Mind Matrix: Navigation
SOP-1011-01-AI_AI-Feature-Decommissioning-and-Model-Retirement
Title: AI Feature Decommissioning and Model Retirement
Effective Date: (Date of Approval)
Previous Version: None
Reason for Update: New SOP
Owner: Chief Technology Officer (CTO)
Signature / Date:
(Sign and date upon final approval)
1. Objective
The purpose of this Standard Operating Procedure (SOP) is to define and formalize the process for decommissioning AI features and retiring AI models used in the production environment, ensuring minimal disruption to users, compliance with ethical/AI-IRB guidelines, and proper knowledge transfer for potential future reference.
2. Scope
This SOP applies to all SDLC AI-based features and machine learning models that are fully or partially deployed in production and must be disabled, removed, or deprecated from the live environment. It covers:
- Trigger Conditions for AI feature or model retirement
- Stakeholder Notifications and AI-IRB involvement
- Data Preservation and archiving steps
- Post-Retirement Validation
- Documentation of the retirement for audit and regulatory compliance
This SOP does not cover the initial development or live operation of AI features; see the relevant SOPs (e.g., SOP-1003-01-AI, SOP-1010-01-AI) for those topics.
3. Applicable To
- AI Development Teams (responsible for building, updating, and maintaining AI models)
- Operations Department (responsible for environment management and final removal of model artifacts)
- Quality Assurance (QA) (responsible for verifying retirement steps and compliance)
- AI-IRB Liaison (if decommissioning has potential user or ethical impact)
- Product Management (for stakeholder communication and acceptance criteria sign-offs)
- Technical Support (for user/client communication and support queries)
4. Definitions
| Term | Definition |
|---|---|
| AI Feature | Any user-facing or internal software functionality powered by machine learning or advanced AI models. |
| AI Model | A trained machine learning model or neural network used within a feature. |
| Decommission/Retirement | The formal process of removing an AI model or feature from the production environment and associated workflows. |
| AI-IRB | A special ethics and compliance review board for AI-based systems. |
| Model Artifacts | Files, logs, metadata, scripts, and any other items necessary to re-instantiate or audit the AI model. |
| Authorized AI Agent | A validated AI system or subsystem identified within the Mind Matrix as having the authority to execute specific SDLC or operational tasks. |
5. Roles and Responsibilities
5.1 AI Dev Team
- Evaluate readiness for model retirement.
- Prepare relevant decommissioning scripts or instructions.
- Oversee final checks on AI model artifacts before removal.
5.2 Operations
- Execute environment changes for decommissioning.
- Archive or purge relevant infrastructure resources.
- Confirm no background processes or cron jobs remain active.
5.3 Quality Assurance (QA)
- Approve final validation steps to ensure no critical system impact.
- Verify that the decommissioning steps have been tested in staging.
- Confirm that relevant logs and artifacts are properly archived.
5.4 AI-IRB Liaison
- Evaluate ethical, regulatory, or compliance implications of removing AI features.
- Provide go/no-go approval if data or user interactions remain.
5.5 Product Management
- Communicate timelines and reasons for feature/model retirement to stakeholders.
- Negotiate new scope if partial replacement or feature bridging is needed.
5.6 Technical Support
- Update user documentation and support channels for the feature’s removal.
- Handle end-user queries about feature unavailability or alternative workflows.
6. Procedure Activities
Below are the key steps in the AI Feature Decommissioning and Model Retirement process:
6.1 Trigger and Planning
- Trigger Identification
- A recognized need to retire an AI feature or model arises. Possible triggers:
- Outdated model performance
- Ethical or compliance reasons (AI-IRB)
- Contract or license expiration
- Business direction changes
- Product Management logs a Decommission Request, capturing rationale, scope, date, and impacted user groups.
- A recognized need to retire an AI feature or model arises. Possible triggers:
- Initial IRB Check
- AI-IRB Liaison quickly reviews the request for potential regulatory or ethical concerns.
- If concerns exist, the IRB Liaison organizes a special ethics review prior to finalizing.
6.2 Formal Decommission Review
- Stakeholder Consultation
- Product Management organizes a meeting with the AI Dev Team, Operations, QA, and AI-IRB Liaison (if required) to confirm scope, timeline, and resource needs.
- A “Decommission Plan” is drafted, including:
- Proposed retirement date(s)
- Potential user impacts or downtime
- Communication messages to customers and internal teams
- Data archival requirements
- Documentation Update
- AI Dev Team compiles final “State of Model” documentation, capturing the last training date, performance metrics, any known issues, plus all relevant code references.
- Technical Support updates user support docs to reflect upcoming removal.
6.3 Preparatory Actions
- Staging Validation
- Operations sets up a staging environment that simulates the removal.
- AI Dev Team tests removal scripts or new code merges that “turn off” or remove the model from active workflows.
- QA verifies that staging environment runs without the model and no critical breakage occurs.
- User Notification
- Product Management or Technical Support announces feature retirement date to end users/clients, clarifying how functionality changes and whether any alternative features exist.
6.4 Decommission Execution
- Shutdown
- On the scheduled date/time, Operations executes the retirement steps (e.g., removing references in code, cron job disabling, pulling the model out of the pipeline).
- AI Dev Team stands by for immediate bug fixes or reverts if issues arise.
- Archive / Purge
- Operations archives essential logs, final model versions, or metadata for future references.
