2.10 Regulatory Compliance and Adaptive Governance
Introduction
Artificial intelligence (AI) is transforming how organizations operate, make decisions, and serve society. As AI becomes more advanced and widespread, it brings not only new opportunities but also complex regulatory challenges. Regulatory compliance and adaptive governance are essential for ensuring that AI systems are developed, deployed, and managed in ways that are ethical, responsible, and aligned with evolving laws and standards. Adaptive governance refers to flexible, dynamic frameworks that can respond to rapid changes in technology, risks, and societal expectations, allowing organizations and governments to maintain effective oversight and accountability (OECD, 2023; MetricStream, 2025).
Technical or Conceptual Background
Regulatory compliance means following the laws, regulations, and guidelines that govern the development and use of AI. These include data protection rules, requirements for fairness and transparency, safety standards, and sector-specific laws. As AI regulations multiply and evolve across the world, organizations face the challenge of keeping up with diverse and sometimes conflicting requirements (BytePlus, 2025; MetricStream, 2025).
Adaptive governance provides a solution by introducing frameworks that are flexible and can be updated as regulations or risks change. Such frameworks typically involve interdisciplinary oversight committees, modular policies (so only parts of the framework need updating when rules change), human-in-the-loop mechanisms to ensure people remain involved in critical decisions, automated monitoring for compliance, and transparent reporting (HKGAI, 2025; Fractal, 2025). This approach allows organizations to manage AI risks more effectively and to demonstrate accountability to regulators, users, and the public.
Problems Being Solved or Best Practice Being Applied
This sub-point addresses the challenges described in Sub-Point 1.10: Regulatory Complexity and Compliance Burden. As AI regulations become more numerous and sophisticated, organizations often struggle to keep up with changing and sometimes conflicting requirements in different countries or sectors. Adaptive governance and robust compliance practices help organizations navigate this complexity, reduce the risks of non-compliance, and foster trust among stakeholders (MetricStream, 2025; BytePlus, 2025).
Best practices include establishing interdisciplinary governance committees, conducting regular risk assessments, using automated compliance monitoring tools, and maintaining human oversight throughout the AI lifecycle. These practices help organizations stay compliant, improve decision-making, and build trust with customers, regulators, and the public (MetricStream, 2025; ResponsibleAI, 2024).
Role of Government and Regulatory Authorities
Governments play a pivotal role in setting the legal frameworks and regulatory standards for AI. They enact laws such as the EU AI Act, the General Data Protection Regulation (GDPR), and national AI strategies that establish requirements for transparency, fairness, safety, and accountability (European Parliament, 2016; BytePlus, 2025). Regulatory authorities provide guidance, conduct audits, and enforce compliance through penalties and corrective actions (MetricStream, 2025).
For example, the European Union’s AI Act categorizes AI systems by risk levels and imposes strict requirements on high-risk applications, including mandatory impact assessments, transparency obligations, and human oversight. In the United States, agencies like the National Institute of Standards and Technology (NIST) have developed detailed AI risk management frameworks that focus on transparency, accountability, and bias mitigation (BytePlus, 2025; NIST, 2023).
Governments also promote adaptive governance by funding research, supporting regulatory sandboxes (where new AI solutions can be tested in a controlled environment), and facilitating collaboration among stakeholders. The UK government’s AI Regulation White Paper, for instance, outlines a principles-based, pro-innovation approach, supported by sectoral regulators and cross-regulatory forums to ensure flexible and effective oversight (UK Government, 2024).
International cooperation is increasingly important as AI risks and regulations cross borders. Organizations like the OECD, UNESCO, and ITU work to harmonize standards, share best practices, and build capacity globally, helping to reduce regulatory fragmentation and enable responsible AI adoption worldwide (OECD, 2023; UNESCO, 2021).
Role of Organizations and Businesses
Organizations must implement adaptive governance models that integrate compliance into every stage of AI development and operation. This involves forming interdisciplinary governance committees that include representatives from legal, technical, business, and ethical backgrounds (ResponsibleAI, 2024; Fractal, 2025). These committees oversee risk assessments, policy updates, and compliance monitoring.
Businesses should develop modular policies that can be updated as regulations change, use automated tools for compliance monitoring, and ensure transparent reporting to both internal and external stakeholders. Regular training and awareness programs are essential to equip employees with up-to-date knowledge of regulatory requirements and ethical AI practices (MetricStream, 2025; ResponsibleAI, 2024).
