AI Guardrails

F5 AI Guardrails is a runtime security platform that protects deployed AI models, applications, and agents from adversarial attacks, data leakage, and compliance failures. It operates at the inference layer, inspecting user prompts and AI-generated responses in real time to block malicious inputs and outputs. The platform is designed for security teams and AI engineers who need to maintain security posture as AI systems move from pilot to production, especially in regulated industries like healthcare and finance. It is delivered through the F5 SaaS platform or can be self-hosted on public clouds (EKS on AWS, AKS on Azure, GKE on Google) or on-premises using Red Hat OpenShift. F5 AI Guardrails is model-agnostic, supporting frontier models with preset configurations for popular enterprise and open-source LLMs.

Key capabilities include low-latency runtime security with dynamic model routing to avoid failover states, content moderation filters to reduce toxic or biased outputs, and distributed data protection that inspects AI interactions across models and apps for DLP and policy violations. The platform combats adversarial attacks like prompt injection and jailbreaks, and it provides audit-ready observability with scanning and logging tools for regulatory compliance. It includes built-in guardrails for the EU AI Act and customizable country-specific guardrails, and it helps meet standards like GDPR and HIPAA. F5 AI Guardrails also integrates with F5 AI Red Team for continuous adversarial testing, and it achieved validated efficacy against 17,733 adversarial test cases in an independent SecureIQLab evaluation.

F5 AI Guardrails competes with Preamble, Protecto, and Cisco AI Defense. Unlike traditional WAFs and API security tools, which are ineffective against prompt injections and jailbreak attacks that comply with API specs, F5 AI Guardrails operates specifically at the inference layer to block these threats. It differentiates itself through its integration with the broader F5 Application Delivery and Security Platform (ADSP), offering unified traffic management and security for AI workloads. The platform's flexible deployment options—SaaS or self-hosted across multiple cloud providers—give it an edge for enterprises with hybrid or multi-cloud strategies, though it faces strong competition from dedicated AI security startups and established network security vendors expanding into AI.

Honest trade-offs: The platform requires organizations to define and enforce access policies for proprietary and sensitive data flows, which adds upfront configuration overhead. Adopting updates may involve manual steps that can disrupt live projects, as noted in user feedback. While it provides comprehensive runtime security, it does not replace model-level guardrails or training-time defenses, meaning teams must layer it with other controls for full coverage. Pricing is not publicly disclosed, and the platform's effectiveness depends on continuous policy tuning to keep pace with evolving adversarial techniques, which demands ongoing security team investment.

Delivered through the F5 SaaS platform or self-hosted on public clouds (EKS, AKS, GKE) or on-premises (Red Hat OpenShift); no public pricing tiers or figures available.