Open-source large language models (LLMs) continue to revolutionize the cybersecurity landscape, serving as a strong catalyst for increasing innovation and enabling startups and established vendors alike to accelerate time-to-market. From new generative AI applications to advanced security tools, these models are proving the foundation of the future of gen AI-based cybersecurity. Open-source models gaining traction in cybersecurity include Meta’s LLaMA 2. LLaMA 3.2, Technology Innovation Institute’s Falcon, Stability AI’s StableLM, and those hosted by Hugging Face, including BigScience’s BLOOM. All of these models are seeing growing adoption and use, thanks in large part to their greater cost-effectiveness, flexibility and transparency. Cybersecurity software providers are facing a growing set of challenges related to governance and licensing while enabling their platforms to scale in response to the fast-moving nature of open-source LLM development. Designing an architecture that can quickly adapt and capitalize on the latest features that most recent open-source LLMs are providing is challenging. Itamar Sher, CEO and co-founder of Seal Security, recently sat down with VentureBeat (virtually) to discuss the foundational yet evolving role of open-source LLMs in their operations. “Open-source LLMs enable us to scale security patching for open-source components in ways that closed models cannot,” he said. The ability to scale models quickly is critical for companies like Seal, which use open-source components to ensure the rapid deployment of patches across different environments. He added that “open-source LLMs give us access to a community that continuously improves models, offering a layer of intelligence and speed that wouldn’t be possible with proprietary systems.”

Full analysis : How open-source LLMs enable security teams to stay ahead of evolving threats.