Why traditional GRC frameworks are inadequate for AI risk management

The use of AI has become routine for many organizations. AI tools are now widely used in hiring, lending, healthcare decisions, customer service, fraud detection, and many other critical business functions. To mitigate AI risks, many organizations rely on their existing AI GRC framework, which was originally developed for IT governance, cybersecurity, and regulatory compliance. However, these frameworks are not designed to manage the unique and evolving risks of AI, and as a result, many organizations fail to properly address AI risk using this approach.

Traditional GRC frameworks were designed for systems that behave in stable and predictable ways. AI systems are very different. They learn from data, evolve over time, and sometimes produce outcomes that even their creators cannot fully explain. In this blog, we explain why standard GRC fails for AI, explore major traditional GRC limitations, and show how frameworks like ISO 42001, NIST AI RMF, and the EU AI Act help organizations upgrade their approach to AI risk management.

What traditional GRC frameworks are built to manage?

Traditional governance, risk, and compliance frameworks were created to manage risks related to:

• IT infrastructure and applications
• Cybersecurity threats
• Financial controls and audits
• Legal and Regulatory compliance

These frameworks assume that systems:

• Change only when humans update them
• Follow clear and documented rules
• Produce predictable and repeatable outcomes
• Can be audited using logs and reports

Whereas traditional GRC works well for database, network, and enterprise software systems; AI systems will exhibit a different kind of behavior, thereby introducing novel risk elements outside the scope of current GRC thinking.

Why are AI systems fundamentally different?

AI systems learn to interpret their environment through patterns in data rather than adhere to hard-coded rules. As such, AI systems can change their method of behavior without changes to the source code that originally guided their design. As such, AI systems introduce a level of uncertainty into the equation that is not adequately accounted for by traditional GRC frameworks.

Key differences include:

• Continuous learning and Adaptation
• Heavy dependence on data quality
• Complex models that are hard to explain
• Automated decision-making at scale

These differences are the main reason why standard GRC fails for AI and why organizations need an AI-specific risk management framework.

Key limitations of traditional GRC when applied to AI

1. Static risk assessments vs. dynamic AI risks

Traditional GRC relies on periodic risk assessments, such as yearly audits or quarterly reviews. AI risks do not follow this timeline.

AI models can degrade over time due to:

• Changes in user behavior
• Shifts in market conditions
• New types of data inputs

AI systems can make inaccurate or biased decisions due to a phenomenon known as ‘model drift’. Continuous Risk Monitoring is not supported through traditional governance-risk-compliance frameworks. Therefore, they are not suitable for managing risk concerning modern AI Technologies.

2. Poor visibility into training and testing data

Data is a critical component of all Artificial Intelligence (AI) systems. All errors in the AI’s data will reflect as an error in the system output. Most GRC (Governance, Risk and Compliance) functions that deal with data place an emphasis on treating the data as a property for information security purposes and protect it with rights and restrictions on who has access to the data and how to store the data.

However, AI risk management requires much more, including:

• Understanding data sources
• Evaluating data representativeness
• Tracking labeling and preprocessing steps
• Monitoring data changes over time

These areas are mostly ignored in legacy GRC models, highlighting major traditional GRC limitations.

3. No controls for bias and fairness

Traditional GRC frameworks do not measure whether automated decisions are fair or discriminatory. AI systems can unintentionally reinforce social biases present in training data.

For example:

• Hiring tools may favor certain demographics
• Credit scoring systems may disadvantage specific groups

Without bias testing and fairness controls, organizations expose themselves to legal, reputational, and ethical risks. This is another reason why standard GRC fails for AI.

4. Lack of explainability and transparency

Many AI models, especially deep learning systems, cannot easily explain how they reach decisions. Traditional GRC focuses on documentation and audit trails but does not require decision explainability.

