Nearly half of organizations have zero visibility into the machine-to-machine traffic their AI agents generate, and the firewalls and API gateways most enterprises rely on were not built to look inside a prompt. The result is a specific gap in security monitoring, one that vendors are now trying to close from inside the infrastructure companies already run.<\/p>\n
That gap matters more as agents take on tasks such as purchasing access to data and online services, handling customer interactions, and completing work without a person approving every step. A log showing that one service contacted another cannot, on its own, explain whether the agent followed the company’s instructions.<\/p>\n
One recent study found that 48.9% of organizations have no way to monitor what their autonomous agents are doing across connected systems. Legacy web application firewalls and standard API gateways were built around attack signatures, rate limits, and predictable human sessions. An agent can improvise a new sequence of otherwise legitimate requests without matching a known attack signature, so those tools have nothing to flag.<\/p>\n
An approved API call is also not automatically an approved business decision. A support agent that uses valid credentials to pull a customer file and then includes it in a reply to someone who should not receive it performs no action that resembles a conventional intrusion. Encryption adds a second obstacle, since AI traffic over HTTPS needs the right certificates and policies before a firewall can decrypt and inspect it at all.<\/p>\n
Even after decryption, a readable prompt is not the same as an understood prompt. Decryption exposes the text, but it does not show whether the instructions are safe.<\/p>\n
The term covers two distinct products. An AI-powered firewall uses machine learning to detect conventional network threats. A firewall built to protect AI inspects prompts and agent interactions for AI-specific harm, including prompt injection, a technique that can turn a document or webpage into instructions an agent follows, and that has been used in recent attacks against coding agents.<\/p>\n
Check Point’s AI Network Firewall, announced this past July, adds AI-specific inspection to existing firewall infrastructure. The product discovers and classifies employee use of generative-AI tools, AI agent activity, Model Context Protocol traffic, and traffic to and from AI applications, then applies real-time inspection to that activity. Model Context Protocol is the industry standard for connecting agents to tools and data.<\/p>\n
The product aims to identify sensitive data heading toward a public AI tool and to flag a manipulated prompt attempting to trigger unintended behavior. Check Point’s broader AI security stack incorporates technology from Lakera, the AI security startup Check Point acquired in 2025, which supplies runtime protection against prompt attacks.<\/p>\n
What stands out is the deployment model. Customers can use their existing firewall infrastructure without adding new hardware or software, bringing AI controls into their established management environment. Security teams can start governing AI traffic without first deploying and maintaining a separate system.<\/p>\n
Nightfall AI’s Firewall for AI takes a different route. The company describes its standalone offering as a client wrapper around generative-AI interactions, using APIs and software development kits to inspect content before it reaches a model. It scans for personally identifiable information, payment-card details, health information, and secrets, so sensitive material can be removed before an application forwards a prompt. Nightfall also offers prompt-injection protection and conversational guardrails separately, checks that cover conversation content and signals such as model-response refusals.<\/p>\n
Finding a payment card number and recognizing an attempt to redirect a model are different security tasks, and the two vendors address them in different places on the network.<\/p>\n
Check Point’s argument is that AI-specific protection belongs inside infrastructure a company already runs. Nightfall’s argument is that AI interactions warrant a dedicated layer inside application workflows. Neither placement, on its own, guarantees that every relevant interaction will be inspected.<\/p>\n
For companies that need to protect their AI systems, the practical questions concern coverage, intervention, and policy enforcement, not which product approach seems freshest. Both point to a wider focus on reliable AI infrastructure rather than model performance alone. An integrated control may fit established operations, while an application-level wrapper gives developers a specific point at which to filter model-bound data.<\/p>\n
A business that cannot observe its agents’ interactions cannot confidently assess whether those agents are staying within their remit. Whichever architecture gains ground, the meaningful advance will be tooling that connects an instruction to an action and applies policy before harm occurs. What matters is whether security tools can see what AI systems are actually doing, and govern it, rather than logging the traffic after the fact.<\/p>\n
48.9% of organizations have zero visibility into the machine-to-machine traffic their AI agents generate, according to a recent study cited on the gap.<\/p>\n
Legacy web application firewalls and standard API gateways were built around attack signatures, rate limits, and predictable human sessions. An agent can improvise a new sequence of legitimate requests without matching a known attack signature, and HTTPS encryption requires the right certificates and policies before any inspection can happen at all.<\/p>\n
An AI-powered firewall uses machine learning to detect conventional network threats. A firewall built to protect AI inspects prompts and agent interactions for AI-specific harm, including prompt injection, where a document or webpage is turned into instructions an agent follows.<\/p>\n