Cloudflare Firewall for AI Alternatives by Control Boundary

Cloudflare Firewall for AI has a new name and a split buying path. The current WAF documentation calls it AI Security for Apps (formerly Firewall for AI). A separate Cloudflare product, AI Gateway, offers Guardrails and DLP for prompts and model responses. A buyer searching for a replacement therefore has to identify the control being replaced: inbound prompt scoring at the WAF, model safety filtering, sensitive-data inspection, or identity-bound authorization on a routed LLM request.

TL;DR

• Cloudflare AI Security for Apps is the current name for Firewall for AI. It scores prompt injection in incoming application traffic and lets Cloudflare WAF rules act on the score.
• Cloudflare AI Gateway has separate beta Guardrails and DLP controls that proxy model traffic and inspect prompts or responses.
• Amazon Bedrock Guardrails fits teams standardized on Bedrock models and APIs.
• Microsoft Prompt Shields fits applications that need a detection result for direct or document-based prompt attacks and can own the response logic.
• Check Point AI Guardrails fits teams prepared to integrate a screening API into application control flow.
• DeepInspect fits authenticated HTTP LLM routes that require identity-aware policy and an independent record for each decision.

The Cloudflare product boundary has changed

Cloudflare's current prompt injection detection documentation states that AI Security for Apps was formerly Firewall for AI. It assigns an injection score between 1 and 99 to incoming prompts. Lower values indicate greater risk. Customers use that field in WAF custom rules or rate-limiting rules, with the threshold determining how aggressively traffic is blocked or challenged.

That WAF path protects an AI application exposed through Cloudflare. The request arrives at the application's network edge, Cloudflare analyzes the prompt, and a rule acts on the resulting score. It is a good fit when the asset under review is a public or internal web application already behind Cloudflare and the immediate requirement is prompt-injection screening.

Cloudflare's current AI Gateway Guardrails are on another path. They proxy traffic between an application and model providers, inspect user prompts and model responses, and can flag or block configured content categories. Guardrails are marked beta in the documentation. The AI Gateway DLP control is also beta and scans text in request and response bodies. Its policies apply uniformly at gateway level. Cloudflare recommends separate gateways when applications need different DLP profiles.

A diagram with two orange boxes makes the split visible. One box sits in front of the AI application as WAF. The other sits behind the application on its outbound model route as AI Gateway. Treating those boxes as one product produces a misleading replacement list.

The AI firewall, gateway, and proxy comparison explains the placement question in more detail.

Amazon Bedrock Guardrails fit an AWS model path

Amazon Bedrock Guardrails provides configurable filters for user inputs and model responses. AWS documents content categories, denied topics, sensitive-information filters, word filters, contextual grounding checks, and automated reasoning checks. Teams can attach a guardrail to supported Bedrock inference calls by guardrail ID and version. They can also call the ApplyGuardrail API without invoking a foundation model.

Choose Bedrock Guardrails when model inference already runs through Amazon Bedrock and the application needs one AWS-managed safety configuration across supported models. The versioned guardrail and built-in test window give engineers a concrete deployment object to evaluate.

The control is tied to the Bedrock service and its supported invocation patterns. A second model path through Azure, OpenAI, or a self-hosted endpoint needs its own integration. Identity authorization is also an application and IAM design question. A content filter can classify a prompt while leaving unresolved whether this particular employee may send that data class to that destination.

Microsoft Prompt Shields fit attack classification

Microsoft's Prompt Shields documentation separates user prompt attacks from document attacks. The latter covers instructions hidden in retrieved documents, emails, or web pages. In Microsoft Foundry, configured controls can filter detected attacks. The standalone Azure AI Content Safety API returns a result, and customer application logic decides whether to allow, block, review, or replace the content.

Choose Prompt Shields when direct and indirect prompt attacks are the narrow problem and the engineering team owns the surrounding control flow. The document-attack input is especially relevant to RAG applications because it gives retrieved content its own inspection field instead of blending it into a single prompt string.

Microsoft explicitly limits the result. Prompt Shields does not establish factual accuracy, policy compliance, provenance, or task alignment. That boundary keeps the tool useful: it is an attack classifier, with enforcement dependent on the deployment route. The prompt injection detection guide covers the difference between a detection signal and an authorization decision.

Check Point AI Guardrails fit application-managed screening

Check Point's AI Guardrails API documentation exposes a /v2/guard endpoint for screening an LLM interaction. The application submits input or output content, receives a flagged result, and decides what mitigating action follows. Detect mode lets teams observe results without triggering the configured enforcement response. Enforce mode returns a flagged decision when a configured detector finds a threat.

Choose this route when the application team wants a dedicated screening API and accepts responsibility for inserting calls at the correct points. Check Point recommends screening interactions at runtime. Its documentation describes output screening after generation and before delivery, with optional input screening before model processing.

That integration shape deserves a failure test. Disconnect the screening API. Send a request and record what the application does. Then repeat the test with a timeout during response screening. The application owns the call sequence and the mitigating action, so its fallback behavior becomes part of the security control. My opinion is that a vendor comparison that skips this timeout test is a product tour, not a security evaluation.

The shortlist should follow the route

Cloudflare AI Security for Apps fits inbound WAF inspection for applications already routed through Cloudflare. Cloudflare AI Gateway fits outbound model traffic sent through that gateway. Bedrock Guardrails fits the AWS model plane. Prompt Shields supplies attack classification for Foundry or application-managed API use. Check Point supplies a screening API that the application places in its own flow.

A regulated deployment may use more than one. Browser sessions, local models, and endpoint-resident agents can bypass all of these HTTP routes. Give those paths to browser or endpoint controls instead of stretching a gateway claim across the laptop. The AI gateway architecture guide provides a route inventory for backend model calls.

For each shortlisted product, run the same acceptance test:

• Send an allowed prompt with a known application identity and record the fields retained.
• Send a prompt that triggers the relevant injection or data policy and confirm where processing stops.
• Change the policy, repeat the request, and identify when the new version takes effect.
• Force an inspection timeout and record the fail-open or fail-closed outcome.
• Retrieve the event later by transaction ID and identify which system controlled the write path.

DeepInspect

DeepInspect governs authenticated HTTP traffic deliberately routed between users or agents and LLM endpoints. The application supplies identity and role context. DeepInspect evaluates that context with prompt classification, destination, and a versioned policy before forwarding allowed traffic, then creates a per-decision audit record outside the calling application's write path.

This boundary is narrower than Cloudflare's WAF and different from a model-safety API. It fits teams asking which authenticated actor was permitted to send a specific data class to a particular model route. Browser-only chat, local model execution, endpoint tool calls, and traffic bypassing the proxy remain assigned to their own controls.

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