Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Enterprise data environments have fundamentally outpaced the security architectures designed to protect them. Sensitive data now exists across endpoints, cloud infrastructure, SaaS platforms, and AI workflows simultaneously, often replicated in fragments that carry no labels and trigger no file-based controls.

As organizations increasingly rely on Microsoft Teams for internal and external collaboration, the platform’s chat and file-sharing capabilities have become central to daily operations. However, speed and flexibility come with risk. User-managed collaboration tools can create challenges in maintaining control over data access and enforcing compliance with organizational sharing and usage policies.

Most data breaches don’t happen because systems fail. They happen because people make routine errors. Attackers know this, which is why social engineering has become the dominant attack vector, exploiting everyday actions like emailing files or responding to messages. Today, 70–90% of successful cyber attacks involve social engineering, resulting in data exposure that technical safeguards can’t intercept.

A sales rep opened Glean—an AI-powered enterprise search platform that connects to your company's SaaS apps and lets anyone query across all of them in natural language—typed "Who are my top 10 customers?" and got a clean, formatted list pulled from Salesforce, cross-referenced with HubSpot, and confirmed against data sitting in Google Drive. They copy-pasted that list into a personal Gmail draft. No alerts fired. No policies triggered. No one noticed. This isn't a hypothetical.

When Anthropic announced Claude Code Security on February 20th—a tool that scans codebases for vulnerabilities and suggests patches for human review—the reaction from markets was swift and brutal. Major cybersecurity names watched their stock prices fall by double digits within days. The implied thesis behind the selling: AI can now do what these companies do, so why pay for them? It's a compelling fear and an inaccurate conclusion at the same time. The DLP space is a clear example of why.

For 83 days, attackers moved freely through Conduent's systems and exfiltrated 8 terabytes of healthcare records, Social Security numbers, and personal data belonging to tens of millions of Americans. No alarm sounded. No transfer was blocked. The breach was discovered when systems stopped working. Not because anyone detected the data leaving.

Netwrix DSPM enhances Microsoft 365 security with unified data discovery, context-aware risk prioritization, automated remediation, and continuous compliance monitoring. While M365 provides foundational security through Purview and Entra ID, it lacks visibility into shadow data and automated risk response. Netwrix closes these gaps, helping organizations discover sensitive data, enforce least privilege, and respond faster to threats.

When it comes to protecting business-sensitive data, understanding the difference and the scope of Network DLP, Endpoint DLP, and Cloud DLP is essential. Each of these Data Loss Prevention solutions (DLP) plays a unique role in securing data across various environments, whether it is on the Network, on individual devices, or in the Cloud. Knowing how each solution works can help you determine the best approach to safeguard your organization's sensitive information.

DLP and Secude solutions work alongside each other to protect your IP data from generation to storage and in transit. Here’s how. Submarine motors. Aircraft engines. Spatial systems. Command platforms. No matter the product, CAD software underpins the modern Defense production chain and contains Defense contractors’ most confidential intellectual property (IP).