Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI usage is invisible to most security tools. Network monitoring sees HTTPS traffic. Endpoint detection sees browser activity. CASB platforms see cloud application access. None of them sees what employees type into AI prompts or upload to AI services through web forms. This invisibility creates a problem. Organizations can’t prove they didn’t expose customer data through AI because they can’t see the data that employees shared.

In 2026, the gap between AI adoption and AI oversight has become a primary boardroom concern. While generative AI has supercharged productivity, it has also introduced Shadow AI: the unmanaged, invisible use of unauthorized AI apps and autonomous agents that operate outside the view of traditional IT security. In this guide, you’ll learn why Shadow AI is exponentially harder to detect than Shadow IT and, more importantly, how to build a modern detection framework. We’ll explore.

Compliance teams have control over approved corporate systems like enterprise software, managed databases, and internal applications. But they don’t have the same over what employees paste into ChatGPT, upload to Claude, or share with Gemini and other unauthorized AI tools. As such, when auditors review AI usage controls, most organizations discover they can’t prove that employees aren’t exposing regulated data through external AI services.

AI governance is not a policy problem. It’s a visibility problem. Most enterprises are approaching it from the outside in: writing acceptable use policies, issuing guidelines, and hoping employees comply. That approach fails because it operates on assumption rather than evidence. You cannot enforce what you cannot see and most organizations have no reliable way to see what AI tools are actually running inside their environment.

As generative AI tools like ChatGPT, Claude, and Gemini become essential to the modern workplace, they bring a new, invisible threat: the risk of sensitive data leaking through every prompt and interaction. Traditional DLP tools are no longer enough to protect proprietary code, PII, and trade secrets from being absorbed into public AI models. This guide explores the mechanics of generative AI DLP (Data Loss Prevention) and how it creates a safety net between your team and the AI apps they use.

This release delivers heavy-hitting updates to the Mac Agent, extends Windows monitoring into native desktop applications like WhatsApp, and provides administrators with more granular tools to manage data and triage security alerts. Here is a summary of the new features and improvements available in this release.

While many organizations focus on external threat actors, insider threats are a significant risk that can devastate a business from within. Because these individuals have legitimate access to a company’s systems, their actions — whether motivated by financial gain or caused by human error — often bypass security controls. And the problem is only getting worse. According to the Ponemon Institute, insider attacks increased by 47% from 2023-25.

Proofpoint DLP and Trellix DLP are two notable data loss prevention solutions. In this blog, we’ll analyze both platforms in depth and see how they compare. We’ll also introduce Teramind as a compelling alternative that combines the best aspects of Proofpoint and Trellix, while offering additional tools that could increase your workforce’s safety and productivity.

Generative AI security is the practice of protecting generative artificial intelligence models, applications, and their underlying training data from cyber attacks, data leakage, and unauthorized access. It focuses on securing both sides of the system—i.e., the AI itself (models, pipelines, APIs) and the sensitive data flowing into and out of it during real-world use.

Here’s the reality most security teams are already living: over 80% of employees are using unapproved AI tools at work, and nearly half are actively hiding it from IT. The question facing every organization is no longer whether to adopt artificial intelligence — it’s how to secure the sensitive data flowing into it every single day. This is the governance gap.