Firewall for AI: Do Cloudflare, F5, and Akamai's Built-In LLM Protections Replace a Dedicated AI Security Vendor?

This table reflects what each vendor's own product documentation states, not aspirational roadmap language.

The row worth reading twice is AWS WAF. As of this research, AWS has not shipped a managed rule group inside WAF itself for prompt injection or other LLM-specific attacks. Instead, AWS addresses the problem one layer up, inside Amazon Bedrock Guardrails, which applies content moderation, prompt attack detection, topic classification, PII redaction, and hallucination detection, but only to traffic that actually flows through Bedrock. A team running a homegrown LLM application outside Bedrock gets none of that coverage from AWS's WAF layer at all.

Cloudflare built AI Security for Apps as a genuine extension of its existing WAF ruleset, not a separate product bought or partnered in. It analyzes request bodies and query parameters headed toward LLM endpoints using a combination of signature matching for known injection patterns and machine learning classifiers for novel ones, then assigns a score you act on through custom rules or rate limiting. Its architecture keeps the detection at the same layer as the rest of Cloudflare's WAF, which is why it can be enabled with no new infrastructure and why it is bundled at no extra cost.

Akamai took a similar native-build approach but scoped the product more explicitly around the full request-response cycle. Firewall for AI inspects both the prompt going into the model and the response coming back, applying policy-based enforcement against prompt injection, data exfiltration, toxic output, and AI-specific denial-of-service patterns. It runs alongside Akamai's existing App & API Protector rather than replacing it, so a team adopts it as an additional layer on infrastructure it likely already has in place.

F5's approach is architecturally distinct from the other two, and worth understanding clearly before you evaluate it. F5 did not build its own prompt injection detection engine from scratch. It partnered with Prompt Security, a dedicated AI security vendor, and integrated that vendor's firewall for AI into the F5 Distributed Cloud Platform and App Stack. F5 supplies the surrounding WAF, bot defense, DDoS protection, and API discovery; Prompt Security supplies the actual LLM-specific inspection of inbound queries and outbound responses. This is not a criticism of the approach; a specialist vendor's detection engine embedded in your existing perimeter platform can be a genuinely strong combination. But it means that evaluating 'F5's AI security' is, in practice, evaluating Prompt Security's detection engine plus F5's deployment and policy tooling around it, which is a different due-diligence exercise than evaluating a fully in-house feature.

Dedicated AI security vendors like Imperva start from a different premise entirely: the application is architected as a reverse proxy purpose-built for GenAI traffic, not a WAF with AI detection added on. Imperva's AI Application Security integrates with a single line of code in front of chatbots, virtual assistants, and AI-driven workflows, and is built specifically to address the OWASP Top 10 for LLM Applications: prompt injection, sensitive information disclosure, system prompt leakage, improper output handling, and unbounded consumption, among others. Because the entire product exists for this one job, its detection logic can carry deeper conversational context and a narrower, more specialized threat model than a general-purpose WAF extending its reach into a new traffic type.

Cloudflare's AI Security for Apps deploys with essentially no new infrastructure for a team already on Cloudflare's network. It is a feature toggle inside the existing WAF configuration, available down to the Free plan, which makes it the lowest-friction option to simply turn on and see what it flags.

Akamai's Firewall for AI deploys as an add-on within Akamai's existing security portfolio, alongside App & API Protector. A team already routing traffic through Akamai gains the feature without standing up new infrastructure, but it is a distinct product to configure and license, not a checkbox inside an existing plan tier.

F5's deployment runs through the F5 Distributed Cloud Platform, with the Prompt Security engine integrated into that control plane. A team needs to be on, or move to, F5's Distributed Cloud App Stack to get this specific integration; it is not a feature you enable inside a standalone F5 BIG-IP deployment without that broader platform.

AWS WAF requires no separate deployment for AI detection because it does not have the feature; the closest AWS-native equivalent, Bedrock Guardrails, deploys as configuration inside Bedrock itself and only protects traffic that actually transits Bedrock's managed model endpoints, leaving any LLM traffic outside Bedrock uncovered by this specific control.

Imperva's AI Application Security deploys as a SaaS reverse proxy positioned in front of the AI application, independent of which WAF or cloud platform already sits in front of the rest of your traffic. That independence is the point: it does not require migrating off an existing WAF vendor to add this layer, but it is a separate proxy hop and a separate vendor relationship to manage.

Cloudflare's detection is explicitly model-agnostic and works regardless of which LLM backend your application calls, since it inspects the traffic passing through Cloudflare's network rather than integrating with a specific model provider's API. Its documented scope covers prompt injection, PII in incoming prompts, and unsafe or custom topic detection, with scoring fed into your own custom rules.

Akamai's scope extends further into the response side by design, explicitly covering data exfiltration and intellectual property theft detection in model output in addition to inbound prompt threats, which matters for teams whose primary risk is a model leaking proprietary training data or internal documents back to a user rather than only being tricked by a malicious prompt.

F5's coverage, inherited from Prompt Security, spans both inbound queries and outbound responses and is described as protecting against prompt injections, jailbreaking, sensitive data disclosure, harmful responses, and denial-of-wallet style abuse, at every touchpoint of generative AI in an organization according to F5 and Prompt Security's own materials.

AWS's Bedrock Guardrails, the closest AWS-native comparison point, covers content moderation, prompt attack detection, topic classification, PII redaction, and hallucination detection, but strictly within Bedrock. A team running self-hosted models, a different cloud's model service, or a third-party API like OpenAI's directly gets no coverage from this control at all, which is the single most important limitation to understand if your organization is an AWS shop but not exclusively a Bedrock shop.

