AIGO — AI Governance Operating Framework
AI Governance Lifecycle
Version: 0.1Status: Draft
Working Name: AIGO
Full Name: AI Governance Operating Framework
1. Purpose
The AIGO AI Governance Lifecycle defines the governance activities required to manage AI systems throughout their organizational lifecycle. The lifecycle provides a structured approach for governing AI systems from initial identification and conception through assessment, design, development, testing, approval, deployment, operation, monitoring, change, and retirement. The lifecycle is intended to provide continuity of governance rather than treating AI governance as a single approval event. Organizations should apply lifecycle activities proportionately according to:- the characteristics of the AI system;
- intended use;
- risk;
- impact;
- level of autonomy;
- organizational context;
- applicable requirements; and
- AI System Profile.
2. Lifecycle Principles
The AIGO AI Governance Lifecycle is based on the following principles:- governance should begin before material AI deployment decisions are made;
- governance should continue throughout the AI system lifecycle;
- governance activities should be proportionate to risk;
- significant decisions should be documented;
- responsibilities should remain clearly assigned;
- changes should trigger appropriate reassessment;
- monitoring should continue after deployment;
- evidence should be maintained throughout the lifecycle;
- significant incidents should trigger appropriate governance response; and
- retirement should be treated as a governed lifecycle activity.
3. Lifecycle Model
AIGO defines the following primary lifecycle stages:- Identification and Initiation
- Initial Assessment
- Concept and Purpose Definition
- Governance Classification
- Risk Assessment
- Design and Planning
- Development and Acquisition
- Validation and Testing
- Governance Review and Approval
- Deployment and Transition
- Operation and Monitoring
- Change and Reassessment
- Incident and Issue Management
- Periodic Review
- Retirement and Decommissioning
- Post-Retirement Review
4. Lifecycle Stage Governance
Each lifecycle stage should define, as appropriate:- purpose;
- entry criteria;
- required activities;
- responsible roles;
- required decisions;
- risk considerations;
- required controls;
- required evidence;
- exit criteria; and
- escalation requirements.
5. Stage 1 — Identification and Initiation
5.1 Purpose
The Identification and Initiation stage establishes whether an AI initiative, system, capability, or service should enter the organization’s AI governance lifecycle.5.2 Initiation Triggers
An AI governance lifecycle may be initiated when an organization:- proposes a new AI system;
- acquires an external AI service;
- introduces AI into an existing application;
- introduces a new AI model;
- introduces an AI agent;
- introduces an agentic workflow;
- materially changes an existing AI system;
- identifies previously ungoverned AI use;
- discovers unauthorized AI use; or
- determines that an existing system requires formal governance.
5.3 Initial Information
The organization should collect sufficient initial information to determine the appropriate governance path. Information may include:- proposed system name;
- business purpose;
- intended users;
- system owner;
- business owner;
- technology provider;
- proposed model or service;
- intended data;
- expected outputs;
- intended actions;
- deployment environment;
- level of autonomy;
- affected stakeholders; and
- anticipated use cases.
5.4 Entry Criteria
The stage may begin when there is sufficient information to identify the proposed AI activity and its organizational owner.5.5 Exit Criteria
The stage should result in:- an identified AI initiative;
- an identified accountable owner;
- an initial description of intended use;
- an initial governance classification; and
- a determination of whether further assessment is required.
6. Stage 2 — Initial Assessment
6.1 Purpose
The Initial Assessment determines whether the proposed AI system requires additional governance activities and establishes an initial understanding of its risk and impact.6.2 Assessment Areas
The assessment may consider:- intended purpose;
- affected stakeholders;
- type of AI capability;
- level of autonomy;
- data involved;
- potential impact;
- security considerations;
- privacy considerations;
- legal and regulatory considerations;
- third-party dependencies;
- operational dependencies; and
- potential misuse.
