While the cybersecurity industry rapidly embraces generative LLMs and cloud-based AI to enhance email security, this trend is not without consequences. By outsourcing core AI threat detection to third-party hosting platforms and generative models, many vendors are unintentionally introducing new risk vectors, reducing data sovereignty, risking regulatory non-compliance, and introducing third-party dependencies that undermine zero-trust principles.

This session will explore why large language models (LLMs), while powerful, may not be the right fit for high-risk communication environments. Instead, we’ll highlight how new privacy-first approaches using smaller, specialist language models (SLMs), executed entirely on-device, can offer a more sustainable, compliant, and secure path forward.

We’ll unpack the growing tension between performance and privacy in AI-based email security and provide real-world context for why enterprises are starting to rethink cloud-first AI strategies. Attendees will leave with a deeper understanding of how AI choices impact their long-term risk profile, and why privacy-first design is rapidly becoming a core requirement for secure AI security.