Dedicated vendors like Imperva are architected to be model- and provider-agnostic in the same way Cloudflare's feature is, inspecting the traffic in front of the application regardless of which model or provider sits behind it, and are built to cover a wider slice of the OWASP LLM Top 10 by design rather than as an extension of an existing ruleset. It is also worth knowing that dedicated point solutions in this specific category, such as Lakera or Prompt Security's own standalone product outside the F5 integration, exist precisely because some security teams conclude that a WAF vendor's AI feature, however capable, is secondary to that vendor's core product and roadmap, while an AI-security-only vendor's entire engineering investment goes into this one detection problem.

Cloudflare's feature is the lowest-operational-lift option of the group for a team already on the platform: it is a dashboard toggle plus custom rule configuration to act on the injection score, with no new proxy hop or vendor relationship to manage. The tradeoff is that you are relying on whatever scope Cloudflare has built into a WAF-native feature, rather than a product whose only job is AI traffic inspection.

Akamai's operational effort is moderate for an existing Akamai customer: configuring a genuinely separate product within a familiar console, with policy tuning for both inbound and outbound inspection rather than a single toggle.

F5's operational effort includes an added dimension the others do not: because the detection engine is Prompt Security's, your team may need to coordinate policy tuning and support escalations across two vendor relationships (F5 for the platform, Prompt Security for the detection logic) rather than one, even though the day-to-day console experience is unified through F5's platform.

AWS-native teams face the highest effort if their LLM traffic is not fully inside Bedrock, since there is no AI-specific WAF feature to configure at all; the practical operational task becomes deciding whether to migrate LLM traffic into Bedrock to get Guardrails coverage, or add an entirely separate third-party layer.

A dedicated vendor like Imperva adds a new proxy and a new vendor relationship regardless of what WAF you already run, which is real incremental operational surface, but it is scoped narrowly to one job, which some security teams find easier to own precisely because it does not compete for attention with a broader WAF configuration.

Cloudflare states that AI Security for Apps carries no additional charge and is available on Free, Pro, and Business plans, not gated to Enterprise, which is a meaningfully different posture from most of the other options here and worth confirming still holds at the plan tier you actually run.

Akamai and F5 do not publish flat, self-service pricing for their AI-specific firewall features; both are positioned as add-ons within a broader commercial relationship (Akamai's security portfolio, F5's Distributed Cloud Platform respectively), so budgeting for either means a sales conversation rather than a published rate card.

AWS WAF itself has no AI-specific line item because it has no AI-specific feature; Bedrock Guardrails is priced separately from WAF and scoped only to Bedrock traffic.

Dedicated vendors including Imperva similarly do not publish flat public pricing for AI Application Security; expect a sales-driven quote based on request volume and deployment scope, the same as most enterprise security add-ons in this category.

Cloudflare's strength is that a genuinely capable, actively maintained detection feature is available at no extra cost to any customer already on the platform, which makes it close to a no-brainer to enable as a first layer even if you plan to add more. Its limit is that it is one feature inside a much larger WAF product, extended to cover a new traffic type rather than purpose-built for it, and its outbound response inspection is documented more narrowly than its inbound prompt inspection.

Akamai's strength is a genuinely dual-direction (input and output) native detection engine with explicit IP-theft and data-exfiltration framing, which is a real advantage for organizations most worried about a model leaking sensitive internal content. Its limit is that it is a separate product to license and configure, not a bundled feature, and like Cloudflare's, it is built by a WAF vendor extending into AI rather than an AI-native company.

F5's strength is that its AI detection logic comes from a vendor, Prompt Security, whose entire product is this problem, which means the underlying detection may carry more AI-specific depth than a WAF vendor's own bolt-on feature. Its limit is the two-vendor coordination overhead this partnership structure implies, and that it requires F5's Distributed Cloud App Stack specifically rather than being available inside a standalone BIG-IP deployment.

AWS WAF's limit is the simplest to state: there is no AI-specific feature at this layer at all. Its Bedrock Guardrails product is a real and capable answer, but only for the subset of traffic that runs through Bedrock, leaving a genuine gap for any AWS-hosted LLM application built outside it.

Dedicated vendors like Imperva have the strength of singular focus: the whole product exists to solve this one problem, typically with broader OWASP LLM Top 10 coverage and provider-agnostic deployment. The limit is cost and operational surface: it is a new vendor relationship and a new proxy hop layered on top of whatever WAF you already run, which is real overhead for a team hoping to consolidate rather than add vendors.

None of these paths is correct for every team. The right answer depends on how much of your traffic is genuinely LLM-facing, how sensitive your model's outputs are, and whether your existing WAF vendor's AI feature actually reaches the specific risk you are worried about.

If your organization runs no customer-facing LLM feature and only uses AI internally through a vetted, closed tool with no public-facing prompt surface, evaluating any of these products today is likely premature. The realistic threat model for a fully internal, low-exposure AI tool is closer to standard access control and logging hygiene than adversarial prompt injection from the open internet, and adding a firewall for AI in that scenario is solving a problem you do not yet have.

A firewall for AI, whether built into your WAF or purpose-built by a dedicated vendor, earns its place once you have a public-facing or customer-reachable LLM surface: a chatbot, an AI-assisted support flow, an API that accepts freeform user input destined for a model. At that point, the decision is not whether to add this layer but which one, and whether your existing WAF vendor's built-in feature closes enough of the gap or whether the sensitivity of what the model can see and say justifies a dedicated layer on top. If your specific concern is adversarial prompt injection accuracy rather than the broader AI security surface, our runtime prompt injection firewall comparison evaluates purpose-built products for exactly that narrower job, and our AI red teaming tools comparison covers the adjacent problem of testing your model's defenses before an attacker does, rather than filtering traffic against them in production.

Whichever combination you evaluate, run the same checklist rather than trusting a vendor's feature page or a partner's press release alone.