Small businesses can gain control of AI agent traffic by identifying where autonomous tools act across their networks, reviewing inspection and encryption coverage, and testing whether their security controls can distinguish approved agents from malicious bots. A recent Salt Security survey found that 48.9% of organizations are essentially blind to non-human traffic, while 48.3% cannot effectively differentiate legitimate AI agents from malicious bots.
That gap matters as agents purchase access to data and online services, interact with customers and complete multi-step tasks without a person approving every step. An ordinary firewall log may show that one service contacted another, but it cannot by itself confirm that the agent followed the company’s instructions.
Why conventional firewalls miss AI agent activity
Legacy web application firewalls and basic API gateways were built around attack signatures, rate limits and predictable human sessions. An autonomous agent can create a new sequence of legitimate-looking requests without matching a known attack signature. Traditional tools were not designed to parse the unpredictable, logic-based actions an autonomous agent can generate.
An approved API call is not automatically an approved business decision. For example, a support agent may use valid credentials to retrieve a customer file and then include it in a response intended for someone who should not receive it. That action can resemble normal application use rather than a conventional intrusion.
Encryption adds a separate inspection problem. AI traffic traveling over HTTPS is encrypted, so a firewall needs appropriate certificates and policies in place to decrypt that traffic before it can examine the contents. Capacity and connectivity constraints can also determine which traffic gets inspected.
Decryption may make a prompt readable, but does not mean that its instructions are understood or safe.
What are the two meanings of AI firewall?
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.
The second category addresses prompt injection, which can turn a document or webpage into instructions an agent follows. The same mechanism has been used in recent attacks against coding agents.
Salt Security’s survey of more than 300 security leaders, published in its 1H 2026 State of AI and API Security Report, also found that nearly 47% of respondents reported API growth of 51% to 100% during the previous year. According to the Salt Security report, agents can create undocumented endpoints or use MCP servers outside the security team’s view, exposing sensitive data without formal oversight.
How Check Point approaches agent traffic
Check Point’s AI Network Firewall was announced in July 2026. According to the company, it adds AI-specific inspection to existing firewall infrastructure. It discovers and classifies employee use of generative-AI tools, AI agent activity, Model Context Protocol activity and traffic to and from AI applications.
Model Context Protocol, or MCP, is an industry standard for connecting agents to tools and data. Check Point says its firewall applies real-time inspection to this activity, which can identify sensitive data heading toward a public AI tool or a manipulated prompt attempting to trigger unintended behavior.
Check Point’s wider AI security stack incorporates technology from Lakera, an AI security startup the company acquired in 2025. Lakera supplies runtime protection against prompt attacks.
What sets this approach apart is the deployment model. According to Check Point, customers can use their existing firewall infrastructure without adding new hardware or software. Security teams can add AI controls to their established management environment instead of first deploying and maintaining a separate system.
How Nightfall AI approaches the same problem
Nightfall AI’s Firewall for AI takes an application-level approach. The company describes the product as a client wrapper around generative-AI interactions. It uses APIs and software development kits to inspect content before that content reaches a model.
Nightfall says the firewall scans for personally identifiable information, payment-card details, health information and secrets, allowing sensitive material to be removed before an application forwards a prompt. The company also offers prompt-injection protection and conversational guardrails separately.
Those controls examine conversation content and signals such as model-response refusals, rather than only sensitive-data categories. Identifying a payment-card number and recognizing an attempt to redirect a model are separate security tasks.
What the difference means for a small-business IT setup
An integrated firewall control may fit an established network, while an application-level wrapper filters data sent to a model at a specific point.
For small businesses, the first question is practical: can the IT team see every route used by an AI agent? A service-to-service log can establish that communication occurred. It cannot establish that the action matched an approved instruction or that sensitive information was sent to the wrong destination.
Determine whether public web content accessible to agents supports the intended workflow.
What to do now
- List the AI tools and agents in use. Record where each one operates, which applications and data it can access, and whether employees use public generative-AI tools for company information.
- Review existing firewall and API coverage. Identify where AI agent and MCP traffic travels, which traffic is encrypted, which certificates and policies are available, and what capacity or connectivity limits inspection.
- Separate sensitive-data controls from prompt-injection controls. Confirm whether the environment can remove personal information, payment-card details, health information and secrets, and whether it can detect manipulated instructions inside documents or webpages.
- Test the response to a hostile input. Use a controlled test of how coding agents and other agents respond to instructions embedded in source code or documentation, then check whether the activity is visible and policy is enforced before the action occurs.
- Require an action trail that includes intent. Recording that two services exchanged traffic alone may not show whether the business authorized the action.
How can a small business know whether its firewall sees AI agent traffic?
Start by mapping the agent’s tools, data access and network paths, then verify that the firewall can inspect encrypted traffic where required. Check whether logs connect an instruction with each action and whether controls can distinguish sensitive-data exposure from prompt injection. Salt Security reported that 48.3% of surveyed organizations could not effectively differentiate legitimate AI agents from malicious bots.
FAQ
What percentage of organizations cannot see their AI agent traffic?
A recent study cited in the source material found that 48.9% of organizations have zero visibility into the machine-to-machine traffic their AI agents generate. Salt Security’s survey of more than 300 security leaders reported the same figure and added that 48.3% could not effectively differentiate legitimate AI agents from malicious bots.
How is a firewall built to protect AI different from an AI-powered firewall?
An AI-powered firewall uses machine learning to detect conventional network threats such as malware and intrusion attempts. A firewall built to protect AI inspects prompts and agent interactions for AI-specific harm, including prompt injection, where crafted input turns a document or webpage into instructions an agent follows.
What is Model Context Protocol?
Model Context Protocol, or MCP, is an industry standard for connecting agents to external tools and data sources. Check Point says its AI Network Firewall classifies MCP activity alongside other agent activity so it can apply real-time inspection.
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