SOP-1306-01-AI_AI-End-of-Life-and-Sunset-Process
Title: AI End-of-Life & Sunset Process
Version: 1.0
Effective Date: (Insert Date)
Previous Version: None (New SOP)
Reason for Update: New SOP
Owner: AI Governance Office / Program Management
1. Objective
This Standard Operating Procedure (SOP) defines the end-of-life (EOL) and sunset processes for AI systems under the AI-IRB governed AI-SDLC environment. Its purpose is to ensure a structured, compliant, and risk-mitigated approach to decommissioning or significantly downgrading AI-based products and services in production.
2. Scope
This SOP applies to all AI systems or modules within the organization that have passed their operational lifecycle or become obsolete, requiring formal discontinuation or significant functionality decommissioning. It encompasses:
AI systems or services developed in-house.
AI modules integrated into existing platforms or solutions.
AI-based solutions hosted by partners but governed by the organization’s policies.
Decommissioning triggered by business decision, performance concerns, compliance mandates, or newly discovered ethical/regulatory obligations.
3. Applicable To
AI Governance Office (AIGO)
AI-IRB
Product Development
Program Management
Quality Assurance (QA)
Operations
Legal/Compliance
Data Owners / Data Science
Technical Support / Service Organization
4. Definitions
| Term or Acronym | Definition |
|---|---|
| AI-IRB | The AI Institutional Review Board responsible for ethical and regulatory oversight of AI solutions. |
| End-of-Life (EOL) | The final stage of the AI system’s lifecycle when formal processes begin to remove, archive, or decommission the system from production. |
| Sunset Process | The methodology and sequence of tasks to retire an AI system, ensure data handling obligations, notify stakeholders, and mitigate impacts. |
5. Roles and Responsibilities
| Role | Responsibility |
|---|---|
| AI Governance Office | Ensures EOL policy compliance, initiates reviews, obtains AI-IRB decisions, coordinates cross-functional alignment on EOL steps. |
| AI-IRB | Performs ethical oversight for decommissioning, reviews final impact, ensures compliance with fair use, data protection, and risk acceptance or mitigation. |
| Product Development | Documents scope changes or replacement strategies, updates or halts new feature development, coordinates with QA on final acceptance before shutdown. |
| Program Management | Facilitates the creation and tracking of EOL project plan, obtains resources, manages timeline, and escalates issues to AI Governance Office. |
| Quality Assurance (QA) | Validates final system shutdown approach, checks documentation quality, ensures any open defects or compliance obligations are completed or waived. |
| Operations | Executes technical tasks for system removal, data archiving, environment decommission, network updates, final backups, and environment tear-down. |
| Legal/Compliance | Ensures the EOL or sunset plan aligns with contractual, regulatory, privacy, and IP obligations. |
| Technical Support | Communicates end-of-support timelines, user notifications, and readiness; provides final feedback from user community. |
| Data Owners / Data Science | Oversees data retention or disposal strategies, ensures proper archiving or migration of AI model artifacts and training data. |
6. Procedure Activities
6.1 Trigger for EOL Decision
Initiation: Any stakeholder (Product Management, AI Governance Office, or user group) can request EOL or partial sunset.
Assessment: AI-IRB or AI Governance Office reviews triggers (e.g., compliance changes, performance issues, shift in business priorities).
6.2 Preliminary Review and Approval to Proceed
Program Management: Drafts an EOL Project Outline describing scope, timeline, resource needs, compliance checks, user impact, and any known critical tasks.
AI Governance Office: Coordinates with Legal/Compliance to confirm no ongoing obligations or external constraints preventing immediate EOL.
AI-IRB: Provides an initial ethical approval or outlines conditions (e.g., no ongoing usage in regulated environments, no user violation, no data retention conflicts).
Go/No-Go: AI Governance Office issues “Provisional EOL Approval” to proceed with a formal plan.
6.3 Detailed EOL Plan Creation
Program Management: Creates an EOL Plan that addresses:
Decommissioning Timeline
User Notifications & Support (including disclaimers or transitional solutions)
Data Retention & Archival (partially or fully archiving data, model artifacts, logs)
Compliance & Regulatory (GDPR, HIPAA, or domain-specific constraints)
Infrastructure Changes (server shutdown, environment teardown, integration points)
Documentation Updates (final updates to knowledge base, disclaimers, or training)
Operations: Drafts a technical tear-down plan (load balancer removal, DNS updates, container or VM destruction).
Quality Assurance: Checks plan for potential open bugs, final QA sign-off steps, or partial testing to confirm the environment is stable enough for safe removal.
Legal/Compliance: Confirms no unaddressed risk or liability.
AI-IRB: Approves final EOL Plan from an ethical and compliance standpoint.
6.4 Implementation and Monitoring
Product Development:
Stops new feature releases or freeze code merges for the AI system.
Communicates the final build or patch if needed to close major defects or security issues.
Technical Support: Notifies end-users regarding official support end date, usage disclaimers, or alternative product suggestions.
Operations:
Executes the environment shut-down or partial decommission in phases if needed.
Archives code and model artifacts in secure repository (with metadata).
Archives or disposes training data following data retention policy.
Monitors for unexpected network or user activity.
Quality Assurance: Validates that environment changes have not disrupted other systems, and that final tasks (data archiving, logs) are executed.
AI Governance Office: Tracks progress, ensures AI-IRB conditions are met. Provides final readiness check.
