As we enter 2025, AI governance and risk management will continue to evolve in meaningful ways. The relatively recent introduction of standards like ISO/IEC 42001:2023 marks an emerging shift towards more structured and responsible AI practices driven by legal, regulatory, and compliance factors. Here are the upcoming trends and key controls that companies should consider implementing:
Upcoming Trends in AI Governance and Risk Management
- AI Integration Becoming Standard: Organizations will begin to move beyond questioning whether to implement AI in Governance, Risk, and Compliance (GRC) processes and focus on optimizing its use. We expect more variety in its applications for real-time risk monitoring, automated control testing, and predictive analytics. Additionally, governance practices will enable organizations to better understand and manage their AI use cases, infrastructure, and resources.
- Scalable Risk Management: Organizations will prioritize creating more adaptable risk management processes that can quickly respond to changing circumstances while still maintaining oversight. As AI becomes a bigger part of daily work and compliance tasks, it will be critical to have solid frameworks to manage risks as well as integrate with Enterprise Risk Management (ERM) processes to improve these risk management capabilities.
- Expansion of AI-Specific Validation Frameworks: Traditional model validation frameworks will evolve to address additional evaluation metrics and incorporate AI-specific risks such as bias, interpretability, and robustness. This will include the development of more rigorous and comprehensive testing methodologies as traditional benchmarks become inadequate for assessing advanced AI models. Additionally, there is growing emphasis on the integration of AI validation processes into earlier stages of the development lifecycle. Furthermore, the integration of AI and machine learning into validation processes is gaining traction, enabling the handling of large datasets, predictive modeling, and early identification of potential risks.
- AI-Driven Model Risk Management Automation: AI used to validate other AI models will become standard practice, automating aspects of model validation and enhancing efficiency in model risk management. The implementation of Model Risk Management (MRM) style solutions will include capabilities like stress testing, risk scoring, and performance monitoring.
- Emphasis on AI Governance: Comprehensive AI governance frameworks will integrate regulatory compliance, ethical considerations, and operational oversight. We expect to start seeing centralized AI governance platforms that allow organizations to oversee the operational aspects of their AI systems throughout their lifecycle including use cases, infrastructure, models, risk impact, and compliance status.
Key Controls to Implement
ISO/IEC 42001:2023 outlines 9 objectives and 38 controls that organizations must implement to ensure responsible AI practices. These controls cover several critical areas:
- AI Governance and Leadership Structure – Establish a dedicated AI governance committee and empower senior leadership to champion AI and data governance initiatives. This additionally includes implementing roles and responsibilities that address governance, security, and compliance with regulatory requirements.
- Data Privacy and Security – Implement data management and security systems to protect user privacy in an AI environment. This involves Ensuring transparency and quality of training data along with focusing on comprehensive data governance, encryption, and access control measures.
- Transparency and Explainability – Implement mechanisms to ensure AI systems’ decision-making processes are documented and communicated clearly to stakeholders. This promotes trust and understanding of AI operations.
- Ethical Accountability – Conduct ethical impact assessments, incorporate ethical principles into AI system design, and engage diverse stakeholders throughout the AI lifecycle. This will help ensure AI systems align with organizational, regulatory, and societal values.
- Reliability and Safety – Develop and maintain AI systems that demonstrate a high degree of safety and reliability across domains. This will involve continuous testing and validation processes.
- Education and Training – Invest in education teams about AI-related risks, emerging regulations, and effective assessment strategies. This will build an organizational capacity to manage and utilize AI systems responsibly.
Implementing ISO/IEC 42001 Controls
To get started and implement these controls, companies should:
- Define Clear Roles and Responsibilities
- Establish a dedicated AI governance committee led by senior executives.
- Appoint a Chief AI Officer (CAIO) or equivalent to oversee AI initiatives.
- Clearly define roles for AI system management, including ethics committees and technical experts.
- Establish Reporting Processes
- Implement a transparent mechanism for reporting AI-related concerns or anomalies.
- Develop incident response and remediation processes for AI-related issues.
- Create a method for frequent communication between stakeholders and developers
- Adopt AI-Specific Validation Frameworks
- Use frameworks designed to assess and monitor AI models across their lifecycle for the specific domains and use cases within your organization.
- Conduct fairness audits and bias assessments, particularly for automated decision-making processes.
- Integrate AI Governance
- Develop an AI governance framework that includes regulatory compliance and ethical considerations applicable to your AI system environment.
- Maintain an AI model inventory and manage the various stages of an AI model lifecycle to maintain close oversight of AI usage in the organization.
- Ensure Data Privacy and Security
- Begin to implement data management and security controls to protect users. This may involve expanding traditional security controls to encompass AI environments or solutions.
- Ensure compliance with data protection laws and AI-specific legislations (e.g., EU AI Act).
- Strive for Continuous Improvement
- Implement solutions for ongoing monitoring of AI performance.
- Invest in educating teams about AI-related risks, emerging regulations, and effective usage.
- Regularly review and update policies to adapt to the AI environment and acceptable usage of AI systems.
By focusing on these areas and implementing the necessary controls, organizations can ensure they are well-prepared for the AI-related changes that will occur in 2025.
Elevate is here to assist in setting your organization in implementing AI Governance and AI Risk Management Practices.