Closing a critical gap: Experts call this acquisition significant and not merely adding just another tool to the stack. “This acquisition closes a real gap by adding AI-native runtime guardrails and continuous red teaming into Check Point’s stack,” said Amit Jaju, senior managing director at Ankura Consulting. “Customers can now secure LLMs and agents alongside their existing network, cloud, and endpoint controls.The immediate benefit is reduced integration friction and unified policy/telemetry for AI use cases already spreading across enterprises especially where agents have tool access and handle sensitive data. Enterprises should immediately treat AI applications and agents as tier-one assets deploy runtime guardrails, enforce continuous red teaming, and integrate AI telemetry with existing policies.”Dedicated AI security is still emerging, but the demand curve is going to be huge with more enterprises adopting large language models, agents, RAG (retrieval-augmented generation) systems, etc.”Demand is there, especially from sectors with regulated data (finance, healthcare), tech firms building AI-powered products and companies at scale that cannot afford unknown unknowns. The strongest pull is coming from early adopters, like cloud providers, AI SaaS, large enterprises with mature security programs and organizations that are building AI in production (not just pilot),” said Devroop Dhar, co-founder and MD at Primus Partners.Strong demand also comes from manufacturing and logistics, where agentic AI is used for automation, and government and critical infrastructure, where trust and compliance are paramount, noted Check Point.
Vendors race to secure AI: Security companies are rapidly reshaping their portfolios to address the risks introduced by artificial intelligence. Jaju acknowledged two clear themes are emerging platformization and depth in LLM-specific risk. “Buyers want AI security integrated into existing suites for unified visibility and response, while vendors are doubling down on controls tailored to LLMs and agent workflows covering prompt injection, jailbreaks, unsafe tool use, data exfiltration, and supply chain risks.”Vendors are moving beyond traditional network and endpoint protection to secure AI models, agents, and applications, for which, instead of building solutions from the scratch, many companies are opting for acquiring companies that can help them plug these gaps.”SentinelOne acquired Prompt Security, Cato Networks picked up Aim and other firms are also investing heavily in startups that protect AI models, APIs and training data. The generative AI cybersecurity market is projected to grow fast, given how quickly adoption is rising and how new attack vectors will keep surfacing,” added Dhar.
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