AI governance is the foundation of responsible AI usage. It’s a framework of policies, practices and guidelines that shape how organisations build, implement and oversee AI systems. Effective AI governance balances advancements with risk management, guiding teams to meet regulatory standards whilst promoting accountability and transparency in AI-driven decisions.
With a solid governance structure in place, your organisation can better protect against cyber threats, avoid potential ethical pitfalls and maintain stakeholder confidence. A governance framework doesn’t limit innovation – strengthens it by aligning AI development with strategic goals.
In this post you’ll find:
- What Is an AI Governance Framework?
- Who Is Responsible for AI Governance?
- Top AI Risks for Businesses
- Current AI Governance Standards
- AI Compliance and Regulations
- Steps to Build Your AI Governance Framework
- Best Practices for Effective AI Governance
- Bringing Your Framework to Life
Laying the Foundation: What Is an AI Governance Framework?
A robust AI governance framework does more than outline technical guidelines. It defines the organisation’s approach to critical issues like data privacy, algorithmic fairness and security. It provides a foundation for managing the complexities of AI, such as bias mitigation, data protection and ethical decision-making.
Who Is Responsible for AI Governance?
Governance isn’t the responsibility of just one department or role. It requires collaboration across the business. Chief Information Security Officers (CISOs), Compliance Officers, Risk Managers and CTOs, play a central role in defining the AI governance framework and setting strategic priorities. These leaders must work closely with data scientists, IT teams, legal advisors and other stakeholders to ensure the framework is practical, effective and aligned with regulatory requirements.
The best approach is a cross-functional approach – where each department understands its role in upholding the framework. This shared responsibility creates a culture of accountability, making AI governance more than just a compliance obligation – it becomes an integral part of daily operations.
Top AI Security Risks for Businesses
AI can have many benefits to your organisation’s productivity. However, deploying AI potentially raises specific considerations, namely:
- Data Privacy Issues:
- Systems collecting personal data without consent can lead to legal risks and profiling.
- Poor anonymisation can allow re-identification, exposing sensitive information.
- Data shared with external AI providers may be misused or resold if transparency is lacking.
- Intellectual Property Infringement:
- AI models scraping internet data may use copyrighted content without permission, risking IP violations.
- The rapid development of AI makes it difficult to secure proprietary algorithms or data, increasing the risk of infringement.
- Determining ownership of AI-generated content is often unclear, complicating copyright claims.
- Cyber Security Threats:
- AI systems often require access to sensitive, centralised data, creating the potential for unauthorised breaches if not properly secured.
- AI data, especially in machine learning, can be vulnerable to attacks like data poisoning, where malicious actors corrupt training data to undermine model reliability.
- Hackers may attempt to steal AI models or exploit weaknesses using adversarial attacks – manipulating model outputs to cause harm.
Current AI Governance Standards
When developing an AI governance program, it’s essential to determine the standards that best fit your organisation. There are two published frameworks to consider. ISO/IEC 42001 focuses on standardized governance, whilst NIST AI RMF emphasises flexible risk management and trustworthy AI practices.
- ISO/IEC 42001 AI Management System
- This globally recognised standard provides a systematic approach to AI management, similar to ISO 27001 standards in information security. ISO/IEC 42001 guides organisations in designing, implementing and maintaining AI systems that meet security, transparency and ethical standards.
- NIST AI Risk Management Framework
- Developed by the U.S. National Institute of Standards and Technology (NIST), this framework is a valuable resource for managing AI risks. It emphasises assessing, managing, and mitigating AI-related risks, focusing on ethics, transparency, and accountability. Adopting this framework helps organisations establish a risk-aware approach to AI that supports regulatory compliance and promotes trust.
