Check Point Software Technologies has announced its agreement to acquire Lakera, an AI-native security platform specializing in Agentic AI applications.
Founded by AI experts from Google and Meta, Lakera was engineered specifically for AI-native environments. The company operates major AI R&D centers in Zurich and San Francisco.
Its flagship solutions, Lakera Red and Lakera Guard, provide pre-deployment posture assessments and real-time runtime enforcement to protect LLMs, AI agents, and multimodal workflows.
With this acquisition, Check Point sets a new standard in cybersecurity, becoming able to deliver a full end-to-end AI security stack designed to protect enterprises as they accelerate their AI journey.
By combining Lakera’s runtime protection with the AI-powered Check Point Infinity architecture, enterprises can secure the full lifecycle of AI models, agents, and data—enabling them to innovate with confidence, at scale, and without compromise.
“AI is transforming every business process, but it also introduces new attack surfaces,” said CEO of Check Point Software Technologies, Nadav Zafrir.
“We chose Lakera because it brings AI-native security, superior precision, and speed at scale. Together we are setting the benchmark for how enterprises adopt and trust AI,” added Zafrir.
With Lakera, Check Point extends these capabilities to deliver one of the industry’s first end-to-end AI security stacks. By combining Lakera’s runtime protection with the AI-powered Check Point Infinity architecture, enterprises can secure the full lifecycle of AI models, agents, and data—enabling them to innovate with confidence, at scale, and without compromise.
“Lakera is purpose-built for the AI era, with real-time runtime security and research at its core,” said co-founder and CEO at Lakera, David Haber.
“Joining Check Point allows us to accelerate and scale our mission globally. Together we will protect LLMs, generative AI, and agents with the speed, accuracy, and guardrails enterprises need to embrace AI with confidence,” he concluded.
