Comprehensive SOP List: AI-IRB Governed AI-SDLC

SOP-1000-01-AI: AI-Integrated Program/Project Management

Purpose: Defines how program and project management activities integrate AI-IRB touchpoints, ensuring alignment with AI ethics, regulatory compliance, and stakeholder requirements.

Key Points:

Clarifies roles and responsibilities for AI-related tasks.

Describes program charter creation, milestone tracking, risk management.

Establishes processes for obtaining AI-IRB approvals at critical points.

SOP-1001-01-AI: Document Governance and AI-IRB Compliance

Purpose: Governs the creation, review, revision, and archiving of documents, ensuring AI-IRB compliance and alignment with regulatory requirements.

Key Points:

Document control procedures (versioning, approval matrix).

AI-IRB mandated sign-offs for changes.

Secure document repository and retention rules.

SOP-1002-01-AI: Capacity Management (AI-Integrated)

Purpose: Outlines methods to forecast, allocate, and manage compute and data capacity for AI solutions, factoring in ML model training and inference workloads.

Key Points:

Resource usage tracking for training/inference.

Monitoring of AI load to ensure system reliability.

Threshold-based triggers for additional resources.

SOP-1003-01-AI: Configuration Management

Purpose: Ensures consistent configuration of AI system components (models, data pipelines, supporting infrastructure).

Key Points:

Baseline tracking of model versions, datasets, dependencies.

Version control guidelines for code, AI model artifacts.

Procedures for controlled changes and rollbacks.

SOP-1004-01-AI: Procurement and Purchasing for AI-Enabled Systems

Purpose: Standardizes the acquisition process for AI hardware, software, external datasets, and consulting services.

Key Points:

AI-IRB screening for potential ethical or compliance issues in new tools.

Vendor due diligence for bias and data privacy compliance.

Ensures budget approvals align with project scope.

SOP-1005-01-AI: AI-Integrated Release Planning

Purpose: Integrates AI roadmaps and iteration cycles into the standard SDLC release planning.

Key Points:

AI feature backlog refinement, prioritization, and gating by AI-IRB.

Roadmap alignment with data readiness and capacity constraints.

Triggers for re-validation if new models are introduced.

SOP-1006-01-AI: AI-IRB Engagement and Ethical Review Procedure

Purpose: Provides the route for engaging with the AI-IRB to secure ethical clearances, especially for new or high-impact AI features.

Key Points:

Formal submission for ethical risk reviews.

Communication channels for clarifications and re-approvals.

Records of IRB decisions and any mandated conditions.

SOP-1007-01-AI: AI Asset Management

Purpose: Tracks and manages AI hardware, software licenses, pretrained model assets, and data assets across the organization.

Key Points:

Lifecycle tracking from acquisition to retirement.

Warranty, licensing compliance, and usage monitoring.

Asset modifications or reassignments require version logs.

SOP-1008-01-AI: AI Incident and Escalation Management

Purpose: Details how to handle real-time AI production incidents, anomalies, or emergent model misbehavior, including escalation to AI-IRB if ethics-related.

Key Points:

Tiered incident severity definitions for AI anomalies.

Communication guidelines and immediate fix or rollback.

Post-incident root cause analysis to incorporate lessons learned.

SOP-1009-01-AI: AI Model Drift and Re-Validation Procedure

Purpose: Ensures periodic checks for model drift (performance degradation or domain shifts) and triggers re-validation cycles.

Key Points:

Metrics for drift detection.

Retraining or model retirement guidelines.

AI-IRB involvement if drift implicates fairness or ethics.

SOP-1010-01-AI: AI-SDLC Site Monitoring and Incident Management

Purpose: Focuses on 24/7 site monitoring for AI-related production issues, bridging with general operations incident management.

Key Points:

Real-time site monitoring for model performance metrics.

Coordinated escalation if system stability is threatened.

Maintains ongoing compliance with SLA targets.

SOP-1011-01-AI: AI Feature Decommissioning and Model Retirement

Purpose: Lays out a structured approach to retire an AI feature or fully remove a model from production.

Key Points:

Checklist for shutting down active inferences.

Handling dependent functionalities or user workflows.

Archival of associated data and code artifacts.

SOP-1012-01-AI: AI Model Explainability and Interpretability Procedure

Purpose: Establishes mandatory steps to ensure each AI model includes interpretability features and relevant user/engineer documentation.

Key Points:

Setting up model explainers (e.g., SHAP, LIME).

