Artificial Intelligence (AI) Risk and Governance

EY AI Risk and Responsible AI Governance Framework help organizations achieve global regulatory compliance while promoting accountable AI development. This framework facilitates the adoption of Responsible AI practices, offering a clear and informed approach to effectively manage and govern AI-related risks within enterprises, ensuring secure and compliant AI deployment.

Connect with us

Key offerings

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.
Perform technical assessments of LLM-based systems to evaluate resiliency, robustness, and threat exposure.

Evaluate the controls in place and their effectiveness for model integrity, input and output validation, error handling, and resilience against adversarial manipulation.

Evaluate controls for periodic testing against vulnerabilities like data poisoning, remote code execution, and AI-specific risks such as LLM prompt injection and training data extraction.

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.

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

Why is AI governance important for organizations?
Strong governance ensures that AI is used responsibly, aligns with business values, and meets regulatory expectations, minimizing reputational and legal risks.

What does a Responsible AI framework include?
It typically covers fairness, transparency, explainability, data integrity, and continuous monitoring of AI behavior.

How can businesses ensure AI compliance?
By embedding legal, ethical, and regulatory requirements into model development and regularly auditing AI systems for alignment.

What are the key risks associated with AI models?
Bias, lack of explainability, data drift, misuse, and security vulnerabilities are among the top concerns for AI at scale.

How do you conduct an AI risk assessment?
Through a structured evaluation of data quality, model behavior, performance drift, compliance exposure, and alignment with intended use.

What is model risk and control assessment in AI?
It is the process of validating model assumptions, performance, and governance controls to ensure that AI outputs are reliable and accountable.

Why is Responsible AI critical for regulatory compliance?
Regulators globally are drafting AI-specific rules—Responsible AI practices help organizations stay ahead and avoid costly compliance gaps.

How do you audit AI systems for fairness and bias?
By testing models on diverse datasets, analyzing outcomes for discrimination, and applying fairness metrics validated by ethical frameworks.

What are best practices for AI governance in enterprises?
Cross-functional oversight, transparent decision-making, ethical review boards, and continuous education are foundational to enterprise-grade AI governance.

The team

Abbas Godhrawala

Partner/Principal, EY India

Seasoned governance, risk and compliance professional; a technology enthusiast

Mumbai, IND

Abhijit Kumar

Partner, Digital Risk, EY India

Seasoned IT professional; cloud security enthusiast; experienced in cybersecurity, data privacy, and information security

Mumbai, IND

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.

Insights

Highlights

Services

Spotlight

Industries

Case studies

Careers

Spotlight

About us

Top news

Artificial Intelligence (AI) Risk and Governance

Key offerings

The team

Abbas Godhrawala

Abhijit Kumar

Insights

Highlights

Services

Spotlight

Industries

Case studies

Careers

Spotlight

About us

Top news

Artificial Intelligence (AI) Risk and Governance

Key offerings

Responsible AI Advisory: Importance of AI compliance for enterprises

Responsible AI risk assessment: How to assess risks in AI models

Model risk and controls assessment

AI governance consulting services and operations support

Responsible AI internalaudit: How to audit AImodels for bias and reliability

Frequently Asked Questions (FAQs)

The team

Abbas Godhrawala

Abhijit Kumar