Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

For security teams, the adoption of agents showed up operationally before it showed up strategically - creating new expectations and requirements. Risk is no longer tied to prompts or the model alone. It shows up in what agents do once they are connected to critical systems - coming from permissions they inherit, tools they invoke, and data they move.

Large language models (LLMs) have revolutionized artificial intelligence and are rapidly transforming the cybersecurity landscape. As these powerful models become commonly used among both attackers and defenders, developing specialized cybersecurity LLMs has become a strategic imperative. The CrowdStrike 2025 Global Threat Report highlights a concerning trend: Threat actors are increasingly enhancing social engineering and computer network operations campaigns with LLM capabilities.

The rapid adoption of AI agents for development is creating a critical security gap. We are moving from predictable logic, deterministic code paths, and human-driven workflows to non-deterministic agents that reason, plan, and act autonomously using large language models across the broader software development lifecycle. As enterprises adopt these autonomous AI agents, the core challenge isn’t just the new risks and attack vectors; it’s a loss of runtime control.

Building an in-house data masking tool often starts as a practical decision. The logic feels sound. Your team understands the data, knows the systems, and can tailor masking logic exactly to your needs. On the surface, it looks like a short engineering project that saves licensing costs and avoids external dependencies. What we’ve learned, after observing many organizations take this path, is that the hidden costs of building your own data masking solution rarely appear during the initial build.

When it comes to data masking or de-identification, one often-overlooked detail is the importance of preserving the original data structure. While it might seem harmless to normalize extra spaces or convert unique newline characters into a standard format, these subtle changes can actually have a significant impact on downstream processing. Let’s explore why this matters, with a couple of concrete examples.

As AI adoption accelerates across business functions, December’s expert roundup focuses on a question many organizations are now confronting in practice rather than theory: how should companies prepare for AI related data processing under GDPR. Unlike traditional automation, AI systems often rely on large, dynamic datasets, continuous learning, and opaque decision logic.