- If required by law or AI-IRB directive, data is either anonymized or disposed of, including audits of recursive self-improvement subroutines.
- Verification
- QA verifies that the production environment is stable and that the feature is indeed removed, integrating Exochain Peer Reviews for all automated retirement verification results.
- AI-IRB Liaison signs off if there were ethical or compliance constraints.
6.5 Post-Implementation Review
- Lessons Learned
- Project Manager schedules a retrospective. The following items are analyzed:
- Efficiency of the decommission steps
- Unforeseen user support issues
- Ethical or compliance surprises
- Impact on system resources
- Operations and AI Dev Team propose improvements for future retirement events.
- Project Manager schedules a retrospective. The following items are analyzed:
- Report and Close
- Project Manager finalizes a “Decommission Report,” including an overview, encountered problems, resolved issues, user feedback, and final status.
- This is distributed to Senior Management, archived in the knowledge repository, and the procedure is declared complete.
7. Forms
- Decommission Request Form – Captures the rationale and details for retiring an AI feature or model.
- Deployment/Decommission Script – Step-by-step instructions for removing or disabling the feature in the environment.
- AI-IRB Clearance Form – Documents IRB review and sign-off if required.
8. Exemptions
- Emergent critical or emergency removal that bypasses standard scheduling must still follow IRB review if user impact or ethical concerns are found.
- Training environment or sandbox environment features do not require this SOP unless they actively feed or influence production data.
9. Tools / Software / Technology Used
- Configuration Management Tools: For code versioning and to ensure the final revision of the removed feature is documented.
- Monitoring/Logging Tools: Observing system performance after removal.
- Ticketing System: For tracking tasks assigned to Dev, Ops, QA, and IRB sign-offs.
- Notification System: Email or client support updates regarding the timeline.
10. Revision History
| Version | Date | Revised By | Details of Change |
|---|---|---|---|
| 1.0 | (Date of Issue) | SOP Author (Name) | Initial Release |
| 1.1 | Future Date | Reviewer Name | Minor Clarifications or Updated Steps |
Final Approval
- SOP Owner (CTO)
- Signature: ____________________ Date: __________________
- Quality Assurance Lead
- Signature: ____________________ Date: __________________
- AI-IRB Liaison (if required)
- Signature: ____________________ Date: __________________
END OF SOP
@startuml
’ Define participants (roles) as shortnames
participant “Product Management” as ProdM
participant “AI Dev Team” as AIDev
participant “Operations” as Ops
participant “Quality Assurance” as QA
participant “AI-IRB Liaison” as IRB
participant “Technical Support” as TSupp
participant “Senior Management” as CTO
title SOP-1011-01-AI: AI Feature Decommissioning & Model Retirement
’ 1. Trigger and Planning
ProdM -> ProdM: Identify need to retire AI feature/model
note right
Possible reasons:
- Outdated performance
- Ethical concerns
- End of contract
- etc.
end note
ProdM -> IRB: Quick check for ethical/compliance flags
alt IRB has concerns
IRB -> ProdM: Request deeper ethics review
note right
IRB organizes special session
to address compliance issues
end note
else IRB has no concerns
IRB -> ProdM: No immediate concerns
end
’ 2. Formal Decommission Review
ProdM -> AIDev: Initiate Decommission Review meeting (scope, timeline)
ProdM -> Ops: Include environment impact in meeting
ProdM -> QA: Include QA for acceptance criteria
ProdM -> IRB: Include if required
note over ProdM,IRB
Meeting outcome:
- Draft “Decommission Plan”
- Proposed retirement date
- Notification approach
end note
AIDev -> AIDev: Compile final model status docs
TSupp -> TSupp: Update user support docs (pending final approval)
’ 3. Preparatory Actions
Ops -> Ops: Prepare staging environment for removal
AIDev -> QA: Provide removal scripts for test
QA -> QA: Verify removal in staging, check for breakage
alt QA finds critical issues
QA -> AIDev: Return to fix removal scripts
AIDev -> QA: Provide updated scripts
QA -> QA: Re-verify
else QA passes
QA -> ProdM: Staging environment validated
end
ProdM -> TSupp: Notify end-users about upcoming removal timeline
’ 4. Decommission Execution
ProdM -> Ops: Authorize final removal in production
Ops -> AIDev: Execute retirement steps (disable features, remove references)
AIDev -> Ops: Stand by for possible revert/fixes
Ops -> QA: Confirm environment stable, feature is removed
alt if IRB involvement
IRB -> Ops: Check final compliance sign-off
end
Ops -> Ops: Archive/purge logs, model artifacts
note right
- If mandated by IRB or law:
anonymize or destroy data
end note
’ 5. Post-Implementation Review
ProdM -> All: Schedule lessons learned meeting
All -> All: Discuss any issues, user feedback
Ops -> AIDev: Propose improvements for next retirement
AIDev -> ProdM: Provide final Decommission Report
ProdM -> Senior Management: Submit official closure docs
@enduml
Short textual explanation:
This sequence diagram shows how Product Management initiates the decommissioning request, checks with the AI-IRB Liaison for ethical compliance, and coordinates with the AI Dev Team, Operations, and Quality Assurance to test removal in a staging environment. After user communication by Technical Support, the final retirement steps occur in production. Lastly, a Post-Implementation Review is held to capture lessons learned and officially close out the AI feature/model retirement process.