Artificial Intelligence (AI) Risk and Governance

• Evaluate the existing AI governance structure, including policies, procedures, guidelines and control environments delineating roles and responsibilities.
  
  
• Benchmark against the EY AI Risk and Governance Framework to determine alignment with recognized risk domains such as model design, algorithmic fairness, data management, and system security.
  
  
• Identify control gaps across AI lifecycle stages (from initial ideation to system decommissioning) against the NIST AI RMF, EU AI Act, and ISO 42001.
  
  
• Design and implement bespoke governance controls for AI and Generative AI, tailored to the organization’s risk appetite and operational complexity.
  
  
• Establish escalation workflows and accountability mechanisms for AI oversight.
  
  
• Establish a Responsible AI framework that defines control tollgates across the AI lifecycle—from demand intake to decommissioning—while embedding foundational controls such as access management, incident response, and continuous monitoring to ensure secure, ethical, and compliant AI operations.

• Conduct an assessment to identify compliance with the NIST AI RMF, EU AI Act and ISO 42001.
  
  
• Classify AI systems by risk, assess their impact, and identify control gaps across the AI lifecycle, with a focus on Responsible AI principles.
  
  
• Evaluate governance structures, policies, and ethical review processes to ensure alignment with best practices and regulatory standards.
  
  
• Validate AI inventory, ensure compliance with regulations, and provide recommendations to remediate identified gaps.
  
  
• Develop a roadmap to mitigate AI risks, with clear timelines, actions, and KPIs for continuous improvement.

• Establish AI governance processes within the enterprise.
  
  
• Conduct a comprehensive AI model risk assessment for each use case before deploying it to production.
  
  
• Operationalize periodic monitoring for use cases in production environment to identify evolving risks.
  
  
• Identify process improvement opportunities to further mature the risk identification and mitigation process.
  
  
• Collaborate with monitoring teams to enable performance monitoring.

• Establish a structured internal audit framework for AI systems to regularly assess risk exposure, governance effectiveness, and compliance with regulatory standards.
  
  
• Ensure AI models adhere to the established policies, procedures, ethical AI principles, and internal governance framework to prevent bias, and establish transparency and accountability.
  
  
• Evaluate AI model behavior, accuracy, and unintended consequence detection by auditing operational metrics and error rates to prevent unreliable or biased outputs.
  
  
• Manage third-party AI risks in enterprises by conducting a structured review of third-party AI models and systems to ensure compliance with organization-defined ethical standards and frameworks.

It involves identifying, assessing, and mitigating the risks posed by AI models across their lifecycle—from design to deployment.