Both standards provide a foundation for organisations to craft a governance framework that reflects industry best practices and ensures responsible, secure and compliant AI usage.
| Feature | ISO/IEC 42001 | NIST AI RMF |
| Publishing Body | ISO (International Organization for Standardization) | NIST (National Institute of Standards and Technology) |
| Purpose | Risk-based approach to secure AI lifecycle management | Helps organisations manage AI-related risks |
| Audience | Broad, multi-industry, international | US-focused; open to various sectors |
| Focus | AI system lifecycle management | Risk management for trustworthy AI |
| Structure | Structured policies, roles and processes | Four functions: Map, Measure, Manage and Govern |
| Certification | Designed for certification | Voluntary, non-certifiable |
| Key Areas | Ethics, data governance, risk control | Privacy, security, transparency |
| Applicability | Internationally applicable | Primarily U.S. but gaining international interest |
AI Compliance and Regulations
As AI technology rapidly evolves, so too does the regulatory landscape. Organisations must ensure that their AI practices align with both global and local regulations to mitigate legal risks and protect their reputation.
As AI technology advances, governments and international bodies are responding to evolving regulatory frameworks to address the complexities and risks associated with AI. Global trends in AI regulation reflect an increasing recognition of the need to balance innovation with responsibility. Here are some key developments shaping the global AI regulatory landscape:
- EU AI Regulation:
The European Union is leading with its AI Act, which classifies AI systems by risk level, with stricter requirements for high-risk sectors like healthcare and finance. It focuses on transparency, accountability, and fairness – aiming to mitigate biases and privacy concerns. The Act is set to impact both EU businesses and global companies engaging with the EU market. - U.S. AI Regulation:
In the U.S. AI regulation is more fragmented, but efforts like the Algorithmic Accountability Act and guidelines from the FTC focus on transparency and protecting consumers from biased or discriminatory AI systems. The NIST AI Risk Management Framework also guides AI reliability and risk management. - China’s AI Regulation:
China’s regulatory approach balances innovation with control, as seen in the New Generation Artificial Intelligence Development Plan and AI Ethics Guidelines. China is also strengthening data privacy laws like the PIPL, aligning with global privacy concerns. - Global Standards and Collaboration:
Global bodies like the OECD and ISO are developing shared frameworks for AI ethics, privacy and security. The UNESCO AI Ethics Recommendations emphasise fairness and human rights – guiding global AI governance. - AI and Data Privacy:
Data privacy regulations, such as the GDPR in the EU, LGPD in Brazil, and the CCPA in California, are defining how AI handles personal data. These regulations are pushing AI developers to adopt privacy-conscious practices. - Ethical and Responsible AI:
A growing trend across regions is the focus on ethical AI, addressing issues like bias, discrimination and harmful uses of AI. Governments continue to call for businesses to implement responsible AI practices to ensure societal benefits.
Steps to Build Your AI Governance Framework
Building a comprehensive AI governance framework requires a methodical approach. By following a structured process from ideation to execution, you can create a framework that is both effective and adaptable. Here’s how to get started:
Ideation Phase: Research and Evaluate AI Compliance Requirements
Before building your framework, you must first understand the AI compliance landscape relevant to your industry and organisation. This includes:
- Identifying Legal and Regulatory Requirements: Research the regulatory standards for AI in your jurisdiction, such as the EU AI Act, UK AI Regulation, GDPR, and any specific sector regulations (e.g., healthcare or finance).
- Assessing Your Risks: Evaluate potential risks related to AI systems, such as privacy breaches, security vulnerabilities and algorithmic bias. This will guide the design of your framework to address these concerns effectively.
Planning and Design: Establish Objectives, Key Metrics and Stakeholder Buy-in
Now that you’ve identified the necessary compliance requirements, risks and goals, it’s time to design your governance framework:
- Determine the Framework: Start by defining your organisation’s AI goals (like compliance, ethics or risk management) and assess industry-specific risks. Compare standards to choose the one suited to your current and planned AI complexity. Sophisticated systems may require more robust governance like ISO 42001 or NIST AI RMF. For less complicated systems you may prefer a custom solution.
We suggest engaging stakeholders to get an understanding of what flexibility will be needed for future growth when selecting your framework.
- Determine Key Performance Metrics: Identify measurable results that will help track the success of your AI governance efforts. These could include compliance levels, risk reduction, transparency in AI decision-making or customer trust.
- Secure Stakeholder Buy-in: Support from senior leadership is vital, along with compliance teams and other key stakeholders. Communicate the value in managing risk, enhancing security and promoting innovation.