Organizations should also conduct regular risk assessments to identify areas where AI might cause harm, and maintain human oversight to catch errors or unintended consequences before they escalate. By adopting adaptive governance, organizations can reduce compliance risks, improve decision-making, and build trust with customers, regulators, and the public (MetricStream, 2025).
Role of Vendors and Third Parties
Vendors provide AI tools and services that support regulatory compliance and adaptive governance. They offer solutions for automated risk detection, compliance reporting, and audit trail generation (Certa, 2024; TrustCloud, 2025). Vendors are expected to design their products in ways that make it easier for organizations to comply with evolving regulations, such as by including features for transparency, explainability, and security.
Third-party auditors and consultants assist organizations in evaluating compliance, identifying gaps, and implementing best practices. Vendors also collaborate with clients to ensure their products meet regulatory standards and support transparency and accountability (Certa, 2024; TrustCloud, 2025).
Vendors should also be proactive in updating their products as regulations change, and in providing clear documentation and support to help clients meet their compliance obligations.
Role of Employees and Internal Teams
Employees participate in governance by following compliance policies, reporting issues, and engaging in training programs. Internal teams, such as legal, compliance, IT, and data science, conduct risk assessments, monitor AI system performance, and ensure that human oversight is maintained (MetricStream, 2025; ResponsibleAI, 2024).
Cross-functional collaboration among these teams is vital to adapt governance practices to changing regulatory landscapes and organizational needs. Employees also play a key role in fostering a culture of ethical AI use and continuous improvement.
Role of Industry Groups and Professional Bodies
Industry groups develop standards, certifications, and best practices that guide adaptive governance and compliance. They facilitate knowledge sharing, provide training, and advocate for effective regulation (IEEE, 2022; IAPP, 2024).
These bodies also engage with policymakers to shape AI laws and promote public awareness of compliance requirements. By setting benchmarks and promoting ethical conduct, industry groups help build public trust and encourage widespread adoption of responsible AI practices (OECD, 2023; IAPP, 2024).
Role of International and Multilateral Organizations
International organizations promote harmonized AI governance frameworks, support capacity building, and facilitate global cooperation. They publish guidelines and host forums to address emerging regulatory challenges and share best practices (OECD, 2023; UNESCO, 2021).
These organizations also help countries develop their own regulatory frameworks, provide technical assistance, and encourage the adoption of global standards for AI governance and compliance.
Role of Consumers and Users
Consumers influence compliance by demanding transparency, accountability, and ethical AI use. They exercise data rights such as access, correction, and deletion, and provide feedback that helps organizations improve governance practices (IAPP, 2024; ENISA, 2021).
By choosing products and services from organizations that demonstrate strong compliance and adaptive governance, consumers drive market demand for responsible AI.
Role of Members of the Public
The public shapes AI governance through advocacy, education, and participation in policymaking. Public opinion encourages governments and organizations to prioritize compliance and ethical AI development (EFF, 2023; OECD, 2023).
Civil society groups raise awareness about AI risks, push for stronger regulations, and participate in consultations to ensure that new laws reflect societal values and protect vulnerable groups.
Role of Artificial Intelligence Itself
AI can automate compliance monitoring, risk detection, and reporting, making adaptive governance more efficient and responsive. For example, AI can scan for regulatory changes, flag potential compliance issues, and generate audit reports (MetricStream, 2025; Veale, 2022).
However, human oversight remains essential to ensure that AI-driven compliance tools are used ethically and effectively, and to address issues that automated systems may miss.
Role of Bad Actors
Bad actors, including hackers, cybercriminals, and malicious insiders, exploit governance gaps and compliance weaknesses. They may attempt to bypass controls, manipulate data, or exploit vulnerabilities in AI systems (ISACA, 2025; Symantec, 2024).
Robust security measures, continuous monitoring, and collaboration among stakeholders are necessary to protect AI systems and maintain compliance. Organizations must stay vigilant and adapt their governance frameworks to address emerging threats.
