AI System Retirement
Retirement is the controlled transition of an AI system from active operation to a retired state. Within AIGO, retirement is a governed lifecycle activity. It should preserve accountability, manage residual risk, address dependencies, retain required evidence, and maintain sufficient records to demonstrate what happened to the system and why. Retirement does not mean simply shutting down a model or application. It is a governance decision that should be planned, approved, executed, verified, and recorded.Purpose
The purpose of retirement is to ensure that an AI system can leave active operation without creating unmanaged technical, operational, legal, security, privacy, governance, or residual-risk issues. A retirement process should establish:- why the system is being retired
- who authorized the retirement
- when retirement becomes effective
- what dependencies must be addressed
- what risks remain
- what data and records must be retained
- what access and operational capabilities must be removed
- how retirement completion will be verified
- how the final governance state is recorded
Retirement triggers
An AI system may enter retirement because of:- replacement by another system
- business or operational discontinuation
- unacceptable or no longer justified risk
- changes in organizational strategy
- changes in applicable requirements
- technical obsolescence
- inability to maintain required controls
- repeated incidents or unacceptable performance
- changes to intended use
- consolidation of systems or capabilities
- expiration of an approved operating period
- completion of a temporary or experimental use
Retirement assessment
Before retirement is approved, the organization should assess the consequences of removing the system from operation. Depending on the system, the assessment may consider:- active users
- affected stakeholders
- dependent systems
- integrations and interfaces
- downstream processes
- data dependencies
- contractual obligations
- regulatory or policy requirements
- open incidents
- unresolved risks
- outstanding corrective actions
- monitoring obligations
- assurance activities
- evidence and record-retention requirements
- replacement or transition arrangements
Retirement approval
Retirement should be authorized by the appropriate governance authority. The decision should be traceable to:- the AI system
- the retirement reason
- the retirement assessment
- relevant risks
- outstanding controls or deficiencies
- incidents and changes where applicable
- required evidence
- responsible owners
- the authorized decision-maker
- the planned retirement date
- any conditions attached to retirement
Retirement execution
The retirement process may include:- confirming retirement authorization
- notifying relevant stakeholders
- disabling or removing active system access
- disabling integrations and automated processes
- addressing dependent systems
- preserving required records and evidence
- handling data according to applicable requirements
- removing or decommissioning infrastructure where appropriate
- updating governance and inventory records
- verifying that the system is no longer operating as an active governed system
- recording the completed retirement
- initiating any required post-retirement review
Data and records
Retirement does not necessarily mean deleting every record associated with an AI system. Organizations should determine what must be:- retained
- archived
- transferred
- anonymized
- deleted
- destroyed
- restricted from further use
Residual risk
Retirement should consider risks that remain after active operation ends. Examples include:- continued availability of historical outputs
- retained data
- residual access
- downstream dependencies
- contractual obligations
- unresolved incidents
- unresolved regulatory or governance obligations
- security exposure from inactive infrastructure
- continued use of outputs or models outside the intended governance boundary
Retirement verification
Retirement should be verified rather than assumed to be complete. Verification may confirm that:- required approvals were obtained
- active operation has ceased
- access has been removed or appropriately restricted
- relevant integrations have been disabled
- dependencies have been addressed
- required data and records have been handled
- outstanding governance actions have been resolved or transferred
- required evidence has been retained
- the AI system inventory reflects the retired state
Traceability
AIGO treats retirement as part of the governed AI system lifecycle. The retirement record should maintain relationships to relevant governance records, including:- AI system
- classification
- risks
- controls
- assessments
- approvals
- monitoring
- incidents
- changes
- assurance
- evidence
- improvement activities
Machine-readable representation
AIGO provides a machine-readable Retirement Schema for representing retirement activities and their associated governance information. Canonical schema:schemas/13-retirement/13-AIGO-Retirement-Schema-v0.1.json
Schema documentation:
schemas/13-retirement/13-AIGO-Retirement-Schema-Documentation-v0.1.md
The retirement record can be related to the governed AI system and other lifecycle records, preserving machine-readable traceability.
Relationship to monitoring and assurance
Retirement should not automatically terminate every governance activity. Depending on the circumstances, monitoring or assurance may be required during the transition or after operational shutdown to verify that:- the system has actually ceased operation
- dependencies have been removed
- residual risks are controlled
- required records have been retained
- retirement conditions have been satisfied
Relationship to continual improvement
Retirement provides an opportunity to capture lessons from the AI system lifecycle. Organizations may identify improvements relating to:- governance processes
- risk assessment
- controls
- monitoring
- incident management
- change management
- approval processes
- system design
- deployment practices
- future AI system selection
Evaluation and validation
Where machine-readable governance rules and evaluation are used, retirement-related conditions may be evaluated before or after retirement. For example, an organization may define checks for required approval, retirement status, evidence, or completion conditions. Evaluation provides structured governance support but does not replace the organization’s authorized retirement decision or other required human judgment. Repository validation can verify the integrity of the relevant schema, references, rules, and associated artifacts. Such validation does not itself establish legal compliance, certification, accreditation, or regulatory conformity.Operational procedure
The canonical implementation procedure is maintained in:guidance/02-procedures/12-AIGO-AI-Retirement-Procedure-v0.1.md
The corresponding implementation template is maintained in:
guidance/03-templates/15-AIGO-AI-Retirement-Template-v0.1.md
Related implementation activities
Retirement should be considered together with:- AI System Registration
- AI Change Management
- AI Monitoring
- AI Governance Assurance
- Risk Acceptance
- Continuous Improvement