Auditable logs explaining decisions.

IRB checks for transparency levels required.

SOP-1013-01-AI: AI Model Post-Production Monitoring and Ongoing Validation

Purpose: Mandates continuous monitoring of model KPIs and triggers re-validation if significant changes occur in performance or data distributions.

Key Points:

Metrics and thresholds for performance, bias, or drift.

Automated alerts to responsible teams.

Frequencies for scheduled health checks.

SOP-1014-01-AI: Regulatory & Ethical AI Compliance Verification

Purpose: Confirm compliance with relevant laws and internal policy for AI solutions (GDPR, CCPA, internal ethics charters, etc.).

Key Points:

Checklists for privacy laws, disclaimers, user consent.

AI-IRB involvement for expansions of scope or new data usage.

Audit trail for all compliance checks.

SOP-1015-01-AI: AI Knowledge Transfer and Handover Procedure

Purpose: Ensures structured knowledge transfer for new AI solutions from the development team to operational owners or support staff.

Key Points:

Documented training sessions, including final readouts.

Code tours, pipeline diagrams, environment replication steps.

Final handover acceptance.

SOP-1020-01-AI: AI Model Lifecycle Management

Purpose: Provides a meta-view of an AI model’s lifespan, from initial concept and prototyping to deployment, maintenance, and eventual retirement.

Key Points:

Stage gates aligned with AI-IRB reviews.

Criteria for scaling up from proof-of-concept to production.

End-of-life triggers and data final dispositions.

SOP-1030-01-AI: AI-Ad Hoc Reporting Procedure

Purpose: Governs how internal teams request quick-turnaround or one-time analysis from existing AI models or data sets.

Key Points:

Quick security/privacy checks for new requests.

IRB oversight if the request expands original data usage.

Prompt escalation or revision if scope creeps.

SOP-1040-01-AI: Requirements Definition

Purpose: Identifies how to capture functional and non-functional requirements for AI solutions, including data needs, acceptance criteria, and AI-IRB constraints.

Key Points:

AI-IRB gating for sensitive data usage or high-risk features.

Clear alignment of acceptance test cases.

Cross-functional reviews for risk, compliance, feasibility.

SOP-1050-01-AI: AI Security Administration and Governance

Purpose: Controls security measures around AI systems: data encryption, API access, key management, and vulnerability scanning for AI pipelines.

Key Points:

AI platform security, software supply chain management.

Periodic vulnerability scans on AI code and dependencies.

Zero-trust posture, especially for privileged AI model endpoints.

SOP-1051-01-AI: Security Administration and Oversight

Purpose: Provides an overarching approach to user account management, privileged account controls, and periodic reviews of access logs for the entire environment.

Key Points:

Role-based access control, especially for data scientists with production data.

Security posture reviews by AI-IRB if emergent risk.

Master accounts and restricted user IDs tracked.

SOP-1052-01-AI: AI Model Lifecycle Oversight and Governance

Purpose: AI-IRB overview ensuring all major steps in a model’s lifecycle comply with established ethical and regulatory frameworks.

Key Points:

Aligns with SOP-1020 but focuses on IRB gating.

Triage critical or sensitive updates for immediate IRB review.

Mandates final sign-off at each milestone.

SOP-1053-01-AI: Ethical Risk Assessment & Mitigation

Purpose: Mandates regular ethical risk assessment (diversity, bias, societal impact) for AI solutions and prescribes mitigation actions.

Key Points:

Periodic ethical risk reviews.

Mitigation plan sign-off by AI-IRB.

Documentation of residual risk acceptance.

SOP-1054-01-AI: AI-Regulated Project Approvals and Sponsorship

Purpose: Documents how AI-IRB obtains the necessary cross-functional approvals and sponsorship for regulated AI projects.

Key Points:

Funding and oversight checkpoints.

Sponsor sign-off from Senior Management.

IRB-structured gating across project phases.

SOP-1055-01-AI: Computer System Controls

Purpose: Ensures that all computing environments that host or serve AI models meet uniform control standards (SOX, HIPAA, ISO).

Key Points:

Automated logging, compliance requirements, access restrictions.

Physical and logical security for server rooms / cloud.

Periodic control audits and recertifications.

SOP-1060-01-AI: Service Level Agreement

Purpose: Stipulates minimum performance, availability, and support commitments for AI solutions.

Key Points:

Defines KPIs such as inference latency and uptime.

Penalties or escalation for repeated SLA breaches.

Review schedule for SLA adjustments.

