Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Historically, software vendors that detect various types of data in customers’ environments have relied heavily on rudimentary methods for identifying that data. One of the most popular methods for identifying the presence of any particular type of data is using regular expressions and, admittedly, Riscosity started off doing the same several years ago.

Sensitive data is zooming across dozens of platforms every day, from Slack to email to gen AI platforms and many more sources. We all need this connectivity to stay productive, but the connectivity also creates countless opportunities for data to slip through the cracks. A single misplaced email attachment can end up exposing confidential information in a matter of seconds.

Organizations today face an unprecedented challenge: their most valuable assets can disappear in a matter of milliseconds through accidental sharing, malicious theft, or simple human error. Data Loss Prevention is a strategic approach to safeguarding information before it crosses organizational boundaries, acting as both a guardian and a gatekeeper for critical business assets.

The rise of generative AI has fundamentally changed how we work, create, and collaborate. But as organizations rush to integrate AI tools into their workflows, they're inadvertently creating entirely new categories of data risk that traditional security measures weren't designed to handle.

When it comes to data security, you want it to be accurate, reliable, thorough, and fast. Ideally, faster today than it was yesterday, and faster tomorrow than it was today, to address increasing data security needs in the same amount of time or less. We’re seeing a growing prevalence of employees using unmanaged devices, handling sensitive organizational structured and unstructured data, and accessing managed and unmanaged apps with those devices and that data.

Partners in the defence supply chain, including manufacturers, distributors, and service providers, play a critical role in supporting national security initiatives. These entities frequently handle sensitive and classified information, which necessitates a robust framework of cybersecurity measures. To safeguard this sensitive data from potential breaches and cyber threats, they are required to adhere to stringent government-mandated cybersecurity protocols.

Personally identifiable information (PII) is any data that can be used on its own or in combination with other information to identify, contact, or locate an individual. Examples of PII include full name, Social Security number, passport number, driver’s license number,r and biometric data.