Is Minimization the Best Approach to AI Governance?

As organizations integrate artificial intelligence (AI) into their business operations, they need clear ways to manage the risks associated with its development and use. Effective governance provides oversight, but complex procedures can consume resources and slow implementation. Minimization offers a potential way to make governance more proportionate by limiting unnecessary data use while preserving accountability and safeguards.

Why AI Governance Matters

AI governance establishes the policies, responsibilities and oversight that guide how organizations develop, deploy and use AI systems. It helps organizations identify risks, assign decision-making authority and establish processes for monitoring performance and addressing problems.

Organizations must consider several risks when adopting AI, including privacy breaches, security vulnerabilities, biased outcomes, inaccurate outputs and limited transparency. Without appropriate controls, AI-generated decisions can affect customers, employees and other stakeholders in ways that are difficult to correct. Governance also helps organizations meet applicable legal requirements and align AI use with business objectives.

The International Association of Privacy Professionals reported that 77% of surveyed organizations were working on AI governance in 2025. The finding indicates substantial organizational attention to the subject, although working on governance does not necessarily mean an organization has completed or fully implemented its framework.

Clear responsibilities and ongoing monitoring help organizations identify emerging issues, investigate unexpected outcomes and adjust controls as systems change. Effective governance can also strengthen stakeholder trust by demonstrating that an organization considers the implications of AI use and remains accountable for its decisions.

Challenges in AI Governance

Although governance helps organizations manage AI risks, establishing and maintaining effective oversight can be difficult. Defining the scope of oversight requires organizations to determine which AI systems, activities and dependencies need governance. 

To establish a clear scope for AI governance, organizations can refer to ISO/IEC 42001, which provides requirements for establishing and maintaining an AI management system. Compliance can improve model performance, allowing for a faster, safer AI. It also helps organizations determine which parts of their operations fall within their AI management system. These standards take into account geographic coverage, connections to other systems and the scope of their AI activities. 

Other key challenges include:

• Complexity of AI systems: AI applications may rely on interconnected models, datasets, third-party tools and automated decisions. These dependencies can make it difficult to trace outputs, identify vulnerabilities and determine where controls are necessary.
• Data privacy and security: Organizations must protect sensitive information throughout data collection, model training, deployment and ongoing use. 
• Bias and limited transparency: Unrepresentative training data can contribute to unfair outcomes, while opaque model behavior can make decisions difficult to explain, assess or challenge.
• Evolving regulations and standards: Organizations operating across jurisdictions may face different or changing legal requirements. Governance processes must accommodate relevant obligations without creating unnecessary duplication.
• Resource and operational constraints: Specialist skills, documentation, reviews and continuous monitoring require time and funding. 
• Balancing oversight and innovation: Excessive approval requirements can delay projects, while weak controls may leave significant risks unaddressed.

These challenges highlight the value of reducing unnecessary complexity. However, minimizing governance must also include establishing controls that protect people and information and ensure organizational accountability.

For example, the New York State Comptroller found that New York City agencies took inconsistent approaches to AI oversight, with some lacking policies to assess bias and accuracy. The findings highlight how reducing complexity requires clear, consistent governance standards to maintain accountability. 

What Minimization Means in AI Governance

Minimization in AI governance involves reducing unnecessary data use and administrative overhead while retaining the measures needed to manage risks. It has two main dimensions, which include data minimization and governance minimization. Both aim to make AI practices more proportionate to their purpose and potential impact.

Data Minimization

Data minimization limits the collection, retention, processing and sharing of information to what an AI system needs for a defined purpose. Organizations can apply the principle by restricting access to sensitive information and using anonymized or synthetic data where appropriate.

Reducing unnecessary data can limit privacy exposure and reduce the amount of information that organizations need to protect. However, data minimization requires careful judgment. Removing relevant information can weaken model accuracy, reduce representation of different groups or make it harder to identify biased outcomes. Organizations must determine which data is necessary to achieve the intended purpose and support reliable, fair results.

Governance Minimization

Governance minimization focuses on streamlining oversight processes. Organizations can consolidate overlapping reviews and reuse assessments where appropriate. They can also clarify decision-making authority and remove approval layers that do not add meaningful risk protection.

These changes help reduce administrative costs and delays while clarifying teams’ responsibilities. However, governance minimization should not eliminate essential documentation, independent review, human oversight or mandatory controls. The objective is to make oversight more efficient while maintaining accountability.

Minimization Strategies for AI Governance

A minimization approach can make AI governance more efficient when organizations match data practices and oversight requirements to the risks of each use case. Several measures can reduce unnecessary complexity while preserving accountability, reliable performance and appropriate safeguards.

Adopt a Risk-Based Approach

Organizations can classify AI systems according to their intended use, potential impact and data sensitivity. The National Institute of Standards and Technology’s AI Risk Management Framework provides a structure for managing AI risks through four functions that include Govern, Map, Measure and Manage. Organizations can use these functions to identify risks, assess their significance and determine appropriate responses.

Define Clear Data Requirements

Each AI system should have a defined purpose before data collection begins. Organizations can then identify the necessary data categories, remove irrelevant fields, restrict access and set appropriate retention limits. Testing should confirm that reduced datasets continue to support accurate and fair outcomes.

Optimize Models and Computing Resources

Model optimization, efficient algorithms and energy-efficient hardware can help reduce unnecessary computing demands. Companies can explore federated learning, which reduces the need to transfer large amounts of information. These practices can lower computing costs and energy consumption while supporting data privacy. 

Simplify Governance Workflows

Organizations can consolidate overlapping reviews, reuse assessments that cover the same risks, clarify responsibilities and establish approval pathways by risk level. Standardized processes help teams handle routine applications efficiently while directing more complex cases to the appropriate specialists.

Preserve Essential Safeguards

A minimization approach must retain privacy and security protections, bias assessments, appropriate human oversight, audit trails and incident response procedures. Organizations should also confirm that streamlined processes continue to meet applicable legal and regulatory obligations.

Monitor Performance and Reassess Controls

Governance requirements may change as an AI system develops, reaches new users or supports additional tasks. Organizations should monitor accuracy, fairness and privacy risks after deployment. They should also revisit data requirements and oversight procedures as conditions change.

Making Minimization Work for AI Governance

Minimization is most effective as part of a risk-based governance strategy. However, the appropriate level of minimization will vary with an AI system’s purpose, complexity and potential impact. By balancing efficiency with appropriate safeguards, accountability and continuous evaluation, organizations can simplify AI governance while maintaining responsible and reliable AI use.