No matter the size of your business, there are assets and data you have on your premises that you need to keep secure from people outside of your organisation. Whether it’s hard copies of personnel data, server rooms, or safety-critical infrastructure, that information, or how to gain access to that information will likely have some value to bad actors willing to use or exploit it.

It’s official: Nightfall has been named as a leader in Data Loss Prevention (DLP), data security, and sensitive data discovery by G2’s peer-reviewed marketplace. At Nightfall, our goal is to help customers to discover, classify, and protect their sensitive data across the cloud.

Nightfall AI is excited to announce a new generation of detectors powered by generative AI (GenAI). Read on to learn more about recent advancements in our PII, PHI, secrets, and images detectors—as well as how they stack up against competitors like AWS Comprehend, Google DLP, and Microsoft Purview.

There are various ways to measure any given machine learning (ML) model’s ability to produce correct predictions, depending on the task that the system performs. Named Entity Recognition (NER) is one such task, in which a model identifies spans of sensitive data within a document. Nightfall uses NER models extensively to detect sensitive data across cloud apps like Slack, Microsoft Teams, GitHub, Jira, ChatGPT, and more.

Data is the lifeblood of organizations. It drives decision-making, fosters innovation, and underpins business operations. However, this wealth of data is scattered across multiple cloud platforms, making it an attractive target for cybercriminals, and rendering traditional approaches to data protection obsolete. This is where data-centric security comes into play. This article will explore the concept of data-centric security, why businesses need it, and the benefits it offers.

Navigating the delicate balance between an expanding number of engineers and the imperative for robust Application Security (AppSec) practices is no small feat. In this interview, we delve into the invaluable perspective of Jeevan Singh, the Director of Security Engineering at Rippling, the #1 workforce management platform. He shares insights and strategies that have allowed him in his career to successfully navigate the intricate maze of security amidst a high engineer-to-AppSec ratio.

In light of recent attacks against on-premises file server appliances affecting organizations of all sizes and industries, it has become clear that, in many cases, companies would be better suited to choosing cloud solutions for their secure data management needs. There are many great products on the market capable of providing easy access to files persisted in your local network.

As the importance of data continues to grow, and the amount of data being processed by organizations grows further, many organizations are beginning to feel the constraints and demands of compliance. Therefore this makes data governance paramount to guarantee the security, accuracy, availability, and usability of your data. Data governance encapsulates the approach to managing data during its lifecycle, from acquisition, to use, to disposal.

The manufacturing industry has undergone a huge digital transformation in recent years. While these changes have increased productivity, the industry has also experienced new pressures from cybersecurity. In 2022, the industry has been vulnerable to 250 data violation incidents in the United States alone costing nearly 23.9 million USD. More and more industries are finding their intellectual property (IP) at risk as threat actors become more sophisticated.

Protecting intellectual property (IP) and trade secrets is critical to a company’s bottom line. Keeping IP and sensitive business-critical information within the confines of the company and its employees is not always possible, especially in the manufacturing industry. Vendors must often share previews of new products with retailers and distribution partners before release.