Advanced Target Profiling for Context-Aware AI Red Teaming
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Advanced Target Profiling for Context-Aware AI Red Teaming

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Advanced Target Profiling for Context-Aware AI Red Teaming

Gather comprehensive contextual information about your AI endpoints to enable more accurate and relevant AI Red Teaming assessments.
Target Profiling enhances your AI security assessments by 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 get 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.
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 profile 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.