The Runtime Security component of Prisma AIRS is designed to protect your entire AI ecosystem at runtime. That includes:

• LLM-powered applications – Any app that relies on large language models, including chatbots, copilots, and internal assistants.
• AI models – The models themselves, helping safeguard them from misuse or manipulation while they’re running.
• Data – The data flowing into and out of your AI systems, including prompts, responses, and intermediate outputs.

By focusing on runtime, Prisma AIRS helps you manage what happens while your AI apps are actively being used, not just during development or testing.

Prisma AIRS is built to help you manage a range of runtime threats that commonly affect LLM-powered AI apps, including:

• Prompt injection – Attempts to override or subvert your system prompts and policies.
• Malicious code – Harmful code that may be generated, executed, or passed through your AI workflows.
• Toxic content – Undesired or harmful language that could impact users or violate policies.
• Sensitive data leaks – Unintended exposure of confidential or regulated information in prompts or responses.
• Resource overload – Excessive or abusive usage patterns that can strain infrastructure and degrade performance.
• Hallucinations – Inaccurate or fabricated outputs that can mislead users or downstream systems.

By addressing these areas, Prisma AIRS helps you rethink how you secure AI at runtime and reduce operational and compliance risk.

Prisma AIRS helps you reshape your AI security strategy by focusing on what happens when your AI systems are actually in use. Instead of treating AI security as a one-time setup, it supports a more continuous, operational approach:

• End-to-end runtime visibility – You gain insight into how prompts, responses, and data are being used in real time.
• Policy-driven protection – You can apply consistent controls to reduce risks like prompt injection, toxic content, and sensitive data exposure.
• Operational resilience – By monitoring for resource overload and hallucinations, you can better maintain performance and reliability for users.

This helps your teams reimagine AI security as an ongoing practice that supports safer, more trustworthy AI adoption across your organization.