Execution and Implementation: Create Policies and Procedures
With a clear plan in place, it’s time to put it into action. This involves creating the policies, procedures and practices.
Policy Development: Draft clear policies for managing AI systems within your business, including guidelines on data privacy, algorithmic transparency and security. Ensure policies are scalable for expanding AI use cases and systems. These policies should reflect the compliance requirements and ethical considerations identified earlier.
- Procedures for Risk Management: Establish risk management protocols to assess, monitor and mitigate AI-related risks. This should include regular risk assessments, audits and ongoing monitoring of AI systems. Set up tools for tracking performance and risk, with regular reporting.
- Integrate with Existing Governance Structures: Ensure the AI governance framework is aligned with broader organisational governance practices. This may include integrating with IT security, data protection and corporate governance processes.
Training and Culture Building: Emphasise Responsible AI Practices
A framework is only as strong as the people who implement it. It’s crucial to:
- Cross-functional support: Build teams from various departments (IT, legal, compliance, data science, etc.) to help sustain AI governance processes.
- Training: Provide employee training on principles, including compliance requirements, security practices and ethical considerations. Regularly update this training to reflect changes in regulations and best practices.
- Foster a Responsible AI Culture: Build a culture that prioritises ethical AI use and transparency across all levels of the organisation. Encourage teams to think critically about the potential impacts of AI technologies and how they can be used responsibly.
Continuous Improvement: Review and Refine
The essence of the AI Management Plan is to continually improve it. It’s key that your organisation continually improves the suitability, adequacy and effectiveness of its AI Management System.
- Adapt to Emerging Regulations and Standards: Monitor trends and update governance practices accordingly.
- Regular Conduct Audits: Ensure compliance and identify areas for improvement through consistent auditing.
Best Practices for Effective AI Governance
AI governance best practices go beyond basic compliance, creating a comprehensive system to effectively monitor and manage AI applications. Below are just a few that Risk Crew recommend.
- Integration with Existing Policies & Training
- Determine which existing policies overlap (e.g., data privacy, security, ethics) with AI governance. Update policies (e.g., data privacy) to include AI-specific practices.
- Ensure AI issue reporting aligns with existing reporting procedures.
- Include AI governance in all training (e.g., employee onboarding, information security and data protection).
- Regular Compliance Audits and Risk Assessments
- Define Clear Objectives and Scope: Set specific audit goals and boundaries, focusing on key models, datasets, or applications.
- Ensure Multidisciplinary Collaboration: Include your cross-functional team and involve key stakeholders.
- Focus on Data Quality: Validate data sources, check for bias and ensure data integrity.
- Enhance Transparency and Explainability: Keep clear, transparent documentation detailing the AI system’s design, inputs, outputs, use case and other essential aspects.
- Evaluate Fairness and Bias: Measure fairness metrics and test for equitable outcomes across demographics.
- Conduct Robustness and Security Testing: Test against adversarial attacks and simulate extreme conditions.
- Monitor Regulatory Compliance: Ensure alignment with relevant laws and ethical guidelines.
- Establish Continuous Monitoring: Implement tracking tools to detect model drift and report findings regularly.
- Document Findings and Decisions: Keep records of procedures, results and corrective actions.
- Plan for Continuous Improvement: Include feedback loops to collect insights from users, to help improve the audit process and the AI system.
- External System Analysis of AI
- Assess the security practices of vendors and partners involved with AI components, ensuring model and data integrity.
- Use penetration testing and threat intelligence feeds to identify emerging AI risks and vulnerabilities.
- Implement an AI-specific vendor risk framework with secure supply chain practices and regular penetration tests, integrating AI threat intelligence into SOC processes for ongoing protection.
Bringing Your Framework to Life
An effective framework should be living and evolving alongside the fast-paced advances in AI technology. Embracing continuous improvement is essential to keep your governance model flexible and resilient. Regular updates, audits and a willingness to adapt will help ensure your framework remains aligned with emerging regulations and best practices – making it more robust against new challenges.
By embracing a comprehensive AI governance approach, your organisation not only mitigates risks but also fuels innovation with confidence and responsibility. Drive forward with a commitment to responsible AI, and let governance be the foundation that empowers your success in this transformative era.