Overview
Accenture RAI Red Teaming proactively tests AI systems against real-world adversarial behaviors to uncover safety, misuse, and policy-violating outcomes before deployment. The approach detects misaligned outputs from AI systems, identifying issues such as hate speech, harassment, political sensitivities, jailbreaks, profanity, and medical or legal advice disclaimers, while considering a company's brand voice. RAI red teaming provides an accelerated approach to identifying AI risks before deployment and before organizations institutionalize controls that may not align with how their AI solutions actually fail.
Our approach uses a human-agentic methodology to test AI systems under adversarial, realistic conditions designed to trigger violative or misaligned content and evaluate the effectiveness of guardrails. Accenture combines multidisciplinary human expertise with Amazon Bedrock-based agents to conduct adversarial tests and generate detailed reports. Accenture experts define the problem statement, articulate risk policies and risk tolerances, and communicate actionable insights from red teaming reports. The AI agents generate diverse test cases, execute attack prompts on target models, evaluate system responses across risk dimensions, and generate comprehensive reports on identified AI system vulnerabilities.
Adversarial testing challenges AI systems with a diverse array of prompts to provide timely insights. The solution provides greater transparency to help organizations proactively identify and address potential AI threats, supporting their efforts to deploy AI responsibly and with greater confidence.
This professional services offering relates to Amazon Bedrock and uses Amazon Bedrock-based agents in support of adversarial test execution, evaluation, and reporting.
This is a featured solution from Accenture Edge. Accenture Edge helps mid-market companies modernize with practical, scalable solutions, bringing enterprise-grade innovation within reach for growing businesses that move fast and expect more.
Highlights
- Flexible multimodal coverage: Adjusts testing sensitivity based on defined risk factors, company policies, and use-case requirements. Adapts testing to changing industry standards and emerging attack vectors. Covers global topics across multiple languages and cultural contexts to help organizations identify potential AI risks.
- Human-agentic testing methodology: Combines AI and automation with multidisciplinary human expertise. Centers tests on human-defined topics, policies, and risk tolerances. Supports human interpretation of identified vulnerabilities and development of actionable insights.
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