Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

DSPM solutions continuously discover and classify sensitive data, map who can access it, and surface misconfigurations across cloud and hybrid environments. Without them, security teams cannot reliably find shadow data, assess real exposure, or demonstrate that sensitive information is protected. Choosing the right platform means matching data coverage, risk prioritization, and remediation workflows to your actual estate.

Most organizations can answer "who can log in" but not "who can access a specific sensitive file, and should they?" Data access governance (DAG) closes that gap. It governs who can reach sensitive data, whether that access is appropriate, and how teams review that access over time, connecting visibility, control, and automation so organizations can govern access continuously rather than scramble before each audit.

On March 24, 2026, thousands of development pipelines ran a routine vulnerability scan. What they actually executed was a credential stealer.

Protecting sensitive data remains a top priority for businesses as data breaches and cyberattacks continue to increase globally. One of the biggest threats to data privacy and security is data leakage, which occurs when private information leaves an organization's secure environment, either by accident or intentionally. Companies worldwide are realizing they need Data Leakage Protection (DLP) solutions now, given the growing number of high-profile data breaches.

A recently patched Google Chrome vulnerability is a signal security leaders cannot ignore. But it's only the beginning of a much larger story. In January 2026, a high-severity vulnerability was disclosed in Chrome's Gemini AI integration: CVE-2026-0628. The flaw allowed a malicious browser extension with only basic permissions to escalate privileges and gain access to a user's camera, microphone, local files, and the ability to screenshot any website, all without user consent. Google patched it quickly.

Security budgets are tightening, and tool consolidation reviews keep landing on the same three categories: data security posture management (DSPM), data loss prevention (DLP), and AI security. At the same time, vendor marketing has done little to clarify the differences among the three and the path for organizations needing to enhance data security efficiently.

‍A DLP solution is only as strong as what it can detect. Gaps in detector coverage aren't just a technical inconvenience; they're exposure windows. Every format that goes unrecognized is a policy that can't fire, a remediation that can't happen, and a breach waiting to occur. Three new detectors are now available in Nightfall: personal photos (selfies and headshots), Malaysian Driver's License numbers, and South African National ID numbers.

Quick definition: Data loss prevention (DLP), also known as data leakage prevention or data loss protection, is a set of technologies and policies that stop sensitive corporate data from leaving the organisation due to user negligence, data mishandling, or malicious intent. DLP solutions enforce data handling rules by allowing or blocking data access and transfer operations based on predefined security policies.