SOP-1003-01-AI_Configuration-Management
Title: Configuration Management
Effective Date: (Fill in)
Supersedes: None (New SOP)
Version: 1.0
Approved By: (AI-IRB, CTO, etc.)
Date of Approval: (Fill in)
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1. Objective
This SOP establishes a consistent, controlled, and repeatable method for configuring, updating, and managing development, QA, staging, and production environments for AI-related systems. It clarifies responsibilities, processes, and quality checks to ensure changes are systematically planned, tested, documented, and reviewed, including where AI-IRB considerations apply.
2. Scope
This SOP applies to all configuration changes (software, hardware, environment parameters, etc.) within the AI Systems Development Life Cycle (AI-SDLC). It includes management of AI model versions, data pipelines, HPC infrastructure, cloud or on-prem deployments, and any other environment or component whose change could affect system performance, functionality, or compliance.
It also covers the integration of AI-IRB reviews where changes pose potential ethical/compliance impact or risk to end-users.
3. Definitions
- Configuration Management: The process of identifying, documenting, and controlling changes to the system’s software, hardware, or environment.
- AI-IRB (AI Institutional Review Board): A regulatory or compliance body ensuring that AI changes meet ethical, safety, and data governance requirements.
- Environments: Development, QA, staging, FOA (First Office Application), and production environments used throughout the AI-SDLC.
- Configuration Item (CI): Any component—model, dataset, service, library, or environment setting—that must be under version control and subject to change management.
- Deployment: The act of pushing a validated or tested configuration to an environment.
- Change Request (CR): A formal proposal to modify a configuration item or environment.
- Authorized AI Agent: An AI system or autonomous agent granted specific governance privileges, approved by the AI-IRB.
- Version Control System (VCS): Tools/systems (e.g., Git, ClearCase) used to track and manage changes to code, data, or environment scripts.
4. Roles and Responsibilities
| Role | Responsibilities |
|---|---|
| Configuration Manager | Maintains the configuration management plan, tracks all changes, ensures CR documentation is complete, and updates the CI repository. |
| AI Development Lead | Proposes model/data changes, ensures changes follow coding standards, and coordinates with the AI-IRB Liaison for high-risk modifications. |
| AI-IRB Liaison | Determines if a proposed change triggers an AI-IRB review, coordinates the review process, communicates acceptance conditions. |
| QA Representative | Reviews, tests, and validates changes before they are moved beyond QA environment. |
| Operations Team | Implements changes in staging/production, maintains environment integrity, ensures correct rollback plan, and monitors post-deployment. |
| Project Manager | Oversees scheduling, resources, risk management, and ensures synergy with all project deliverables; escalates complex CRs if needed. |
| Product Owner | Approves significant changes, signs off on user impact statements, ensures alignment with business objectives. |
5. Metrics
- Change Lead Time
- Measures days/hours from a Change Request submission to final deployment.
- Number of Emergency Changes
- Tracks unplanned or urgent changes, which may indicate inadequate planning or repeated issues.
- AI-IRB Triggered Changes
- Number of changes requiring formal AI-IRB review for ethical or safety concerns.
- Rollback Frequency
- Measures how often changes are reverted, indicating potential issues with QA or testing processes.
6. Procedure Activities
All configuration management activities incorporate recursive self-improvement subroutines and Exochain Peer Reviews to track configuration lineages under AI-IRB governance.
6.1 Configuration Management Planning
- Initiation
- The Project Manager coordinates with the Configuration Manager to create or refine the configuration management plan at the start of each AI-SDLC release.
- The plan must list all Configuration Items (CIs), including AI models, data pipelines, environment variables, HPC resource allocations, container images, etc.
- Define Roles and Scope
- Assign each CI to an owner (e.g., AI Development Lead for model code, Operations for environment infrastructure).
- Identify the approvals needed, including AI-IRB involvement for model changes, dataset changes, or other high-risk modifications.
- Version Control Integration
- Confirm that all changes to code, environment scripts, data schemas, or HPC resource definitions are committed to an approved VCS (e.g., Git).
- Create relevant branches or tags to isolate changes from stable production code.
6.2 Change Request (CR) Process
- CR Creation
- Any user (Development, Ops, QA, etc.) identifies a need for a configuration or environment change and documents it in a CR form.
- The CR includes scope, risk, justification, rollback plan, and potential AI-IRB triggers.
- CR Triage
- The Configuration Manager reviews the CR and assigns a priority (Critical, High, Medium, Low).
- If flagged for AI-IRB, the AI-IRB Liaison checks risk (ethical, data privacy, algorithmic fairness, etc.).
- The CR is either approved for progression or returned for more info.
- CR Analysis
- For major changes, a mini impact analysis is done (time, cost, potential risk).
