Published on 02/12/2025

Third-Party Attestations: SOC2/ISO in AI Context

In the evolving landscape of artificial intelligence (AI) and machine learning (ML) within Good Practice (GxP) environments, understanding the significance of third-party attestations is paramount. In this comprehensive guide, we will explore the critical elements encompassing risk, AI ML model validation, intended use risk, data readiness curation, bias and fairness testing, and model verification and validation. This guide is structured to assist pharma professionals, clinical operations, regulatory affairs, and medical affairs professionals in navigating these complex domains.

Understanding Third-Party Attestations in AI Context

Third-party attestations, such as SOC 2 and ISO certifications, play a crucial role in establishing trust and accountability in AI applications. These certifications provide assurance to stakeholders about the system’s reliability, security, and compliance with regulatory expectations.

The SOC 2 framework is particularly relevant for software and technology service providers, focusing on five key Trust Services Criteria: Security, Availability, Processing Integrity, Confidentiality, and Privacy. On the other hand, ISO 27001 is a widely recognized standard for information security management systems (ISMS), ensuring that organizations manage their information assets securely.

When assessing AI systems in GxP environments, it’s essential to ensure that these third-party attestations address specific regulatory requirements such as 21 CFR Part 11, which governs electronic records and signatures. Understanding how these frameworks align with regulatory requirements is vital for compliance and effective governance.

Step 1: Identify Intended Use and Associated Risks

Establishing the intended use of AI systems is fundamental. This entails defining the specific applications and outcomes expected from the AI/ML model within a GxP context. Clarity in intended use will assist in risk assessment, enabling stakeholders to recognize and prioritize potential risks that might impact the quality, integrity, and compliance of the AI systems.

• Define intended use: Clearly articulate what the AI model is meant to achieve, including its functions and the context in which it will operate.
• Conduct a risk assessment: Utilize tools like FMEA (Failure Mode and Effects Analysis) to evaluate potential risks related to the proposed use of the AI model.
• Document findings: Maintain comprehensive documentation of the intended use and identified risks; this will serve as a foundation for subsequent validation activities.

Step 2: Assess Data Readiness and Curation

The success of an AI/ML model in achieving its intended use heavily depends on the quality of data fed into the system. Data readiness encompasses ensuring that the right type and quality of data are used for training, validating, and testing AI models.

• Source validation: Verify the credibility of data sources to ensure that the training datasets are representative and free from biases.
• Data cleaning and preprocessing: Implement methodologies for data cleaning to remove inaccuracies, duplicates, and erroneous entries while standardizing formats.
• Bias and fairness testing: Conduct analysis to assess whether the data reflects inherent biases, ensuring fairness in AI outputs. In this context, it is critical to utilize frameworks like Fairness/Aware to evaluate potential bias.

Incorporating regular checks for data readiness is crucial as AI models evolve, recognizing the dynamic nature of data sources and ensuring continued compliance with regulatory standards.

Step 3: Execution of Model Verification and Validation

Model verification and validation (V&V) are essential steps in the AI/ML lifecycle, providing confidence that the model performs as intended and meets the regulatory requirements. The execution of V&V should align with methodologies outlined in GAMP 5, focusing on risk-based approaches tailored to the specific context of AI applications.

• Model verification: This phase includes assessing whether the model meets the specified design requirements. It involves simulating real-world conditions to evaluate model behavior and performance metrics.
• Model validation: Validation demonstrates that the model achieves its intended use under specified operating conditions. It involves comprehensive testing and comparison against established benchmarks.
• Documentation: Maintain clear and detailed documentation of V&V activities, including test plans, results, and analyses. Proper audit trails are vital for compliance with Annex 11 and other relevant standards.

Step 4: Implement Explainability (XAI) Frameworks

Explainability plays a vital role, especially in regulated environments where stakeholders require transparency in AI decision-making processes. By implementing explainable AI (XAI) frameworks, organizations can gain deeper insights into the factors influencing AI outputs, thus aligning with the ethical and regulatory expectations.

• Employ model-agnostic methods: Use techniques like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) to explain model predictions irrespective of the underlying algorithms.
• Visualize decision processes: Generate visual representations of the decision-making process to facilitate understanding among stakeholders.
• Engage in stakeholder communication: Communicate the AI model’s capabilities and limitations to relevant stakeholders, fostering trust and transparency during audits or inspections.

Adopting XAI contributes to fostering confidence in AI systems, thereby enhancing compliance with AI governance and security protocols.

Step 5: Monitor for Drift and Re-Validation

Once an AI model is deployed, continuous monitoring for drift is necessary. Model drift occurs when the performance of the model diminishes over time due to changes in underlying data distributions or external factors. Ensuring models remain valid and compliant necessitates robust drift monitoring strategies and prompt re-validation processes.

• Establish performance metrics: Implement key performance indicators (KPIs) that are continually monitored to assess the ongoing effectiveness of the AI model.
• Schedule periodic reviews: Regularly review and analyze the AI model’s performance against baseline metrics to detect drift early. Utilize automated systems where feasible.
• Conduct re-validation: When drift is detected, initiate a re-validation process to confirm continued adherence to intended use, performing comprehensive testing using updated data sets.

The importance of drift monitoring and re-validation cannot be overstated; ongoing vigilance supports compliance with regulatory frameworks and the integrity of AI systems.

Step 6: Ensure Robust Documentation and Compliance with Audit Trails

In GxP environments, maintaining comprehensive documentation throughout the AI lifecycle is not only a best practice but a regulatory requirement. Proper documentation and audit trails serve multiple purposes, including compliance verification and process analysis.

• Document all processes: Maintain records of the development, validation, and deployment processes of the AI/ML model, including iterations and changes made over time.
• Utilize electronic systems: Leverage validated electronic systems for documentation, ensuring compliance with 21 CFR Part 11. These systems enable automated audit trails that document user actions and data manipulations.
• Prepare for inspections: Compiling concise documentation packages will facilitate inspections by regulatory bodies, including detailed descriptions of validation results, risk assessments, and compliance checks.

Establishing robust documentation practices not only aids in regulatory compliance but also fosters confidence among stakeholders regarding the use of AI systems in GxP settings.

Conclusion

As AI technologies increasingly integrate into GxP frameworks, understanding the nuances of third-party attestations such as SOC 2 and ISO certifications becomes essential. By following the structured steps outlined in this guide—focusing on intended use and associated risks, data readiness curation, model V&V, explainability, drift monitoring, and maintaining robust documentation—pharmaceutical and clinical professionals can navigate the complexities of AI ML model validation effectively.

Establishing a comprehensive approach towards AI governance and security not only ensures regulatory compliance but also enhances the overall quality and reliability of AI technologies in the pharma industry. Continuous education and engagement with the latest regulatory developments, standards, and best practices are critical for professionals tasked with managing and overseeing the integration of AI in GxP analytics.