SOP-LIST-01-AI_AI-IRB-Governed-AI-SDLC
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.
Summary
This integrated AI-SDLC set of SOPs ensures a rigorous end-to-end framework guided by the AI-IRB to manage risk, compliance, ethics, and operational excellence. Each SOP addresses a particular aspect of AI solution design, development, governance, and retirement. By adhering to these procedures, organizations can mitigate ethical, compliance, and technical risks while consistently delivering robust, fair, and transparent AI systems.
@startuml
title AI-IRB Governed AI-SDLC Sequence
actor “Senior Management” as SM
actor “AI-IRB” as IRB
actor “Project Manager” as PM
actor “Product Dev” as PD
actor “Quality Assurance” as QA
actor “Operations” as OP
actor “Technical Support” as TS
SM -> PM: Propose new AI project idea
PM -> IRB: Submit high-level proposal for ethical review
alt IRBApproves
IRB -> PM: Approval with conditions
else IRBDenies
IRB -> PM: Request changes or rejects
return
end
PM -> PD: Provide project scope/cost/schedule\n(Including IRB requirements)
PD -> QA: Share preliminary functional\n& system requirements
QA -> OP: Provide input on environment needs
OP -> TS: Collaborate on training and docs plan
PM -> PD: Refine resource assignment and schedule
PD -> IRB: Request partial re-check if scope changes
alt IRBApprovesAdditionalScope
IRB -> PD: Additional scope accepted
else IRBRequiresFurtherChanges
IRB -> PD: Must revise approach
return
end
PD -> QA: Deliver code increments for smoke/integration tests
QA -> PD: Integration test results,\nLog issues
PD -> PD: Fix code issues, rebuild
loop Iteration
PD -> QA: Provide updated code
QA -> PD: Re-test & accept
end
PM -> IRB: Request gate sign-off for major readiness
IRB -> PM: Ethical readiness sign-off
PM -> TS: Prepare user training materials
TS -> PD: Confirm final product doc
PD -> QA: Provide final user-facing doc for QA check
QA -> PD: Approves documentation
PD -> OP: Coordinate final environment checks
PM -> IRB: Request final pre-deployment review
alt IRBFinalApproval
IRB -> PM: Deployment clearance
else IRBRejectForRevisions
IRB -> PM: Must re-address concerns
return
end
PM -> OP: Initiate Deployment
OP -> QA: Final verification in staging
QA -> OP: Confirms staging test pass
OP -> OP: Release code to production
OP -> PD: Notify Dev of production push
TS -> TS: Begin user training classes
PD -> IRB: Confirm production rollout success
note right
Post-Deployment:
- Monitor system performance & model drift
- IRB re-check if major incidents or new features
end note
alt System or model drift found
QA -> PD: Found significant drift
PD -> IRB: Request re-validation
IRB -> PD: Re-approval after review
end
alt Retirement
PD -> IRB: Request EOL approval
IRB -> PD: Provide final acceptance
PD -> OP: Decommission model
end
@enduml