Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every DLP evaluation starts with the same frustration: The tools that dominated the market a decade ago were built for a threat landscape that no longer exists. Sensitive data now moves across SaaS platforms, AI tools, encrypted messaging apps, and personal cloud accounts, often in ways no file-level policy can follow. If you are evaluating DLP for the first time or replacing a tool that has underdelivered, this guide gives you the framework to ask the right questions and recognize the right answers.

Storing data in a specific country doesn’t automatically mean that that country’s laws are the only ones that apply. This disconnect catches a lot of organizations off guard, and it’s exactly where the confusion between data sovereignty vs. data residency begins. One is about where your data physically lives. The other is about which laws govern it, regardless of location.

After decades of innovation in personal technology, ranging from watches that track personal fitness, mini super-computers that we call phones, and a whole host of other gadgets and self-help technologies, our companies still rely on one technology that started over 45 years ago – the laptop. Fun fact: the first one, called the Osborne 1, weighed 24 pounds! The modern laptop has a better screen, longer battery life, and weighs significantly less, but at its core is still a hard drive.

Every security vendor now claims artificial intelligence (AI) capabilities. Foundation models are becoming increasingly interchangeable, and the gap between what vendors promise and what programs actually deliver is widening. The question worth asking is not which vendor has the best model. It is: what is the model running on? The answer to that question determines whether a data security program hardens over time or requires constant manual maintenance.

Regulatory compliance is one of the most operationally expensive obligations security and legal teams carry. GDPR, HIPAA, CCPA, PCI DSS, and CMMC all require organizations to demonstrate, on demand, that they know where regulated data lives, who can access it, and how it is protected. Most enterprises struggle to meet that standard because they are trying to answer a continuous question with a periodic process.

Somewhere in your environment right now, an AI agent is reading files, querying a database, and passing output through a channel your DLP has never seen. It's running under a legitimate user credential, inside a sanctioned tool, and it will not trigger a single alert. When it's done, there will be no record of what it accessed or where that data went. This is not an edge case. It is the default state of most enterprise environments in 2026.

Most organizations have a reasonable handle on their sanctioned SaaS apps. Model Context Protocol - hit 10,000 public servers within a year of launch, with 97 million monthly SDK downloads. None of those numbers capture the servers your developers configured locally. Those don't appear in any registry. They were added at the IDE level, one developer at a time, with no approval step and nothing that touches a central system. That's the inventory problem. It comes before any question of enforcement.

AI agents create data exfiltration risk by combining three capabilities that are dangerous together: access to private data, exposure to untrusted content, and the ability to communicate externally. When all three exist in one agent, an attacker can hide instructions inside an email, document, or webpage the agent processes and trick it into sending sensitive data out. No software vulnerability is required. The attacker doesn't need to break in. They just need to talk to your agent.