6.3 Initial Risk Screening
Organizations should perform an initial risk screening sufficient to determine the appropriate governance pathway. The screening should consider whether the system may create:- significant business impact;
- material financial impact;
- significant security risk;
- privacy risk;
- legal or regulatory risk;
- safety risk;
- reputational risk;
- discrimination or fairness concerns;
- human rights concerns;
- operational risk; or
- other material impacts.
6.4 Assessment Outcome
The outcome may include:- proceed with standard governance;
- proceed with enhanced governance;
- require additional specialist assessment;
- require management approval;
- defer the initiative; or
- prohibit the proposed use.
7. Stage 3 — Concept and Purpose Definition
7.1 Purpose
The Concept and Purpose Definition stage establishes the intended purpose, scope, objectives, and boundaries of the AI system.7.2 Intended Purpose
The intended purpose should be documented clearly enough to determine:- what the system is intended to do;
- who is expected to use it;
- what decisions or activities it supports;
- what actions it may perform;
- what it is not intended to do; and
- what conditions apply to its use.
7.3 Intended Use
The organization should distinguish between:- intended use;
- reasonably foreseeable use;
- permitted use;
- prohibited use; and
- unsupported use.
7.4 Purpose Boundaries
Purpose boundaries should identify relevant limitations on:- users;
- data;
- outputs;
- decisions;
- actions;
- environments;
- integrations; and
- system autonomy.
8. Stage 4 — Governance Classification
8.1 Purpose
The Governance Classification stage determines the level and type of governance required for the AI system.8.2 Classification Factors
Classification may consider:- system purpose;
- business criticality;
- potential impact;
- affected individuals;
- level of automation;
- level of autonomy;
- data sensitivity;
- security exposure;
- regulatory exposure;
- model characteristics;
- third-party dependency; and
- operational complexity.
8.3 Governance Categories
Organizations may establish governance categories such as:- standard;
- elevated;
- high;
- critical; or
- another risk-based classification model.
8.4 Classification Review
Classification should be reviewed when:- intended use changes;
- system capabilities change;
- autonomy increases;
- new data is introduced;
- the operating environment changes;
- material incidents occur; or
- applicable requirements change.
9. Stage 5 — Risk Assessment
9.1 Purpose
The Risk Assessment stage identifies, evaluates, and documents risks associated with the AI system.9.2 Risk Identification
Risk identification should consider, where relevant:- organizational risks;
- operational risks;
- technical risks;
- cybersecurity risks;
- privacy risks;
- data risks;
- legal and regulatory risks;
- ethical risks;
- safety risks;
- financial risks;
- reputational risks;
- third-party risks;
- human factors; and
- societal or stakeholder impacts.
9.3 Risk Analysis
Risks should be analyzed using an appropriate organizational methodology. The analysis may consider:- likelihood;
- impact;
- exposure;
- affected stakeholders;
- existing controls;
- residual risk; and
- uncertainty.
9.4 Risk Treatment
Risk treatment may include:- mitigation;
- avoidance;
- transfer;
- acceptance;
- additional controls;
- restricted use; or
- discontinuation.
9.5 Risk Acceptance
Residual risk should be accepted only by an appropriately authorized role. Risk acceptance should be documented where material.10. Stage 6 — Design and Planning
10.1 Purpose
The Design and Planning stage translates governance, business, risk, security, privacy, and operational requirements into system design and implementation requirements.10.2 Design Considerations
Design activities may consider:- system architecture;
- model selection;
- data architecture;
- security architecture;
- privacy requirements;
- human oversight;
- access control;
- monitoring;
- logging;
- resilience;
- fail-safe behavior;
- intervention mechanisms;
- integration boundaries; and
- third-party dependencies.
10.3 Governance by Design
Governance requirements should be incorporated into system design where practical rather than added only after implementation.10.4 Design Evidence
Design evidence may include:- architecture documentation;
- data-flow documentation;
- risk assessments;
- security assessments;
- privacy assessments;
- requirements specifications;
- model documentation;
- control specifications; and
- testing plans.
