Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

AI adoption has accelerated faster than most organizations’ ability to manage it. Security and compliance teams are now responsible for overseeing machine learning models, large language models (LLMs), agentic AI systems, and shadow AI—often with frameworks and processes that weren’t built for any of it. The gap between deploying AI and governing it responsibly is where risk lives. AI governance tools exist to close that gap.

Organizations have spent years hardening their perimeters against external attackers. Yet some of the most damaging breaches today originate from within. Insider threats—whether from disgruntled employees, compromised accounts, or AI agents—are responsible for a growing share of data loss and costly security incidents. Traditional security tools weren’t built for this reality.

In only a few years, artificial intelligence (AI) has changed almost every aspect of life, and especially so in business. Today, employees are using generative AI tools to draft emails, code software, and analyze data at lightning speed. However, there is a hidden side to this productivity boost: unauthorized AI use. Many employees are bypassing official IT channels and using shadow AI applications to get their work done.

It’s tempting for organizations to let employees use their devices for work. It saves money, is convenient for users, and allows corporate network access from remote locations. However, “bring your own device” (BYOD) arrangements can lead to serious security risks compared to issuing company-owned devices. In this post, we’ll assess the main BYOD security risks and explain how you can prevent them.