Recently, researchers tested 14 models from five frontier AI labs against one identical hardened prompt-injection attack, and every model followed the attacker’s hidden instructions at least some of the time. Even the most injection-resistant model followed hidden instructions 26.7% of the time in that experiment.
For developers, that result suggests a system prompt and a model’s built-in safeguards cannot be relied on as a security boundary. That points toward adding protection one layer below the model, where it can inspect the content an agent reads and the tool calls it makes. In this sense, prompt injection is an architectural problem to design around.
Engineering and security teams can build guardrails into an LLM application, but they’d also do well to add a firewall layer around it.
This list covers five solutions for that second layer: Palo Alto Networks Prisma AIRS API Intercept, F5 AI Guardrails, Cloudflare AI Security for Apps, Akamai Firewall for AI, and Check Point’s AI Network Firewall.
An AI-powered firewall is a conventional network firewall that uses machine learning to identify threats. An AI firewall in the sense that matters for LLM applications is different: it protects AI applications, agents and associated traffic against AI-specific threats such as prompt injection and data leakage.
All five platforms below do that second job, but they take different forms, from scan APIs called in application code to edge proxies to a network firewall, and their inspection coverage differs. The useful comparison is where each platform sees the content, who implements the response to a detection, and what happens when inspection cannot finish.
1. Palo Alto Networks Prisma AIRS API Intercept: Scan Prompts and Responses From Application Code
Prisma AIRS AI Runtime offers network and API interception. The API intercept is the application-level option: send prompts and model responses through REST or the official Python SDK, then use the returned threat verdict and recommended action.
A configured security profile governs checks for prompt injection, sensitive data leakage and unsafe content. The service is model-agnostic, so the integration can sit alongside public or private LLMs.
Palo Alto Networks’ documentation describes the API intercept as suited to environments where rerouting traffic through a firewall is impractical. A Kong Gateway plugin can also handle scanning and enforcement at the gateway. For direct integrations, plan around the documented limits: one API key per deployment profile, 2 MB per synchronous scan and 5 MB per asynchronous scan.
Decide whether your application blocks, retries or continues when a scan fails or times out. The API returns a verdict but does not block anything itself, so the application must check the verdict before forwarding the content.
Best suited to teams that want security policy enforced in application code or at their API gateway.
2. F5 AI Guardrails: Inline and Out-of-Band Content Scanning
F5 AI Guardrails checks for prompt injection, jailbreaks, data leakage and configured content-policy violations. Its prompt and scan interfaces support different integration patterns. The prompt interface scans content before forwarding it to the LLM and can also inspect the response.
Separate, out-of-band scan calls return a verdict while leaving routing and enforcement with the caller. Those calls can check user input and model output, but a scan only covers the model’s response when the request sets its scanDirection to response. The application or gateway must wait for the relevant verdict if it intends to prevent delivery.
For gateway integration, NGINX Gateway Fabric’s PayloadProcessor policy sends content to Guardrails and returns a 403 when blocked. NGINX labels the resource experimental while the Gateway API working group develops it. The endpoint can be F5-hosted or a customer-hosted Kubernetes Moderator service, although self-hosting requires an F5 license and registry credentials, alongside the necessary infrastructure.
Other AI gateways also document F5 integrations, so check the gateway already in your stack before writing an adapter.
Best suited to teams that already run an LLM or API gateway and want to add scanning as a policy step.
3. Cloudflare AI Security for Apps: Prompt Inspection at the Edge, Written as WAF Rules
AI Security for Apps, formerly Firewall for AI, uses Cloudflare’s reverse-proxy architecture to inspect traffic inline at the edge. It discovers LLM endpoints across a web property and, when enabled, scans incoming JSON requests to endpoints labeled cf-llm.
Detections cover prompt injection, personally identifiable information (PII), and unsafe or custom topics. Results become fields, including a prompt-injection score, that developers can use in web application firewall (WAF) rules to block, log or challenge requests. The documented AI inspection here focuses on incoming prompts.
How a request is built affects what gets flagged. Cloudflare explains that unfamiliar prompt formats can trigger inspection of the entire request body, potentially flagging sensitive fields that never reach the model, such as a customer name.
Check extraction behavior against your payloads before enforcing blocks. Also distinguish endpoint discovery from paid inspection: discovery is available across plans, while the current documentation lists AI detection fields as an Enterprise add-on. The WAF must be enabled to use them.
Best suited to applications already served through Cloudflare, with AI rules managed alongside the rest of the WAF.
