Summary
Prisma AIRS assesses and helps manage security risks across enterprise AI models, applications, agents, and related artifacts.
About
Prisma AIRS supports enterprise AI security assessment and oversight by combining red-team testing with risk management across models, applications, agents, and related artifacts.
Prisma AIRS is Palo Alto Networks’ platform for assessing and managing security risks in enterprise AI systems. It tests models, applications, and agents against attack techniques and scenarios, with documentation describing more than 50 techniques and 500 scenarios and assessments that can launch in under 10 minutes. The platform also addresses sensitive data, agent identities, autonomous actions, AI costs, and artifacts such as skills and MCP servers. Teams can use assessment reports to identify weaknesses before deployment and maintain oversight as AI use expands.
Key Features
Tests AI systems against more than 50 attack techniques and 500 scenarios, according to October 2026 documentation.
Supports assessments of models, applications, agents, skills, and MCP servers.
Provides reports intended to help teams identify weaknesses before deployment.
Addresses sensitive data, agent identities, autonomous actions, and AI costs.
Can launch assessments in under 10 minutes, according to the provided documentation.
Real-World Use Cases
Pre-deployment AI security assessment
Red-team AI applications and agents before release to identify weaknesses that may require remediation.
Ongoing oversight of AI deployments
Assess systems as enterprise AI use expands and review reports to support continued risk oversight.
Agent and artifact risk review
Evaluate agents and related artifacts, including skills and MCP servers, as part of an AI security process.
AI risk governance
Address security considerations involving sensitive data, agent identities, autonomous actions, and AI costs.
Business Opportunities
AI application security assessment
Problem: Organizations may deploy AI applications without systematically testing them against relevant attack scenarios.
Solution: Use platform assessments and reports to identify weaknesses before deployment.
Estimated ROI: Potentially meaningful through earlier identification of security weaknesses and more consistent assessment. Financial return cannot be quantified from the provided information.
Execution: Integrate assessments into AI application review and release processes, then route identified issues to responsible engineering and security teams.
Enterprise AI risk oversight
Problem: As AI deployments grow, teams may find it difficult to maintain visibility into risks across models, agents, and related artifacts.
Solution: Use assessments and reports to support oversight across AI systems and address data, identity, autonomous-action, and cost considerations.
Estimated ROI: Potential value includes improved risk visibility and more repeatable oversight. Specific savings or financial returns are unknown.
Execution: Establish ownership for AI assets and assessment findings, and incorporate recurring reviews into existing governance processes.