6.5 Post-EOL Verification and Lessons Learned
Operations: Verifies system is fully offline, no user access. Confirms that data disposal or retention is completed.
AI-IRB: Conducts final oversight session to confirm ethical obligations have been met (model reusability or termination, user disclaimers).
AI Governance Office: Documents outcomes, fosters feedback from all teams.
Program Management: Issues official “EOL Completed” note to stakeholders.
Lessons Learned: A final Post-Implementation Review is performed to capture insights, potential improvements for future EOL processes.
7. Forms / Templates
EOL Project Outline (high-level scope, triggers, resource needs).
EOL Plan (detailed timeline, user notifications, environment tear-down, compliance checklist).
Lessons Learned template (for capturing best practices or areas needing improvement).
8. Exemptions
If a partial EOL or “sunset” is required (e.g., only certain features disabled), the same process applies but with scope limited to the sub-module or partial functionality.
If a system is replaced by an internal new AI solution, the typical system integration or upgrade approach from the AI-SDLC must be referenced, ensuring no conflict with this EOL procedure.
9. Tools/Software/Technology Used
Project Management Software (for scheduling tasks, tracking EOL milestones).
Version Control System (for final code freeze and archival).
Archiving Tools (for data backups, logs, model artifacts).
Issue Tracking (to track EOL tasks, final bugs, sign-offs).
Communication Channels (email blasts, chat, wiki notices for end-users).
10. Appendix A: RACI Matrix (Example)
| Activity | Responsible | Accountable | Consulted | Informed |
|---|---|---|---|---|
| Initiate EOL Request | Anyone | AI Governance Office | AI-IRB, Program Mgt | Teams, Stakeholders |
| Draft EOL Project Outline | Program Mgt | Program Mgt | AI-IRB, AI Governance | Teams |
| Approve EOL & Sunset Plan | AI-IRB | AI Governance Office | Legal, Program Mgt | All Stakeholders |
| Execute Environment Decommission | Operations | Operations Manager | Dev, QA, Governance | Product, TechSupport |
| Validate Final EOL Steps | QA | QA Lead | Program Mgt | Stakeholders |
| Post-EOL Verification & Lessons Learned | AI-IRB | AI Governance Office | All Teams | Entire Org |
11. Revision History
| Version | Date | Description | Author |
|---|---|---|---|
| 1.0 | (Insert Date) | Initial Release of SOP-1306-01-AI | (Your Name / Title) |
End of SOP-1306-01-AI
@startuml
’ Participants (short names for clarity)
participant “Stakeholder” as SH
participant “AI Governance Office” as AIGO
participant “AI-IRB” as IRB
participant “Product Dev” as PD
participant “Program Mgmt” as PM
participant “Quality Assurance” as QA
participant “Operations” as OPS
participant “Legal/Compliance” as LEGAL
participant “Tech Support” as TS
participant “Data Science” as DS
’ 1) Stakeholder triggers EOL or partial sunset
SH -> AIGO: Request EOL (or partial sunset)
’ 2) AI Governance Office reviews triggers with AI-IRB
AIGO -> IRB: Present reason for EOL
’ 3) IRB decides to proceed or not
alt IRB Approves EOL
IRB -> AIGO: Provisional EOL Approval
else IRB Requests changes
IRB -> AIGO: Additional conditions or not feasible
end
’ 4) Program Mgmt drafts EOL Outline
AIGO -> PM: Request creation of EOL Project Outline
PM -> PM: Draft scope, timeline, resource needs
’ 5) Program Mgmt consults Legal
PM -> LEGAL: Check unaddressed compliance or contractual constraints
LEGAL -> PM: Confirm or provide recommendations
’ 6) IRB checks final ethical aspects
PM -> IRB: Submit Outline for ethical considerations
IRB -> PM: Outline accepted or conditions added
’ 7) Program Mgmt finalizes EOL Plan
PM -> OPS: Provide technical environment details
OPS -> PM: Proposed tear-down approach
PM -> QA: Check final QA tasks or waived items
QA -> PM: QA review of open defects or leftover issues
PM -> PD: Freeze new dev features for this AI system
’ 8) AI Governance Office obtains sign-off
AIGO -> IRB: Present final EOL Plan
IRB -> AIGO: EOL Plan Approved
’ 9) Implementation & Monitoring
PD -> PD: Stop new releases or merges
TS -> SH: Notify end-users of end-of-support date
OPS -> OPS: Archive code, model artifacts, logs
OPS -> DS: Archive or properly dispose training data
QA -> QA: Validate environment changes are safe
AIGO -> AIGO: Track EOL progress, ensure IRB conditions are met
’ 10) Post-EOL Verification & Lessons
OPS -> OPS: Complete system removal from production
IRB -> AIGO: Confirm all ethical obligations met
AIGO -> PM: Request final EOL completion note
PM -> SH: Communicate “EOL Completed”
AIGO -> AIGO: Perform Post-Implementation Review
AIGO -> IRB: Summarize lessons learned
@enduml
Short textual explanation:
This diagram shows the entire AI End-of-Life (EOL) and Sunset Process: a stakeholder initiates the request, AI Governance Office and AI-IRB decide feasibility, Program Management drafts an EOL plan in consultation with Legal/Compliance and QA, Operations handles technical tear-down, and finally AI-IRB confirms everything is ethically and procedurally complete before a closure announcement is issued and lessons learned are recorded.