11. Stage 7 — Development and Acquisition
11.1 Purpose
The Development and Acquisition stage covers the development, configuration, integration, procurement, or acquisition of the AI system.11.2 Internal Development
Internal development should follow applicable organizational engineering and governance processes. Activities may include:- requirements implementation;
- model development;
- application development;
- data preparation;
- integration;
- testing;
- security implementation;
- monitoring implementation; and
- documentation.
11.3 Third-Party Acquisition
Where an AI system or service is acquired from a third party, organizations should conduct appropriate due diligence. Considerations may include:- provider capabilities;
- security;
- privacy;
- reliability;
- contractual terms;
- data handling;
- model limitations;
- transparency;
- service dependencies;
- change management; and
- exit arrangements.
11.4 Development Evidence
Evidence should be maintained according to the system’s governance requirements. Examples include:- source and configuration records;
- model information;
- test results;
- technical documentation;
- supplier documentation;
- security evidence;
- privacy evidence; and
- approval records.
12. Stage 8 — Validation and Testing
12.1 Purpose
The Validation and Testing stage determines whether the AI system performs sufficiently for its intended purpose and whether applicable governance requirements have been satisfied.12.2 Testing Scope
Testing should be proportionate to risk and may include:- functional testing;
- performance testing;
- accuracy testing;
- robustness testing;
- security testing;
- privacy testing;
- bias and fairness assessment;
- misuse testing;
- adversarial testing;
- human factors testing;
- safety testing; and
- operational testing.
12.3 Test Evidence
Test results should be documented sufficiently to demonstrate:- what was tested;
- how it was tested;
- expected results;
- actual results;
- identified deficiencies;
- remediation activities; and
- approval or disposition.
12.4 Deficiencies
Material deficiencies should be:- documented;
- risk assessed;
- remediated where required;
- accepted by an authorized role; or
- used as a basis for restricting or preventing deployment.
13. Stage 9 — Governance Review and Approval
13.1 Purpose
The Governance Review and Approval stage determines whether the AI system has satisfied the governance requirements necessary for deployment or the next applicable lifecycle stage.13.2 Review Scope
The review should consider, as applicable:- intended purpose;
- governance classification;
- risk assessment;
- risk treatment;
- applicable controls;
- security assessment;
- privacy assessment;
- legal and regulatory assessment;
- technical validation;
- human oversight;
- operational readiness;
- third-party requirements;
- required evidence; and
- outstanding issues.
13.3 Approval Decision
The approval decision may result in:- approval;
- conditional approval;
- approval with restrictions;
- deferral pending remediation;
- rejection; or
- escalation to a higher authority.
13.4 Approval Authority
Approval should be provided by an appropriately authorized role according to:- governance classification;
- risk level;
- organizational policy;
- system impact; and
- applicable requirements.
13.5 Conditions of Approval
Where approval is conditional, the conditions should be documented. Conditions may include:- restricted users;
- restricted use cases;
- additional monitoring;
- additional human oversight;
- additional security controls;
- remediation deadlines;
- additional assurance; or
- limitations on system autonomy.
14. Stage 10 — Deployment and Transition
14.1 Purpose
The Deployment and Transition stage moves the AI system from development or testing into its approved operational environment.14.2 Deployment Readiness
Before deployment, organizations should confirm, as appropriate:- required approvals are complete;
- identified material risks are addressed;
- required controls are implemented;
- monitoring is operational;
- security controls are active;
- access controls are configured;
- human oversight is established;
- incident procedures are available;
- users are appropriately trained;
- required documentation is available; and
- operational ownership has been established.
14.3 Controlled Deployment
Deployment should be performed according to an approved deployment process. For higher-risk systems, organizations may use controlled deployment approaches such as:- pilot deployment;
- phased rollout;
- limited user groups;
- restricted environments;
- additional monitoring; or
- progressive expansion.
14.4 Transition to Operations
The transition should establish clear responsibility for:- system operation;
- monitoring;
- incident response;
- maintenance;
- access management;
- change management; and
- lifecycle documentation.
