Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every new AI feature that ships into a platform your employees already use is a security question your stack probably can't answer yet. It sounds like hyperbole, but it's the structural reality of how AI adoption works in 2026. A recent update to WhatsApp is a useful illustration of why.

It’s getting harder than ever for businesses to keep their data safe. Between remote work, cloud services, and third-party integrations, sensitive information now moves faster and across more touchpoints than traditional security models were built to handle. Luckily, there’s a new generation of data loss prevention tools on the market. Ones that boast advanced features and leverage AI to resolve data security incidents.

Organizations today face a common challenge: sensitive data is everywhere. It lives across collaboration platforms, endpoints, databases, SaaS applications, and cloud storage systems. Employees and partners need to access and share information quickly, often across teams, organizations, and even countries. At the same time, regulatory requirements, security mandates, and privacy obligations demand stronger protection for sensitive data.

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.

Implementing cloud security policies and technologies has seen sustained growth in recent years. However, despite the widespread adoption of cloud security services, many companies have yet to fully recognize the critical importance of cloud security or still find themselves wondering: what is cloud security and why should it matter to their business? Migrating to the cloud provides organizations with the ability to move faster and more efficiently.

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.