F5 AI Security Platform

An AI security platform provides centralized runtime protection, governance, and observability for AI models, applications, and agents. It protects enterprise LLMs by monitoring every interaction and blocking threats like prompt injection, data leakage, and other kinds of harmful outputs. Unlike point solutions, an AI security platform unifies capabilities that reinforce each other such as adversarial testing informing runtime defenses, or AI discovery identifying the most urgent models for security testing based on workforce adoption. This integrated approach delivers a more robust defense that fragmented tools simply can't match.

Open-source AI security tools offer a starting point but require significant internal expertise to configure, deploy, and maintain. They're community-driven, which often means slower updates, limited compliance-ready templates, and gaps in audit trails required for regulated industries. Commercial AI security tools provide enterprise-grade capabilities including dedicated threat research, regular updates against emerging attack techniques, and out-of-the-box compliance controls needed for enterprise AI workloads.

AI governance is the framework of policies, controls, and oversight mechanisms that ensure AI systems align with organizational standards and regulations. It includes defining how AI is allowed to interact with users and data, maintaining audit-ready logs of AI activity, and aligning deployments to the risk management frameworks and compliance obligations specific to the organization. The F5 AI Security Platform helps teams operationalize AI governance with customizable policy enforcement aligned to unique risk categories and providing auditability for every model, user, and agent action.

AI Security platforms are purpose-built to secure and govern enterprise AI workloads, rather than comprehensive security and user activity monitoring. Traditional network threats exist in packets and data, whereas AI threats live in the words and tokens exchanged between users, agents, and models. Traditional cybersecurity solutions should be used in tandem with dedicated AI security tooling for an effective defense-in-depth approach.

Model provider guardrails are typically a baseline protection designed to meet the provider’s compliance obligations and risk appetite rather than protections extensible to all enterprise use cases, and risk definitions. In recent SecureIQLab testing of foundational models’ built-in guardrails against sophisticated AI attacks, roughly 13% of attacks were successfully blocked. Model providers may offer limited security controls but the efficacy of those controls does not meet the standards of most enterprise workloads and often restricts those controls to specific models rather than the full breadth of an organization’s AI inventory.