Bengaluru: F5 has announced the general availability of F5 AI Guardrails and F5 AI Red Team, two new solutions designed to secure mission-critical enterprise AI systems.
With these launches, F5 positions itself as the only vendor offering an end-to-end lifecycle approach to AI runtime security. The solutions focus on protecting AI agents through both out-of-the-box and custom guardrails.
The offerings are built to meet enterprise demands for flexible deployment, model-agnostic protection, and real-time policy adaptation. The tools aim to bring greater visibility and control to AI environments.
Already in use by Fortune 500 enterprises
F5 AI Guardrails and F5 AI Red Team are already deployed at leading Fortune 500 companies across multiple industries. These include sectors such as financial services and healthcare, where data protection and compliance are critical.
Kunal Anand, Chief Product Officer at F5, said, “Traditional enterprise governance cannot keep up with the velocity of AI. When policy lags adoption, you get data leaks and unpredictable model behavior. Organizations need defenses that are as dynamic as the models themselves. F5 AI Guardrails secure the traffic in real time, turning a black box into a transparent system, while F5 AI Red Team proactively finds vulnerabilities before they reach production. This allows organizations to stop fearing risk and start shipping apps and features with confidence.”
Addressing a rapidly changing risk landscape
As enterprises expand AI use across customer experiences, internal workflows, and decision-making systems, the threat landscape continues to evolve. Beyond external attacks, organizations now face risks such as adversarial model manipulation, data leakage, unpredictable user behavior, and stricter compliance requirements.
By pairing AI Guardrails and AI Red Team with existing protections like API security, web application firewalls, and DDoS defenses, enterprises can secure AI systems alongside traditional applications. This approach is expected to improve visibility and policy consistency while reducing reliance on fragmented point solutions.