15. Stage 11 — Operation and Monitoring
15.1 Purpose
The Operation and Monitoring stage ensures that the AI system continues to operate within approved conditions throughout its operational lifecycle.15.2 Operational Monitoring
Monitoring may include:- system availability;
- performance;
- accuracy;
- output quality;
- security events;
- privacy events;
- unusual behavior;
- model or data drift;
- user activity;
- access activity;
- control effectiveness; and
- significant changes in risk.
15.3 Monitoring Frequency
Monitoring frequency should be proportionate to:- system risk;
- system criticality;
- level of autonomy;
- operational impact;
- rate of change; and
- applicable requirements.
15.4 Monitoring Evidence
Monitoring activities should produce appropriate evidence. Evidence may include:- monitoring records;
- alerts;
- incident records;
- performance reports;
- review records;
- control results;
- audit logs; and
- risk indicators.
15.5 Operational Exceptions
Material deviations from approved operation should be:- investigated;
- documented;
- risk assessed;
- remediated; and
- escalated where appropriate.
16. Stage 12 — Change and Reassessment
16.1 Purpose
The Change and Reassessment stage governs material changes to an AI system and determines whether previous governance decisions remain valid.16.2 Changes Requiring Assessment
Changes may include:- model replacement;
- model version changes;
- significant model updates;
- changes to training data;
- changes to retrieval sources;
- changes to prompts or system instructions;
- changes to tools;
- changes to integrations;
- changes to permissions;
- changes to intended use;
- changes to users;
- changes to deployment environment;
- increases in autonomy; or
- changes to third-party providers.
16.3 Change Classification
Changes should be classified according to their potential impact. Classification may determine whether the change requires:- routine approval;
- technical review;
- risk reassessment;
- security review;
- privacy review;
- legal review;
- governance committee review; or
- reapproval of the AI system.
16.4 Reassessment
A material change should trigger reassessment of relevant:- risks;
- controls;
- governance classification;
- intended purpose;
- system profile;
- evidence requirements; and
- approval conditions.
17. Stage 13 — Incident and Issue Management
17.1 Purpose
The Incident and Issue Management stage provides governance for unexpected, harmful, unauthorized, or materially non-compliant AI system behavior.17.2 AI Governance Incidents
AI governance incidents may include:- unauthorized AI use;
- material security events;
- privacy incidents;
- harmful outputs;
- significant model failures;
- unexpected autonomous actions;
- control failures;
- significant bias or discrimination concerns;
- material data issues;
- regulatory concerns; and
- significant third-party failures.
17.3 Incident Response
Organizations should establish appropriate procedures for:- detection;
- reporting;
- triage;
- containment;
- investigation;
- remediation;
- escalation;
- communication;
- evidence preservation; and
- lessons learned.
17.4 Temporary Restrictions
Where appropriate, organizations may:- restrict system functionality;
- restrict users;
- disable integrations;
- suspend autonomous actions;
- place the system into a controlled state; or
- suspend the system entirely.
17.5 Post-Incident Review
Material incidents should be reviewed to determine whether changes are required to:- risks;
- controls;
- system design;
- monitoring;
- training;
- procedures;
- governance roles; or
- lifecycle requirements.
18. Stage 14 — Periodic Review
18.1 Purpose
The Periodic Review stage evaluates whether the AI system remains appropriate, effective, controlled, and aligned with its approved purpose.18.2 Review Areas
Periodic review may consider:- continued business purpose;
- system performance;
- risk profile;
- control effectiveness;
- security;
- privacy;
- regulatory requirements;
- incidents;
- user feedback;
- stakeholder impact;
- third-party changes;
- model changes; and
- governance maturity.
18.3 Review Frequency
Review frequency should be proportionate to the AI system’s risk and characteristics. Higher-risk systems may require more frequent review.18.4 Review Outcomes
A periodic review may result in:- continued operation;
- updated controls;
- updated risk assessment;
- updated governance classification;
- additional assurance;
- restricted operation;
- redesign;
- replacement; or
- retirement.