- Product Owner approval required if user experience, budget, or timeline is significantly impacted.
- Project Manager updates project plans accordingly.
- CR Approval
- The Configuration Manager obtains sign-off from relevant parties (AI Development Lead, AI-IRB Liaison, QA, Product Owner).
- AI-IRB review is mandatory if flagged as high-risk.
- Approved CR moves to implementation scheduling.
6.3 Implementation and Documentation
- Environment Preparation
- Operations Team readies environment (Dev, QA, FOA/Beta, Staging, Production) for the new or updated CI.
- If HPC or specialized environment changes are required, cross-check resource usage and system constraints.
- Change Execution
- Developer or Ops staff implements the approved CR in the environment.
- Code or environment scripts updated in the VCS.
- The Configuration Manager logs the change and updates the CI repository.
- Review/Testing
- QA Representative performs or oversees validation tests (integration, load, regression).
- If any critical bug or environment conflict is detected, code is fixed or environment reconfigured, then retested.
- Deployment
- Post-approval from QA, changes move to the next environment (e.g., from QA to staging, or staging to production).
- Operations Team ensures rollback plan is available if issues emerge.
- The Configuration Manager updates official records to reflect the final, deployed configuration version.
6.4 Post-Deployment Activities
- Monitoring
- Operations Team monitors system performance, usage, and logs.
- If anomalies appear, an urgent CR or rollback might be initiated.
- Lessons Learned
- If the change had significant complexity or triggered issues, a brief retrospective is done.
- Document any improvement points in the configuration management plan.
- AI-IRB Follow-Up
- For changes that required AI-IRB involvement, confirm that final deployed version aligns with the board’s guidance.
- If any compliance deviation is discovered, escalate to the AI-IRB Liaison.
7. Post Implementation Review
- The Project Manager or Configuration Manager schedules a session with all relevant stakeholders.
- Evaluate how effectively the configuration process was followed, any unexpected impacts, time to complete, and system stability.
- Summarize key points in the final project documentation or a dedicated “lessons learned” log.
8. References
- SOP-1000-01-AI: Program/Project Management
- SOP-1005-01-AI: Release Planning
- SOP-1040-01-AI: Requirements Definition
- SOP-1101-01-AI: Training and Documentation
- AI-IRB Governance Charter (if applicable)
9. Revision History
| Version | Date | Author | Changes |
|---|---|---|---|
| 1.0 | (Fill in) | (Fill in) | Initial AI-SDLC SOP Release |
10. Approvals
| Role | Name | Signature | Date |
|---|---|---|---|
| SOP Owner | (Fill in) | ||
| AI-IRB Liaison | (Fill in) | ||
| CTO or Delegate | (Fill in) | ||
| Operations Manager | (Fill in) | ||
| QA Lead | (Fill in) |
End of SOP
@startuml
title SOP-1003-01-AI: Capacity Management Process
participant “Project Manager” as PM
participant “Capacity Manager” as CM
participant “Operations Team” as Ops
participant “AI-IRB Liaison” as IRB
participant “QA” as QA
participant “Developer” as Dev
PM -> CM: Request initial capacity assessment
CM -> Ops: Gather system usage metrics
Ops -> CM: Provide metrics and resource usage data
alt Additional AI-IRB concern?
CM -> IRB: Submit capacity plan for ethical/data compliance review
IRB -> CM: Provide recommendations and constraints
else No AI-IRB concern
note over CM: Proceed without IRB constraints
end
CM -> QA: Present draft capacity plan for QA input
QA -> CM: Validate performance test coverage
QA -> Dev: Confirm test environment readiness
Dev -> QA: Provide any needed environment updates
CM -> PM: Submit finalized capacity plan
PM -> CM: Approve plan and incorporate into project schedule
CM -> Ops: Implement capacity changes\n(e.g., HPC scaling, memory, CPU)
Ops -> CM: Report successful implementation
CM -> PM: Confirm changes in progress logs
PM -> QA: Request final verification
QA -> Ops: Verify system stability under new capacity
Ops -> QA: Provide monitoring data
alt Issues found?
QA -> CM: Notify capacity shortfalls or errors
CM -> Ops: Coordinate adjustments
Ops -> QA: Re-verify changes
QA -> PM: Confirm resolution
else No issues
note over QA,PM: Capacity deployment successful
end
@enduml
Short textual explanation: This sequence diagram shows how capacity management is initiated by the Project Manager and carried out by the Capacity Manager with input from Operations and QA. If AI-IRB review is triggered, the IRB Liaison is consulted to ensure compliance. Once validated, changes are implemented in the environment by Operations. QA then verifies the system’s stability, and if no further issues arise, the process concludes successfully.