Abstract
As artificial intelligence (AI) becomes more integrated into business operations, traditional governance, risk management, and compliance (GRC) models face challenges in addressing its unique ethical, legal, and technical issues. This paper examines how organizations can adapt their governance strategies to ensure the responsible use of AI. It analyzes key frameworks, including the NIST AI Risk Management Framework and the EU AI Act, and identifies gaps in oversight, transparency, and accountability.
To address these gaps, a Mini AI GRC Framework is proposed, which emphasizes five domains: governance, risk, compliance, transparency, and accountability. Each domain is mapped to practical controls and existing standards. A real-world example related to AI-driven hiring demonstrates how organizations can manage bias, conduct impact assessments, and establish incident response plans to maintain trust and compliance.
The findings indicate that effective AI governance requires not only adherence to regulations but also a continuous commitment to ethical design, collaboration with stakeholders, and adaptive risk management. This approach positions responsible AI as both a compliance issue and a business advantage.
Introduction
Artificial intelligence (AI) is no longer just a futuristic concept. It is rapidly becoming the decision-maker behind hiring processes, loan approvals, medical diagnoses, and even law enforcement strategies. While these applications promise efficiency and innovation, they also raise unsettling questions. What happens when an algorithm discriminates? Who is accountable when a machine’s decision leads to harm? These questions emphasize the growing tension between the speed of adoption of AI and the slower pace of governance and regulatory oversight.
Traditional frameworks for governance, risk management, and compliance (GRC), such as ISO 27001 or COSO ERM, were not designed to manage the unique risks of AI systems. Issues such as algorithmic bias, lack of transparency, and evolving regulatory standards stretch conventional compliance models beyond their limits. Biased training data can perpetuate discrimination, while opaque “black box” models make it difficult for organizations to explain or defend automated decisions. Regulators are beginning to respond, such as the NIST AI Risk Management Framework, which provides a structured approach to mapping, measuring, and managing AI risks, while the EU Artificial Intelligence Act aims to classify and regulate AI systems by risk level.
This article looks at how organizations can modify existing GRC models to better manage the challenges posed by AI. It discusses the limitations of current approaches, introduces new regulatory ideas such as the NIST AI Risk Management Framework and the EU AI Act, and suggests a simple mini-framework tailored to govern AI effectively. As businesses continue to adopt smart technologies, creating responsible and transparent governance strategies will be crucial, not only to comply with regulations but also to build trust and resilience for the future.
AI-Specific Risks in Governance, Risk, and Compliance
AI poses a variety of risks that go beyond what traditional GRC frameworks typically cover. Unlike standard technologies, AI systems are dynamic, meaning they learn from new data and can adapt to changes, sometimes behaving in ways that their creators cannot fully understand. This unpredictability complicates oversight and can lead to ethical, legal, and reputational issues.
One major concern is algorithmic bias. It is unrealistic to expect that bias can be eliminated from AI systems. Instead, companies need to adopt structured methods to identify, track, and correct any unfair outcomes. If unchecked, biased algorithms, especially in critical areas like hiring or lending, can reinforce existing inequalities and leave organizations open to lawsuits and damage to their reputations.
Another significant risk is the lack of transparency and explainability in many advanced AI systems, which often operate as “black boxes.” This makes it difficult to audit or justify their decisions, undermining compliance efforts and eroding public trust. This is particularly concerning when AI systems make life-altering decisions, such as denying medical treatment. Organizations must be able to explain those decisions clearly and understandably.
Regulatory uncertainty also complicates how businesses adopt AI. Policymakers worldwide are still figuring out how to balance the need for economic growth and innovation with the necessity for safety measures. This uncertainty can lead to practical issues. For example, organizations using AI for surveillance may ignore risks such as disproportionate impacts on marginalized communities. These realities show that static compliance checklists are insufficient and that AI demands ongoing monitoring and adaptable risk management strategies.
Finally, strategic and organizational risks play a crucial role. Board members must prioritize AI governance, ensuring it is integrated into the overall enterprise strategy rather than treated as just a technical issue. AI should be seen as a long-term societal challenge, not just a short-term compliance matter. Taken together, these factors indicate that AI risks are complex and multifaceted, covering areas like bias, lack of transparency, regulatory challenges, and governance shortcomings—all of which necessitate a reevaluation of traditional GRC frameworks.
