Responsible AI Suite

2. Conduct AI Risk Assessment: Build a detailed inventory of AI systems-automatically integrated with platforms like Amazon SageMaker and Bedrock-and classify them by risk.

3. Enable Systemic RAI Testing: Apply a reference architecture and over 280 quantitative metrics to assess AI model risks, fairness, robustness, transparency, and more.

4. Ongoing Monitoring & Compliance: Establish centralized monitoring to implement a continuous control plane for compliance and risk mitigation, enabling long-term accountability.

5. Red Teaming: The Responsible AI (RAI) Red Teaming approach is designed to detect flaws and vulnerabilities in Generative AI models. It aims to protect against brand and reputational damage, keep pace with emerging issues, and expose vulnerabilities not traditionally found by cybersecurity testing. The approach involves identifying AI Issues by creating Testing Prompts, Recording Responses and Assessing Results; Using this approach addresses the limitations of traditional manual RAI Red Teaming processes, which are time-consuming, manual, and narrow in scope. It offers advantages such as lower costs, real-time adaptation to emerging issues, advanced detection and understanding of language, a standardized approach to testing and reporting results, and reduced time to market while improving scalability.

The RAI Suite can be offered as a service hosted on Accenture cloud or deployed on client environment. As a service, outcomes delivered can include enterprise-wide RAI maturity assessment, RAI strategy and roadmap, revised RAI policies and controls, risk taxonomy, RAI operating model, RAI training plan, regulations readiness report, updated risk controls and thresholds, data and AI risk test results, mitigated AI risks, RAI monitoring office governance and operating model, AI, prompt and data risk assessment results, and mitigation approaches, to name a few.