now enterprises must also safeguard their generative AI (GenAI) applications, including both custom and third-party GenAI tools.

As an organization’s data volume grows, so does the need to manage its access and governance consistently and securely. The proliferation of vast and siloed data spread across diverse applications, multi-cloud and hybrid environments, and formats over the last few decades has made it difficult to control data access and governance. In order for structured data to be effectively utilized, it needs to be combined and enhanced in a way that supports both operational and analytical tasks.

Backup and recovery solutions, anti-malware tools, data encryption tools, and network security tools—how much protection is really enough to prevent endpoint data loss? In this article, we’ll go over everything you need to know about endpoint data loss prevention, including the types of DLP, specific activities you can monitor, how endpoint DLP software can help, and more.

What is there to know about a person? Certainly, their name, but how about their affiliations, philosophical beliefs, or sexual orientation? The nuanced information about a person—including those elements listed above and more—falls into a data category called “personal information” or “personally identifying information” (PII).