The product is designed to deploy without new hardware or software, enabling organizations to govern AI traffic within their existing security stack rather than building a separate system.

Both approaches aim for reliable AI infrastructure and policy enforcement, prioritizing visibility and control over agent interactions rather than solely logging traffic after the fact.

Encryption of AI traffic (such as HTTPS) complicates inspection because decryption is needed to view content, and even decrypted prompts don’t guarantee safe instructions or outcomes.

Key customer components include Exposure Management to validate exploitable risks, Keystone autonomous security management that adapts controls based on AI understanding, the Autonomous Workspace Platform to reduce alert fatigue and provide clear remediation, and NexPloit which translates vulnerability data into verified attack material for faster protection.

Traditional firewalls and API gateways rely on signatures and human-session patterns, but autonomous AI agents can improvise legitimate request sequences that evade these tools, complicating assessment of alignment with business instructions.

Check Point’s AI Network Firewall adds AI-specific inspection within existing firewall infrastructure, classifies AI tool use and agent activity, and inspects traffic for sensitive data or manipulated prompts, building on Lakera tech acquired in 2025.

The partnership uses frontier AI to keep defenders ahead of evolving threats by discovering risk, validating exploitable paths, accelerating protection, investigating threats, and securing environments.

Check Point unveiled the AI Network Firewall to address a visibility blind spot, as nearly half of organizations lack visibility into machine-to-machine traffic from AI agents, including how prompts are executed and whether actions follow policy.

The rollout is phased, with some capabilities in production and others in development, and governance is emphasized to constrain model visibility and actions, with testing and verification before broader deployment.

Competitors like Nightfall AI offer standalone, application-level prompt inspection, illustrating different architectural approaches—from infrastructure-integrated to application-layer protection.

There is a distinction between AI-powered firewalls that detect traditional threats with ML and those that inspect prompts and agent interactions for AI-specific harm, with the latter needed to guard against prompt injection and misuse.

The initiative serves a dual purpose: strengthening defense with frontier AI and enabling secure adoption of OpenAI technologies within customer environments.