Target Profiling enhances your AI security assessments by automatically gathering comprehensive contextual information about your AI endpoints, enabling more accurate and relevant vulnerability discoveries. When you conduct AI Red Teaming assessments without proper context, you receive only generic baseline risk evaluations that may not reflect real-world threats specific to your environment. With Target Profiling, you can leverage both user-provided information and intelligent agent-based discovery to build detailed profiles of your AI models, applications, and agents.

Target Profiling automatically collects critical background information about your AI systems, including industry context, use cases, competitive landscape, and technical foundations such as base models, architecture patterns, and accessibility requirements. AI Red Teaming's agentic profiling capability interrogates your endpoints to discover configuration details like rate limiting, guardrails, and system prompts without requiring manual input. This automated approach saves you time while ensuring comprehensive coverage of contextual factors that influence security risks.

The feature provides you with a centralized Target Profile page where you can visualize all gathered context, review assessment history, and track risk scores across multiple scans over time. You can distinguish between user-provided information and agent-discovered data, giving you full transparency into how your target profiles are constructed. When you need to update target information due to system changes, you can easily modify profiles or trigger new agentic profiling sessions.

Target Profiling directly improves your AI Red Teaming effectiveness by enabling context-aware assessments that identify vulnerabilities specific to your industry, use case, and technical implementation. AI Red Teaming uses your target's industry and competitive context to evaluate brand and reputational risks more accurately, while technical configuration details help identify implementation-specific vulnerabilities. By maintaining detailed profiles and assessment histories, you can track your security posture improvements over time and ensure that your AI systems remain protected as they evolve in production environments.

You can now leverage Prisma AIRS AI Red Teaming's enhanced capabilities to comprehensively assess the security posture of your autonomous AI agents and multi-agent systems. As your organization deploys agentic systems that extend beyond traditional AI applications to include tool calling, instruction execution, and system interactions, you face an expanded and more complex attack surface that requires specialized security assessment approaches. This advanced AI Red Teaming solution addresses the unique vulnerabilities inherent in both pro-code agents and supported no-code/low-code frameworks by employing agent-led testing methodologies that craft targeted goals and attacks specifically designed to exploit agentic system weaknesses.

When you configure your AI Red Teaming assessments, the system automatically tailors its approach based on your target endpoint type, enabling you to uncover critical vulnerabilities such as tool misuse where malicious actors manipulate your AI agents to abuse their integrated tools through deceptive prompts while operating within authorized permissions. The solution also identifies intent breaking and goal manipulation vulnerabilities where attackers redirect your agent's objectives and reasoning to perform unintended tasks. Through comprehensive target profiling, the agent-led AI Red Teaming capability gathers complete contextual information about your endpoints and develops sophisticated attack scenarios that traditional security testing approaches might miss.

Your security reports now provide enhanced visibility into agent-specific vulnerabilities versus generic security issues, with risk scoring algorithms adapted to properly weight the unique threats facing agentic systems. The automated reporting includes specialized summaries and recommendations that emphasize the specific vulnerabilities discovered in your agentic deployments, helping you understand not just what risks exist but how they relate to your broader AI security strategy. This targeted approach ensures you can confidently deploy AI agents in production environments while maintaining robust security controls against the evolving threat landscape targeting autonomous AI systems.

The Remediation Recommendations feature enables you to seamlessly transition from identifying AI system vulnerabilities through Red Teaming assessments to implementing targeted runtime policy configurations that address your specific risks.

When you conduct AI Red Teaming evaluations on your AI models, applications, or agents, this integrated solution automatically analyzes the discovered security, safety, brand reputation, and compliance risks to generate contextual remediation recommendations that directly address your specific vulnerabilities. Rather than configuring runtime security policies through trial and error, you receive intelligent guidance that maps each identified risk category to appropriate guardrail configurations, such as enabling prompt injection protection for security vulnerabilities or activating toxic content moderation for safety concerns.