SOP-1061-01-AI: Incident Tracking

Purpose: Comprehensive approach to log, categorize, and track incidents involving AI, from minor anomalies to critical outages.

Key Points:

Triage rules for severity.

Root cause analysis must consider model aspects.

Post-mortem that can trigger new IRB reviews if changes are required.

SOP-1100-01-AI: Documentation of Training

Purpose: Records job-related training for staff engaged in AI functions and compliance with policy or regulatory demands (AI-IRB included).

Key Points:

Documents training events and curricula.

Ensures staff have required AI knowledge, including bias/fairness.

Central repository of training logs for audits.

SOP-1101-01-AI: Training and Documentation

Purpose: Creates and maintains user instructions, training materials, knowledge base for newly delivered AI solutions.

Key Points:

Trainer selection from Technical Support or SMEs.

Product documentation readiness and acceptance by QA.

Post-training surveys and feedback loops.

SOP-1200-01-AI: Development

Purpose: Outlines coding, integration, unit test strategies specifically for AI components, referencing data pipelines and ML frameworks.

Key Points:

Emphasizes code reviews, test coverage for ML logic.

Aligns with environment config from SOP-1003.

Tools, branches, merges, and iteration cycles.

SOP-1210-01-AI: Quality Function

Purpose: Details test strategy, integration test plan, QA acceptance for both standard software and AI components (performance, bias, correctness).

Key Points:

System test includes functional, load, and regression tests.

Model performance acceptance criteria.

QA gate sign-off required prior to release.

SOP-1220-01-AI: Deployment

Purpose: Final rollout and push to production for AI solutions. Checks that AI-IRB’s final sign-off is present, and that relevant training is complete.

Key Points:

Transition from QA/staging to production.

Monitoring for immediate post-release anomalies.

Post-deployment review and lessons learned.

SOP-1300-01-AI: AI-IRB Governance & Oversight

Purpose: Defines the role, responsibilities, and authority of the AI-IRB to enforce ethical, regulatory, and operational checks.

Key Points:

Composition, decision-making process, meeting intervals.

Mandated reviews for high-impact or ethically sensitive AI projects.

Documentation of all IRB judgments or waivers.

SOP-1301-01-AI: AI Bias & Fairness Evaluation

Purpose: Mandates methods for detecting and mitigating bias within AI models, ensuring fairness across protected classes.

Key Points:

Tools and metrics for measuring bias.

IRB audits for critical classes (e.g., race, gender).

Remediation steps and retesting.

SOP-1302-01-AI: AI Explainability & Model Transparency

Purpose: Ensures that each AI model can be explained at an appropriate level to both internal stakeholders and external regulators/users.

Key Points:

Tracking of model interpretability methods.

Documentation or instrumentation for real-time interpretability.

IRB sign-off for permissible black-box levels if truly necessary.

SOP-1303-01-AI: AI Data Protection & Privacy

Purpose: Protects data used in AI systems from unauthorized access, ensuring compliance with relevant privacy laws (GDPR, HIPAA, etc.).

Key Points:

Pseudonymization or anonymization approach.

Secure data pipelines, encryption at rest/in transit.

IRB data usage approvals or rejections.

SOP-1304-01-AI: AI Validation & Monitoring

Purpose: Ongoing process to validate AI models’ correctness, reliability, and compliance after the initial deployment.

Key Points:

Monitoring plan for performance, unexpected behaviors.

Automated triggers for re-validation.

AI-IRB audit logs for significant anomalies.

SOP-1305-01-AI: AI Ethical Risk & Impact Assessment

Purpose: Formal assessment of the broader societal and ethical impacts of an AI initiative, ensuring that all relevant stakeholders and impacted parties are considered.

Key Points:

Comprehensive methodology for risk identification and rating.

Stakeholder consultation (including vulnerable populations).

Documentation of mitigations and sign-off by IRB.

SOP-1306-01-AI: AI End-of-Life & Sunset Process

Purpose: Provides a structured approach for decommissioning AI models that have outlived their useful or safe lifecycle.

Key Points:

AI-IRB verification that all obligations and user impacts are addressed.

Data and model archiving or destruction.

Post-sunset review of lessons learned.

SOP-2002-01: Control of Quality Records

Purpose: Governs management of all quality records and associated evidence for the entire AI-SDLC (including sign-offs, IRB documents, test logs).

Key Points:

Retention schedules, versioning, authorized destruction.

Cross-referencing for audits.

Accessibility and security for critical records.

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