Published on 02/12/2025

Segregation of Duties in MLOps

The incorporation of Machine Learning (ML) within Good Practice (GxP) regulated environments has highlighted the importance of segregation of duties (SoD) to manage risks effectively. Establishing clear guidelines regarding AI/ML model validation, including considerations of intended use risk, data readiness curation, bias and fairness testing, and model verification and validation, is essential for compliance with regulatory standards such as FDA, EMA, MHRA, and PIC/S.

Understanding the Concept of Segregation of Duties in MLOps

Segregation of Duties (SoD) is a fundamental internal control that aims to prevent errors and fraud in organizational processes by dividing responsibilities among different individuals or teams. For Machine Learning Operations (MLOps), this means assigning distinct roles to individuals involved in AI/ML model development, validation, deployment, and monitoring. By implementing SoD principles, organizations can mitigate risks associated with data manipulation, biased outcomes, and inadequate model performance.

Within the context of MLOps, the segregation of duties must encompass several critical phases:

• Model Development
• Data Preparation and Curation
• Model Verification and Validation
• Deployment and Monitoring
• Audit Trails and Documentation

Each phase may necessitate distinct competencies and perspectives to ensure thorough oversight and compliance with applicable regulatory requirements.

Step 1: Establishing Roles and Responsibilities

The first step in implementing SoD within your MLOps framework is to clearly define the roles and responsibilities involved in the model lifecycle. This involves identifying key personnel and structuring teams in a way that fosters accountability and checks and balances. Common roles include:

• Data Scientists: Responsible for developing machine learning algorithms and models.
• Data Engineers: Focused on data acquisition, preparation, and ensuring quality.
• Quality Assurance (QA) Professionals: Tasked with validating models, ensuring they meet specified requirements.
• Compliance Officers: Oversee regulatory adherence and internal controls.
• Business Owners: Define the intended use of AI/ML models and their alignment with business goals.

To maintain SoD, it is crucial that no single individual has control over multiple conflicting functions, such as model development and approval. The division of duties promotes objectivity in assessing risks associated with AI/ML solutions.

Step 2: Data Readiness & Curation

Data readiness and curation are crucial for effective AI/ML model validation. The quality of data directly influences model performance, and any bias or inaccuracies can lead to flawed outcomes. Organizations must implement clear policies on data sourcing, cleaning, and preparation. Key considerations include:

• Ensuring data is representative of the intended population to mitigate bias.
• Implementing data provenance tracking to document the lineage and transformation of datasets.
• Conducting exploratory data analysis to identify attributes, distributions, and potential issues.

Given that AI/ML technologies rely heavily on data, personnel involved in data handling must operate independently from those developing models to avoid conflicts of interest. This form of segregation can ensure a more rigorous assessment of data quality and compromise detection.

Step 3: Intended Use Risk Assessment

Before deploying an AI/ML model, it is imperative to conduct an intended use risk assessment. This assessment evaluates the potential implications of the model’s applications, ensuring compliance with internal policies and external regulatory expectations such as 21 CFR Part 11 or Annex 11. Important aspects of risk assessment include:

• Identifying scenarios where model outputs may lead to significant impact, whether positive or negative.
• Evaluating the consequences of errors, including patient safety and regulatory ramifications.
• Determining risk mitigation strategies, such as implementing controls to minimize the probability of adverse outcomes.

Engaging cross-functional teams during this assessment can heighten awareness of both technical and operational risks, ensuring a comprehensive approach to intended use validation.

Step 4: Model Verification and Validation

Model verification and validation (V&V) is a critical component of AI/ML lifecycle management, ensuring that models operate as intended and comply with the necessary regulatory standards. This involves two distinct processes:

• Verification: Confirming that the model has been developed according to specified requirements, including algorithm correctness, computational efficiency, and implementation quality.
• Validation: Demonstrating that the model meets its intended purpose in real-world scenarios. This phase includes criteria such as accuracy, reliability, and robustness across various situations.

While verification can be handled by the model development team, validation requires an independent review by QA professionals. Such segregation ensures objective analysis of model performance against predetermined benchmarks and enhances stakeholder confidence in the model’s readiness.

Step 5: Bias and Fairness Testing

Bias and fairness in AI/ML systems have garnered significant attention, underscoring the need for effective testing methodologies to ensure equitable outcomes across diverse populations. Organizations should implement rigorous bias detection frameworks, which may include:

• Analyzing model predictions against demographic variables to identify discrepancies.
• Utilizing fairness metrics to quantify potential bias issues systematically.
• Incorporating feedback loops that allow continuous assessment and adjustments to mitigate biased outcomes.

As bias and fairness testing is often multifaceted, it is preferable that teams dedicated to outcome testing are separate from those involved in model design and implementation. This approach promotes an unbiased evaluation and ensures that models align with ethical standards and regulatory compliance.

Step 6: Drift Monitoring & Re-Validation

Once models are deployed, continual monitoring for performance drift is vital to sustain accuracy and relevance over time. Drift refers to changes in the statistical properties of the data used by the model, which can potentially lead to degraded performance. Ongoing drift monitoring should involve:

• Establishing baseline performance metrics at deployment.
• Regularly reassessing model accuracy against current datasets.
• Implementing conditions for trigger-based re-validation if significant deviation is detected.

Having a dedicated team monitor drift should be separate from the personnel managing the model. This further reinforces independent oversight and ensures that models remain compliant and operational in line with regulatory expectations.

Step 7: Documentation & Audit Trails

Comprehensive documentation is an essential aspect of GxP compliance, particularly in the context of AI/ML systems. Every stage of the model lifecycle needs to be meticulously documented, and audit trails should be maintained to substantiate actions taken. This includes:

• Recording decisions made throughout the model development, verification, and validation stages.
• Documenting all data sources used in training and validation, along with any pre-processing steps.
• Maintaining complete revision histories for models to track changes over time.

Documentation is integral not only for compliance purposes but also for maintaining organizational transparency. It allows for effective knowledge transfer and adherence to established SoD principles.

Step 8: Governance and Security in AI/ML

As AI/ML technologies progress, organizations must prioritize governance and security to mitigate risks associated with data breaches and misuse. Establishing comprehensive governance frameworks involves:

• Implementing policies for data access, ensuring that only authorized personnel can interact with sensitive information.
• Regularly training employees on security best practices and potential vulnerabilities.
• Conducting security audits to identify gaps and implementing corrective measures promptly.

Strong security measures help protect both the data and the models themselves, ensuring compliance with regulations such as those outlined by the WHO and the ethical considerations intrinsic to AI governance.

Conclusion

The segregation of duties within MLOps is a crucial aspect of ensuring compliance with regulatory standards and managing associated risks effectively. By adhering to a step-by-step approach from defining roles to implementing comprehensive monitoring systems, organizations can enhance their AI/ML model validation processes while fostering transparency and accountability. As the landscape of GxP evolves, ongoing education, updated practices, and rigorous adherence to segregation of duties will be paramount for success.