19. Stage 15 — Retirement and Decommissioning
19.1 Purpose
The Retirement and Decommissioning stage governs the controlled withdrawal of an AI system from operational use.19.2 Retirement Triggers
Retirement may occur when:- the business purpose is no longer required;
- the system is replaced;
- risks become unacceptable;
- the system becomes obsolete;
- the provider is discontinued;
- the system no longer meets requirements;
- maintenance becomes impractical; or
- organizational strategy changes.
19.3 Retirement Planning
Retirement planning should consider:- users;
- dependencies;
- data;
- integrations;
- models;
- infrastructure;
- third-party services;
- records;
- contractual obligations;
- security;
- privacy; and
- business continuity.
19.4 Decommissioning Activities
Activities may include:- disabling access;
- removing integrations;
- terminating services;
- archiving required records;
- securely disposing of data where required;
- revoking credentials;
- removing infrastructure;
- updating inventories; and
- communicating retirement.
20. Stage 16 — Post-Retirement Review
20.1 Purpose
The Post-Retirement Review evaluates the outcomes and lessons from the AI system lifecycle after retirement.20.2 Review Areas
The review may consider:- lifecycle performance;
- incidents;
- governance effectiveness;
- control effectiveness;
- risk decisions;
- stakeholder outcomes;
- lessons learned;
- technical lessons;
- operational lessons; and
- opportunities for improvement.
20.3 Knowledge Retention
Relevant information should be retained according to organizational requirements. This may include:- system documentation;
- risk assessments;
- approval records;
- incident records;
- audit evidence;
- change records;
- retirement records; and
- lessons learned.
21. Lifecycle Gates
AIGO may use governance gates to establish decision points between lifecycle stages. Examples include:- Initiation Gate
- Assessment Gate
- Risk Gate
- Design Gate
- Development Gate
- Validation Gate
- Approval Gate
- Deployment Gate
- Operational Review Gate
- Retirement Gate
22. Lifecycle Entry and Exit Criteria
Each lifecycle stage should define appropriate entry and exit criteria. Entry criteria establish whether sufficient conditions exist to begin a stage. Exit criteria establish whether the required activities and decisions have been completed before proceeding. Where exit criteria are not satisfied, the organization should determine whether to:- remediate;
- defer;
- restrict;
- escalate; or
- terminate the activity.
23. Lifecycle Evidence
Organizations should maintain evidence appropriate to the AI system and its governance requirements. Evidence may include:- system records;
- risk assessments;
- governance classifications;
- architecture documentation;
- technical documentation;
- test results;
- approvals;
- monitoring records;
- incident records;
- change records;
- review records; and
- retirement records.
24. Lifecycle Traceability
AIGO lifecycle activities should maintain traceability between governance decisions and the artifacts supporting those decisions. Where practical, organizations should be able to trace: Lifecycle Stage → Activity → Role → Risk → Control → Evidence → Decision Traceability should support:- accountability;
- auditability;
- assurance;
- change management; and
- continuous improvement.
25. Lifecycle Tailoring
Organizations may tailor the AIGO lifecycle according to:- organizational size;
- system complexity;
- risk;
- regulatory environment;
- AI System Profile;
- development methodology;
- procurement model;
- deployment architecture; and
- operational context.
26. Lifecycle for Third-Party AI Systems
Organizations using third-party AI systems or services should apply the AIGO lifecycle to the extent appropriate to their level of control and responsibility. Lifecycle activities may include:- provider identification;
- due diligence;
- contractual assessment;
- risk assessment;
- security and privacy assessment;
- approval;
- deployment;
- monitoring;
- provider change management;
- incident management;
- periodic review; and
- service termination.
27. Lifecycle for AI Agents
AI agents may require additional lifecycle activities because they may perform actions, invoke tools, interact with external systems, or operate with a degree of autonomy. Organizations should consider:- authorized actions;
- available tools;
- permissions;
- external integrations;
- human oversight;
- action limits;
- failure handling;
- monitoring;
- escalation; and
- shutdown mechanisms.
