Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

DLP solutions have a challenge in detecting standard document types: financial records, source code, and customer lists. Moreover, what happens when your organization needs to protect business-critical documents that don't fit pre-built categories? Or when you need more granular classification to support specific workflows? Traditional approaches force you to choose between brittle regex patterns that generate false positives.

On April 8, 2025, the National Security Division (NSD) of the US Justice Department (DOJ) implemented the Data Security Program (DSP).

In our recent analysis of AI browser exfiltration risks, we exposed how OpenAI's Atlas and Perplexity's Comet create permanent backdoors to sensitive data through persistent memory, autonomous agents, and cross-platform sync. The challenges with AI native browsers strongly resonated with CISO’s and security leaders we speak with on a daily basis. But the threat extends far beyond Atlas and Comet.

Let’s look at something many teams quietly struggle with. Detecting PII inside unstructured text. It feels like it should be simple. After all, we’ve used regular expressions for years to find emails, phone numbers, and ID formats. Yet when we deploy regex in real environments. ticket systems, chat logs, CRM notes, uploaded documents, support transcripts. something becomes clear very quickly. Regex isn’t enough.

The data security landscape is reaching an inflection point. In 2026, the question won't be whether your security stack can detect risks; it will be whether it can predict, prevent, and adapt to them in real time.

In today’s high-turnover work environment, we’re watching something unusual happen: record numbers of security cleared, experienced professionals are re-entering the job market. They’re leaving shuttered programs, reorganised agencies, downsized contractors, and sometimes entire departments caught in a budget reshuffle. Conventional wisdom says these people are an asset anywhere they land.

Customer support teams face an impossible paradox: they need to help customers quickly, but customers routinely share sensitive information that creates compliance risks and security exposure. Credit card numbers pasted into chat. Driver's licenses attached to verification tickets. Medical records uploaded to troubleshoot healthcare apps. Social security numbers submitted through web forms. Traditional DLP wasn't built for this reality.

Endpoint Data Loss Prevention (DLP) solutions are critical tools for organizations looking to safeguard sensitive information from insider threats, unintentional leaks, and external attacks. These solutions monitor, detect, and block the transfer of valuable data outside company networks, ensuring compliance with data protection regulations. In this post, we’ll explore the top eight endpoint DLP solutions that offer robust security features, ease of use, and integration capabilities.

I’ve been meeting with customers across APAC, and a clear pattern is emerging: privacy laws are tightening, timelines are shrinking, and boards are asking tougher questions. The takeaway is simple: progress isn’t optional. Here’s the headline: Netwrix is leaning into Asia-Pacific with identity‑first data security so organizations can meet the letter of the law and actually reduce risk in the real world. Our philosophy is simple: data security that starts with identity.