Glossary
Term |
Meaning and Example Sentence |
|---|---|
Regulatory Compliance |
Following laws and regulations related to AI. Example: "Regulatory compliance ensures AI systems meet legal standards." |
Adaptive Governance |
Flexible frameworks that adjust to changing AI risks and regulations. Example: "Adaptive governance helps organizations stay compliant as laws evolve." |
Risk Assessment |
Evaluating potential risks in AI systems. Example: "Risk assessment identifies areas where AI might cause harm." |
Human Oversight |
Involving people in monitoring AI decisions. Example: "Human oversight helps catch AI errors before they cause problems." |
Transparency |
Being open about how AI systems work. Example: "Transparency builds trust in AI decisions." |
Accountability |
Being responsible for AI outcomes. Example: "Accountability means organizations must answer for AI mistakes." |
Questions
What is regulatory compliance, and why is it important for AI systems?
How does adaptive governance help organizations manage AI risks?
What roles do governments and regulatory authorities play in AI compliance?
How can organizations implement adaptive governance effectively?
Why is human oversight essential in AI governance?
Answer Key
Suggested Answer: Regulatory compliance means following laws and regulations related to AI to ensure systems are safe, fair, and legal (OECD, 2023; MetricStream, 2025).
Suggested Answer: Adaptive governance provides flexible frameworks that can adjust to changing AI risks and regulations, helping organizations stay compliant and manage risks effectively (HKGAI, 2025; MetricStream, 2025).
Suggested Answer: Governments establish legal frameworks, provide guidance, enforce compliance, and promote international cooperation to support AI governance (European Parliament, 2016; UK Government, 2024).
Suggested Answer: Organizations can implement adaptive governance by forming interdisciplinary committees, conducting risk assessments, maintaining transparency, and using automated compliance tools (MetricStream, 2025; ResponsibleAI, 2024).
Suggested Answer: Human oversight is essential to monitor AI decisions, ensure ethical use, and address issues that automated systems may miss (Veale, 2022; MetricStream, 2025).
References
European Parliament. (2016). Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2016/679/oj
EFF. (2023). Digital rights and privacy advocacy. Electronic Frontier Foundation. https://www.eff.org
ENISA. (2021). Privacy and security by design in AI systems. European Union Agency for Cybersecurity. https://www.enisa.europa.eu/publications/privacy-and-security-by-design-in-ai-systems
HKGAI. (2025). Hong Kong Generative Artificial Intelligence Technical and Application Guideline. Government of the Hong Kong Special Administrative Region. https://www.digitalpolicy.gov.hk/en/our_work/data_governance/policies_standards/ethical_ai_framework/doc/HK_Generative_AI_Technical_and_Application_Guideline_en.pdf
IAPP. (2024). Privacy and security by design: Best practices for AI. International Association of Privacy Professionals. https://iapp.org/resources/article/privacy-and-security-by-design-best-practices-for-ai/
ISACA. (2025). Six steps for third-party AI risk management. ISACA Now Blog. https://www.isaca.org/resources/news-and-trends/isaca-now-blog/2025/six-steps-for-third-party-ai-risk-management
MetricStream. (2025). AI in GRC: Trends, Opportunities and Challenges for 2025. https://www.metricstream.com/blog/ai-in-grc-trends-opportunities-challenges-2025.html
OECD. (2023). OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data. https://www.oecd.org/digital/privacy/
ResponsibleAI. (2024). Navigating Organizational AI Governance. https://responsible.ai/navigating-organizational-ai-governance/
Symantec. (2024). Threat intelligence and cybersecurity trends. https://www.symantec.com/security-center
UK Government. (2024). Regulators’ strategic approaches to AI. https://www.gov.uk/government/publications/regulators-strategic-approaches-to-ai/regulators-strategic-approaches-to-ai
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000380455
Veale, M. (2022). AI and privacy: The role of automation in compliance. Harvard Journal of Law & Technology, 35(1), 1–73. https://jolt.law.harvard.edu/assets/articlePDFs/v35/35HarvJLTech1.pdf
BytePlus. (2025). Future of AI regulations: What to expect in 2025. https://www.byteplus.com/en/topic/381863
Certa. (2024). AI-powered vendor management: A game-changer for procurement teams. https://www.certa.ai/blogs/ai-powered-vendor-management-a-game-changer-for-procurement-teams
TrustCloud. (2025). How AI is revolutionizing third-party risk assessments. https://www.trustcloud.ai/ai/how-ai-is-revolutionizing-third-party-risk-assessments/
Fractal. (2025). Adaptive AI governance and cognitive compliance. https://fractal.ai/article/adaptive-governance-and-cognitive-compliance-for-resilient-ai
NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). https://www.nist.gov/itl/ai-risk-management-framework
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