You can leverage this capability to close the critical gap between risk assessment and mitigation in your AI deployment lifecycle. The feature provides you with detailed security profile recommendations that specify optimal runtime protection settings based on your AI Red Teaming results, eliminating the guesswork typically associated with configuring AI security controls. For organizations deploying AI systems in production environments, this capability ensures that your runtime security configurations are informed by actual risk insights rather than generic best practices, resulting in more effective protection against the specific threats your AI systems face.

The remediation recommendations appear directly in your AI Red Teaming reports, providing you with actionable guidance on creating appropriate security profiles with the necessary parameters to protect against identified threats. You can manually create and attach these recommended security profiles to your desired workloads, transforming AI risk management from a reactive, disconnected process into a proactive workflow that directly connects vulnerability discovery with targeted protection measures.

You can now generate executive-ready AI Red Teaming assessment reports that provide comprehensive security insights tailored for top executives of your organizations ((like CEO, CFO, COO, CTO). This enhanced reporting capability transforms technical vulnerability data into strategic intelligence that you can easily share with Chief Information Security Officers (CISOs), Chief Information Officers (CIOs), and other executives who need to understand your organization's security posture at a high level. The feature leverages advanced LLM technology to automatically generate concise summaries that distill complex attack simulation results into clear verdicts about your target endpoint's safety, security, and compliance alignment, while identifying the most critical attack vectors and their business implications.

When you complete an AI Red Teaming scan, you receive an AI-powered overview that synthesizes target profiling data with vulnerability findings to provide contextual insights about your security risks. This executive summary eliminates the need for manual interpretation of technical data, allowing you to quickly understand which attack methods pose the greatest threats to your systems and what the potential business impact might be. You can then export these comprehensive insights as professional PDF reports that maintain the detailed technical information security teams require while presenting the strategic overview that executives need for decision-making.

This capability is particularly valuable when you need to communicate security assessment results across different organizational levels or when preparing briefings for leadership meetings. Rather than struggling to translate technical vulnerability reports into business language, you can rely on the AI Red Teaming generated executive report to articulate security, safety, compliance, brand, and business risks in terms that resonate with executive audiences.

The exportable PDF format ensures you can easily share findings in board presentations, compliance documentation, or strategic planning sessions while maintaining the granular attack details that technical teams use for remediation efforts.

When you conduct AI Red Teaming scans using Prisma AIRS, you may encounter situations where scans fail completely or complete only partially due to target system issues or connectivity problems. The Error Logs and Partial Scan Reports feature provides you with comprehensive visibility into scan failures and enables you to generate actionable reports even when your scans don't complete successfully. You can access detailed error logs directly within the scan interface, both during active scans on the progress page and after completion in the scan logs section, allowing you to quickly identify whether issues stem from your target AI system or the Prisma AIRS platform itself.

This feature particularly benefits you when conducting Red Teaming assessments against enterprise AI systems that may have intermittent availability or response issues. When your scan completes the full simulation but doesn’t receive valid responses for all attacks, AI Red Teaming marks it as partially complete rather than failed. You can then choose to generate a comprehensive report based on the available test results, giving you valuable security insights even from incomplete assessments. AI Red Teaming transparently informs you about credit consumption before report generation and clearly marks any generated reports as partial scans, indicating the percentage of attacks that received responses.

By leveraging this capability, you can maximize the value of your Red Teaming efforts, troubleshoot scanning issues more effectively, and maintain continuous security assessment workflows even when facing target system limitations or temporary connectivity challenges during your AI security evaluations.

Palo Alto Networks' is an automated solution designed to scan any AI system—including LLMs and LLM-powered applications—for safety and security vulnerabilities.

The tool performs a Scan against a specified Target (model, application, or agent) by sending carefully crafted attack prompts to simulate real-world threats. The findings are compiled into a comprehensive Scan Report that includes an overall Risk Score (ranging from 0 to 100), indicating the system's susceptibility to attacks.

Prisma AIRS offers three distinct scanning modes for thorough assessment:

A key feature of the service is its single-tenant deployment model, which ensures complete isolation of compute resources and data for enhanced security and privacy.