28. Lifecycle for Agentic Workflows
Agentic workflows may involve multiple AI components operating together to achieve a business objective. The lifecycle should consider:- individual AI components;
- orchestration mechanisms;
- workflow logic;
- data flows;
- tool access;
- permissions;
- human intervention;
- dependencies;
- monitoring; and
- failure propagation.
29. Lifecycle for Generative AI Systems
Generative AI systems may require lifecycle considerations related to:- model selection;
- prompt design;
- system instructions;
- retrieval sources;
- generated content;
- output validation;
- content risks;
- intellectual property;
- privacy;
- security;
- hallucination or inaccurate output;
- user reliance; and
- monitoring.
30. Lifecycle for AI-Enabled Business Processes
Where AI is embedded within an existing business process, the AIGO lifecycle should consider both the AI system and the overall business process. Assessment should consider:- process purpose;
- decision points;
- human involvement;
- process dependencies;
- downstream impacts;
- affected stakeholders;
- control points; and
- business continuity.
31. Lifecycle for Existing AI Systems
Organizations may discover AI systems that were implemented before the AIGO governance lifecycle was established. Such systems should be brought into the lifecycle through an appropriate retrospective assessment. The retrospective process may include:- identifying the system;
- identifying the accountable owner;
- documenting intended use;
- determining governance classification;
- performing risk assessment;
- identifying governance gaps;
- implementing required controls;
- obtaining required approval; and
- establishing ongoing monitoring.
32. Lifecycle for Unauthorized AI Use
Organizations should establish mechanisms for identifying and responding to AI systems or services that are being used without appropriate authorization. Unauthorized use may include:- unapproved AI services;
- unauthorized models;
- unapproved AI applications;
- unauthorized data processing;
- unapproved AI agents;
- personal use of organizational data with external AI services; or
- AI functionality introduced without governance review.
- investigation;
- containment;
- removal;
- risk assessment;
- remediation;
- user education; and
- formalization through the AIGO lifecycle where appropriate.
33. Lifecycle Risk Proportionality
AIGO lifecycle requirements should be proportionate to the potential risk and impact of the AI system. Higher-risk systems may require:- additional assessment;
- more detailed documentation;
- stronger controls;
- independent assurance;
- increased monitoring;
- additional human oversight;
- more frequent review; and
- higher approval authority.
34. Lifecycle Exceptions
Organizations may establish controlled exceptions to standard lifecycle requirements where justified. An exception should:- identify the requirement being excepted;
- document the reason;
- assess associated risk;
- identify compensating measures where appropriate;
- identify an authorized approver;
- define any conditions or limitations; and
- establish an expiration or review date where appropriate.
35. Lifecycle Escalation
Lifecycle issues should be escalated when they exceed defined authority, risk tolerance, or governance thresholds. Escalation may be required when:- material risks cannot be adequately mitigated;
- required evidence is unavailable;
- approval authority is unclear;
- significant control deficiencies remain;
- system behavior materially differs from expectations;
- an incident occurs;
- a significant regulatory concern is identified; or
- lifecycle requirements cannot be satisfied.
36. Lifecycle Decision Records
Material lifecycle decisions should be documented. Decision records may include:- decision date;
- decision maker;
- decision scope;
- information considered;
- relevant risks;
- controls considered;
- conditions;
- rationale; and
- resulting actions.
37. Lifecycle Monitoring
Organizations should monitor the effectiveness of their AI governance lifecycle itself. Lifecycle monitoring may consider:- stage completion;
- approval delays;
- unresolved risks;
- control deficiencies;
- incidents;
- exceptions;
- recurring issues;
- assessment quality;
- evidence completeness; and
- governance outcomes.