Governance for AI
Strong governance is crucial for the responsible adoption of artificial intelligence. While traditional corporate governance has focused on accountability and oversight, AI presents unique challenges that require new frameworks to address its specific risks. Effective AI governance should not be limited to technical teams. It needs to involve people, processes, and technology across the organization to ensure ethical and transparent use.
One effective solution is to establish an AI Governance Board or a similar oversight committee. Board-level involvement is essential because AI decisions can significantly impact a company's reputation, financial success, and ethical standards. If leadership does not engage in AI governance, organizations may implement AI in ways that contradict their stated values. The board does not need to understand the technical details of every model but should set clear expectations, demand accountability, and ensure alignment with legal and ethical standards.
Another important aspect is the use of clear accountability frameworks. Defining roles for each stage of the AI lifecycle—data collection, model development, testing, deployment, and monitoring—can prevent governance issues caused by ambiguity. Tools like a RACI (Responsible, Accountable, Consulted, Informed) matrix help clarify these roles and ensure that compliance officers and technical experts know what is expected of them.
Transparency and documentation are also vital. Applying frameworks like the NIST AI Risk Management Framework to real-world systems requires organizations to document not just technical risks but also social impacts such as fairness and privacy. Tools like model cards and datasheets for datasets give stakeholders clear information about how models were trained, what data was used, and any limitations. This kind of transparency demonstrates a commitment to ethical AI design and helps manage potential societal risks before they escalate into regulatory issues.
Lastly, effective governance must be collaborative and adaptable. Simply having principles is not enough for AI oversight. Governance should aim to build relationships across different parts of the organization to account for AI’s unpredictable nature. In this way, AI governance becomes less about strict rules and more about fostering cooperation among compliance teams, engineers, executives, and external stakeholders.
Risk Management Adjustments
Traditional risk management practices need to adapt to the challenges posed by AI. Frameworks like ISO 27001 and COSO ERM are strong for managing information security, operational continuity, and financial risks, but they do not fully address AI-specific concerns. Risk management strategies must expand to include ongoing monitoring, ethical considerations, and the unpredictable behavior of AI systems.
Bias and fairness testing are crucial. Instead of trying to eliminate bias, organizations should focus on practical ways to mitigate it, acknowledging that data imperfections are inevitable. This includes regular fairness audits, using diverse training data, and implementing adversarial testing models that go beyond simple compliance checks. Without these measures, companies might meet current regulations but still face significant risks if public expectations change.
Model drift and continuous monitoring are also key concerns. AI models often perform well when first trained, but their performance can degrade as real-world conditions change. Organizations should implement ongoing monitoring to catch these shifts early, using checkpoints for retraining, tracking changes, and incorporating monitoring throughout the system development lifecycle (SDLC). Explainability dashboards and similar tools can help compliance officers and executives oversee AI performance.
Incident response and contingency planning must also be updated. Failures related to AI present not only operational risks but also systemic challenges. Businesses need crisis plans that account for the complexities of algorithmic decision-making. For example, if an autonomous system leads to harmful outcomes, companies should have protocols to roll back the system, notify affected users, and comply with regulators. Integrating AI-specific response strategies into disaster recovery and business continuity plans helps organizations recover without losing public trust.
Finally, adapting to changing regulations must be treated as a core part of risk management. Global rules are constantly evolving, creating uncertainty for businesses that operate across borders. Organizations should monitor legislative changes, maintain flexible frameworks, and recognize that ethical risks add complexity that rigid controls alone cannot address.
Compliance Challenges
Even with improved governance and risk management, enterprises must confront the shifting landscape of compliance. AI introduces novel legal and ethical dilemmas that existing regulatory frameworks were not designed to resolve. Regulations such as the GDPR and CCPA mainly focus on data privacy, but neither fully addresses how automated decision-making systems should be audited or held accountable. As a result, organizations are forced to interpret and adapt older laws to fit the realities of AI, often with inconsistent results.