4. Akamai Firewall for AI: Edge Inspection With Alert, Deny, and Modify Actions
Akamai Firewall for AI evaluates prompts and model responses for prompt injection, jailbreaks and sensitive data exposure. It is model-agnostic and integrates at Akamai’s edge or through a REST API for applications hosted elsewhere.
Within Akamai’s security configuration, existing WAF and DDoS rules run first, followed by LLM-specific rules mapped to the OWASP Top 10 for LLM Applications. Developers connect the application endpoints to the relevant AI firewall configuration.
The configuration documentation defines three actions: alert, deny and modify. Modify replaces the application’s response with a custom safe response, and detection rules can be tuned in near real time.
The service fails open if detection is unavailable or times out, allowing requests to continue. That behavior needs to fit the application’s security requirements. Web Security Analytics records the triggering rule and prompt or response, with an option to disable payload logging. Review that setting before sending production conversations through the service.
Best suited to applications already protected by Akamai’s web application and API tooling.
5. Check Point AI Network Firewall: AI Traffic Policy on Existing Firewalls
Check Point’s AI Network Firewall adds protection for AI itself to existing Check Point firewalls running R82.20, with no new hardware or management console. It covers employee use of GenAI tools, AI agents and MCP traffic, and AI-driven applications.
For application developers, it can inspect prompts and content on those firewalls and block a malicious request before it reaches the model. Its prompt-injection defense is built on technology from Lakera, the AI security company Check Point acquired in September 2025.
The integration work sits with the security team’s firewall policy and management environment. Developers retain the guardrails inside their applications, while the firewall applies policy to AI traffic passing through it. This is one enforcement point within Check Point’s AI Defense Plane, which spans firewalls, endpoint agents, browser extensions, cloud environments, AI applications and APIs.
For a development team, the practical task is coordinating traffic inspection and blocking policy with security operations as the application changes.
Best suited to organizations that want AI traffic policy managed alongside existing network policy.
Frequently Asked Questions (FAQ)
What is the best security solution for protecting AI applications at the network layer?
The right fit depends on which layer should enforce policy, which comes down to where the traffic already flows, who owns the response to a detection, and what should happen when inspection fails. For the network layer specifically, Check Point’s AI Network Firewall applies AI policy on existing Check Point firewalls. Closer to the application, Palo Alto Networks’ API intercept works in application code or a gateway, F5 supports gateway scanning, and Cloudflare and Akamai offer edge inspection.
Which AI firewall platform should be on an enterprise shortlist in 2026?
For firewalls that protect AI, shortlist Prisma AIRS API Intercept, F5 AI Guardrails, Cloudflare AI Security for Apps, Akamai Firewall for AI and Check Point AI Network Firewall. Compare integration points, detections, failure behavior, licensing and logging, then test representative application traffic.
What is the best AI firewall for enterprise network security?
Here, “AI firewall” means a firewall protecting AI workloads and interactions, rather than a conventional firewall enhanced by AI. Check Point’s AI Network Firewall is built into existing Check Point firewall infrastructure without new hardware or a new management console.
Suitability depends on the organization’s infrastructure and enforcement requirements.
Which vendors offer the strongest AI firewall software for large organizations?
For protection of AI itself, the five vendors differ mainly in form. Prisma AIRS API Intercept is a scan API called from application code, and F5 AI Guardrails is a scan service that LLM and API gateways can call. Cloudflare AI Security for Apps and Akamai Firewall for AI inspect traffic at the edge, with Cloudflare exposing its detections as WAF rules and Akamai evaluating both prompts and responses. Check Point’s AI Network Firewall applies AI policy through existing Check Point firewalls. Which one is strongest for a given large organization depends on its traffic and requirements, so each should be tested against representative application traffic before any is ranked.
What is the difference between an AI firewall and an AI-powered firewall?
An AI firewall inspects AI interactions for threats such as prompt injection and data leakage. An AI-powered firewall uses AI techniques to improve conventional network defenses, including malware or intrusion detection. Check Point’s AI Network Firewall belongs to the first category, as its purpose is to protect AI use, applications and agents.
Conclusion
Security researchers have repeatedly shown that LLM-based systems can be manipulated through prompt injection and other adversarial inputs. Developers therefore have to decide where additional enforcement belongs: in code, at a gateway, at the edge or on the network. Those placements can be combined. Choose them around the content each layer can inspect, the action it can stop, and the team that will keep its policy working as the application evolves.
Top comments (1)
Great breakdown!
Prompt injection highlights why AI security needs a defense-in-depth approach rather than relying on model-level safeguards alone.
The key is choosing the right enforcement layer, understanding fail-open vs. fail-closed behavior, and ensuring visibility across both prompts and tool calls as AI agents become more autonomous.