This becomes a serious issue when:

• Regulators demand justification for decisions
• Customers challenge automated outcomes
• Internal teams need to understand AI behavior

Explainability is now central to emerging AI regulations, yet it is missing from most legacy GRC frameworks.

5. Weak human oversight mechanisms

Traditional GRC assumes humans are always the final decision-makers. In AI systems, decisions are often automated or only lightly reviewed by humans.

Existing frameworks do not clearly define:

• When humans must review AI decisions
• How overrides should work
• Who is accountable for AI outcomes

This creates gaps in responsibility and increases operational risk.

6. Misalignment with emerging AI regulations

New global regulations focus specifically on AI risks. Traditional GRC frameworks were created long before these laws existed.

As emerging AI regulations like the EU AI Act take effect, organizations using only legacy GRC will struggle to:

• Classify AI systems by risk level
• Demonstrate Ongoing Compliance
• Prove proper governance and oversight

How modern AI frameworks address these gaps

To solve these problems, new frameworks focus specifically on AI risks. They are designed to complement existing GRC programs, not replace them.

ISO/IEC 42001: structured AI governance

ISO 42001 is the first international standard for AI management systems. It provides a structured way to govern AI across its lifecycle.

Key strengths include:

• Continuous AI risk identification and treatment
• Strong data governance and documentation requirements
• Defined roles and accountability for AI oversight
• Alignment with existing ISO standards

ISO 42001 makes upgrading GRC for AI risk more practical and scalable.

NIST AI Risk Management Framework (AI RMF)

The NIST AI RMF offers a flexible and practical approach to AI risk management.

It is built around four core functions:

• Govern: Define policies and accountability
• Map: Identify AI risks and impacts
• Measure: Assess and monitor risks continuously
• Manage: Mitigate risks through controls and oversight

This framework fits well into any AI GRC framework and addresses the shortcomings of traditional GRC.

EU AI Act: Legal expectations for AI systems

The EU AI Act introduces legally binding requirements for AI systems based on their risk level.

For high-risk AI systems, it requires:

• Formal risk management processes
• High quality and well-documented datasets
• Human oversight controls
• Continuous post-deployment monitoring

Traditional GRC frameworks alone cannot meet these obligations, making AI-focused extensions essential.

How to upgrade existing GRC for AI risk management?

Organizations do not need to discard their current GRC tools. Instead, they should extend them to handle AI-specific risks.

Key steps include:

• Adding AI risks to enterprise risk registers
• Monitoring model performance, bias, and drift continuously
• Implementing explainability and transparency controls
• Defining human-in-the-loop governance processes
• Mapping controls to ISO 42001, NIST AI RMF, and regulations

This approach allows organizations to modernize their GRC programs while protecting existing investments.

Conclusion

While traditional governance frameworks have served organizations well in managing risk across their business, IT, and regulatory/compliance domains, they do not provide sufficient coverage for the risk management requirements associated with the development and use of AI systems. AI systems have a unique risk profile derived from their learning, adaptive and data-centric characteristics, which do not fit within the confines of traditional governance models. The limitations imposed on traditional governance by these new areas of concern highlight why the use of conventional GRC is insufficient when it comes to mitigating the risks associated with AI. Organizations that adopt new standards such as ISO 42001, and NIST’s AI RMF, and that align themselves with the emerging regulatory frameworks such as that represented by the EU’s proposed AI Act, will establish a better and more sustainable future-proofed risk management framework for their development and use of AI systems. The intent here is not to eliminate governance as it pertains to AI, but rather to evolve what has historically been used into the appropriate governance framework that is AI-centric.

Are you still relying on legacy GRC models to manage AI risks? Now is the right time to act. Start upgrading GRC for AI risk with a modern AI GRC framework that supports transparency, compliance, and trust. ValueMentor helps organizations design and implement AI risk management programs aligned with ISO 42001, NIST AI RMF, and emerging AI regulations. Partner with ValueMentor to build an AI risk management approach that is ready for today’s challenges and tomorrow’s innovations.

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