Newer initiatives aim to fill these gaps. The EU AI Act introduces a risk-based approach, classifying AI systems from minimal to “unacceptable” risk. This pushes enterprises to consider not only whether systems function properly, but also whether they fall into categories that trigger stricter obligations such as transparency reporting, human oversight, or prohibition. In the U.S., the NIST AI Risk Management Framework offers a complementary tool for aligning compliance practices with AI risk assessment.
Yet compliance challenges extend beyond legal frameworks. Even well-designed regulations cannot anticipate every risk posed by increasingly autonomous AI systems. Compliance must be paired with ethical reflection. Organizations should treat AI oversight as a relational practice in which responsibilities are negotiated in real time across compliance teams, engineers, and stakeholders. This perspective highlights that compliance is dynamic and must evolve as AI systems and their impacts evolve.
Industry-specific requirements add another layer of complexity. In regulated domains like healthcare, finance, and cybersecurity, AI systems must align with both sector-specific regulations and broader ethical expectations. For instance, an AI tool used to analyze medical images must comply with privacy rules like HIPAA and also meet standards of clinical accountability and fairness. This “layering” of obligations makes it harder for enterprises to stay consistent across jurisdictions and industries.
Mini AI GRC Framework Proposal
Traditional GRC methods are useful, but they fall short when dealing with the unique challenges posed by AI. Organizations need a simpler, more targeted approach that combines GRC with AI-specific concerns. To support that, this article introduces a Mini AI GRC Framework built around five domains: governance, risk, compliance, transparency, and accountability.
| Domain | AI-Specific Risk | Suggested Control / Action | Reference Standard |
|---|---|---|---|
| Governance | Lack of oversight and unclear accountability | Establish an AI Governance Board and RACI matrices defining roles across the AI lifecycle. | NIST AI RMF (Govern function) |
| Risk | Algorithmic bias and fairness gaps | Conduct routine bias testing, fairness audits, and dataset documentation. | NIST AI RMF (Map / Measure) |
| Compliance | Privacy violations and regulatory misalignment | Perform Privacy Impact Assessments (PIAs / DPIAs) and monitor evolving regulatory obligations. | NIST SP 800-53; GDPR; CCPA |
| Transparency | Opaque “black box” models | Implement explainability tools, model cards, and datasheets for datasets. | NIST AI RMF; NIST CSF 2.0 |
| Accountability | Absence of response protocols for AI incidents | Develop AI-specific incident response playbooks integrated into DR / BCP strategies. | NIST SP 800-61 |
The framework is grounded in governance research that emphasizes board engagement, structured risk controls, fairness considerations, and the integration of ethical principles into compliance practices. By aligning with guidance such as the NIST AI RMF and related standards, it remains consistent with established best practices while staying flexible enough to adapt to evolving regulatory landscapes.
Example of Application: AI in Hiring
To illustrate how this framework can be put into practice, consider a company that uses an AI-driven tool to screen job applications.
Governance: The company sets up an AI Governance Board that includes representatives from Human Resources, compliance, and IT. A RACI matrix clarifies roles: HR is responsible for overseeing data and use cases, IT is accountable for monitoring the system, and the compliance and legal teams are consulted during risk assessments.
Risk Management: Regular audits are conducted to check for potential bias related to gender, race, or age. Findings are documented and reviewed quarterly to maintain fairness and correct issues promptly.
Compliance: Before deploying the AI tool, the organization performs a Privacy Impact Assessment to ensure compliance with applicable privacy laws and automated decision-making rules. Local employment laws are also reviewed.
Transparency: The company publishes a model card that explains how the hiring algorithm works, what data it was trained on, and its limitations. This supports both candidates and regulators by clarifying how automated decisions are made.
Accountability: An incident response protocol is developed for cases where the tool produces discriminatory results. It includes suspending automated decision-making, retraining the model, notifying affected applicants, and reporting outcomes to leadership.
By using the Mini AI GRC Framework in this context, the organization not only meets regulatory requirements but also demonstrates proactive governance and ethical responsibility. This builds trust with stakeholders and reduces long-term operational and reputational risk.
Conclusion
Artificial intelligence offers exciting possibilities but also presents significant challenges for how organizations manage governance, risk, and compliance. Traditional models struggle to keep up with issues like algorithmic bias, regulatory uncertainty, and organizational misalignment. Effective AI governance requires structured oversight, board-level engagement, and close collaboration between management, compliance officers, and technical experts.
The Mini AI GRC Framework provides a practical, risk-based approach aligned with standards like the NIST AI RMF. By focusing on governance, risk, compliance, transparency, and accountability, it gives organizations tools to address AI’s specific challenges while building trust with stakeholders. The hiring example shows how these ideas translate into concrete practices such as bias testing and AI-specific incident response.
Looking ahead, regulations such as the EU AI Act indicate that compliance demands will continue to evolve. Organizations must be ready to adjust their frameworks to stay aligned with both legal obligations and societal expectations. In that sense, AI governance is not a one-time project but an ongoing process that must adapt as technologies and risks change.
In summary, AI challenges organizations to rethink governance, risk, and compliance as dynamic and collaborative practices. By integrating insights from multiple disciplines and aligning with federal standards, organizations can create frameworks that not only meet regulatory requirements but also promote ethical responsibility and public trust.
Annotated Bibliography
Coeckelbergh, M. (2024). From principles to relationships: Redesigning ethics for AI’s alien cognition. AI & Society, 39(2), 345–356.
This source frames AI governance as a flexible, evolving process rather than a fixed set of rules. It underscores the importance of adaptability, collaboration, and communication across teams when managing AI risks and responsibilities.
Hendrycks, D. (2024). Introduction to AI safety, ethics, and society. arXiv.
Hendrycks’ work highlights potential long-term risks posed by AI to society. It supports the view that businesses must incorporate safety, ethics, and contingency planning into AI risk and compliance frameworks.
Institute of Internal Auditors. (2022). Cybersecurity frameworks: Virtual symposium presentation [PDF].
This resource emphasizes that traditional risk approaches like ISO and COSO are not sufficient for AI risks. It argues for enhanced strategies that include continual monitoring, ethical checks, and AI-specific controls.
Kulothungan, V., Mohan, P. R., & Gupta, D. (2025). AI regulation and capitalist growth: Balancing innovation, ethics, and global governance. BigDataSecurity.
The article discusses global regulatory challenges for AI and stresses the need for flexible systems that can adapt to evolving standards, such as those introduced by the EU AI Act.
National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0).
NIST’s AI RMF provides foundational guidance for the Mini AI GRC Framework, informing key domains like governance, risk, and transparency and supporting practical examples such as AI in hiring.
National Institute of Standards and Technology. (2020). Security and privacy controls for information systems and organizations (NIST SP 800-53, Rev. 5).
This document underpins recommended privacy and security practices, including impact assessments and alignment with laws such as GDPR and CCPA.
National Institute of Standards and Technology. (2012). Computer security incident handling guide (NIST SP 800-61, Rev. 2).
NIST SP 800-61 supports the development of AI-specific incident response plans that account for ethical concerns and user impact.
Sayles, J. (2024). Principles of AI governance and model risk management: Master the techniques for ethical and transparent AI systems. Apress.
Sayles’ work informs ideas around role clarity, lifecycle oversight, and transparency tooling, and reinforces the need to embed AI governance into overall organizational strategy.
Sharma, R. (2024). AI and the boardroom: Insights into governance, strategy, and the responsible adoption of AI. Apress.
Sharma emphasizes board-level engagement in AI strategy and governance, arguing that AI must be treated as a core business consideration rather than a side project.
Swaminathan, N., & Danks, D. (2024). Application of the NIST AI Risk Management Framework to surveillance technology. Journal of AI, Ethics, and Society.
This source illustrates how the NIST AI RMF can be applied to real-world use cases, particularly regarding fairness and transparency in surveillance, and argues against rigid checklist-only approaches.
Townson, S. (2023). Manage AI bias instead of trying to eliminate it. MIT Sloan Management Review, 64(3), 42–47.
Townson focuses on algorithmic bias and fairness testing, offering practical steps for dealing with imperfect data rather than promising to eliminate all bias. This perspective is central to AI risk management and compliance.
Artificial Intelligence Disclosure
In the research and writing process behind this article, AI tools were used to support organization, structure, and clarity. AI assisted with outlining sections, suggesting transitions, managing citations, and formatting references in APA style, while care was taken to preserve the author’s voice and critical thinking. Final responsibility for the analysis, arguments, and conclusions remains with the author.