38. Lifecycle Metrics
Organizations may establish metrics and indicators for lifecycle governance. Examples include:- number of AI systems registered;
- percentage of systems with assigned owners;
- percentage of systems with completed risk assessments;
- percentage of systems with required approvals;
- number of overdue reviews;
- number of open governance issues;
- number of exceptions;
- incident frequency;
- control effectiveness; and
- retirement completion.
39. Lifecycle Documentation
Organizations should maintain documentation appropriate to the lifecycle stage and risk of each AI system. Documentation may include:- initiation records;
- system descriptions;
- intended use;
- risk assessments;
- governance classifications;
- design documentation;
- testing evidence;
- approval records;
- operational documentation;
- monitoring records;
- change records;
- incident records;
- periodic reviews; and
- retirement records.
40. Lifecycle Records and Retention
Lifecycle records should be retained according to applicable organizational, legal, regulatory, contractual, and information-management requirements. Retention requirements should consider:- record type;
- business importance;
- risk;
- regulatory requirements;
- contractual requirements;
- privacy;
- security; and
- future assurance needs.
41. Lifecycle Roles and Accountability
Each lifecycle stage should have clearly defined responsibility and accountability. Roles identified in the AIGO Governance Roles and Accountability model may participate at different stages according to their responsibilities. Organizations should be able to determine, where appropriate:- who performs the activity;
- who is accountable;
- who approves the decision;
- who provides specialist advice;
- who must be consulted; and
- who must be informed.
42. Lifecycle and Controls
AIGO lifecycle activities should be connected to applicable controls. Controls within07-controls may apply to:
- one lifecycle stage;
- multiple lifecycle stages;
- the entire lifecycle; or
- specific AI System Profiles.
43. Lifecycle and Risk Management
The lifecycle should integrate with the AIGO risk model defined in06-risk.
Risk assessment should not be limited to a single point in the lifecycle.
Risk should be reviewed when:
- the system changes;
- new information becomes available;
- incidents occur;
- the operating environment changes;
- new stakeholders are affected;
- applicable requirements change; or
- the system reaches a new lifecycle stage requiring reassessment.
44. Lifecycle and Maturity
Organizations may assess the maturity of their AI lifecycle governance. Maturity considerations may include:- lifecycle definition;
- process consistency;
- role clarity;
- risk integration;
- evidence;
- automation;
- monitoring;
- assurance; and
- continuous improvement.
45. Lifecycle Continuous Improvement
The AIGO lifecycle should support continuous improvement. Organizations should use information from:- incidents;
- audits;
- assessments;
- monitoring;
- user feedback;
- stakeholder feedback;
- regulatory developments;
- technology changes;
- lessons learned; and
- governance metrics
46. Lifecycle Integration with Organizational Governance
AIGO should integrate with existing organizational governance processes where appropriate. Relevant processes may include:- enterprise risk management;
- information security management;
- privacy management;
- data governance;
- software development;
- procurement;
- change management;
- business continuity;
- internal audit;
- compliance; and
- quality management.
47. Lifecycle Integration with External Requirements
Organizations should consider applicable external requirements throughout the AI lifecycle. These may include:- laws;
- regulations;
- contractual obligations;
- industry standards;
- customer requirements;
- professional requirements; and
- organizational commitments.
48. Lifecycle Traceability Model
The AIGO lifecycle should support traceability across governance artifacts. A representative traceability relationship is: AI System → Lifecycle Stage → Activity → Role → Risk → Control → Evidence → Decision → Outcome This relationship supports:- accountability;
- governance transparency;
- auditability;
- assurance;
- change management; and
- continuous improvement.
49. Lifecycle Completion
An AI system should not be considered fully governed solely because it has passed an initial approval process. Governance should continue through:- operation;
- monitoring;
- change;
- reassessment;
- incident management;
- periodic review; and
- retirement.
50. Document Status
Document: AIGO AI Governance Lifecycle Version: 0.1 Status: Draft Working Name: AIGO Full Name: AI Governance Operating Framework Document Type: Framework Lifecycle Identifier Prefix:AIGO-LC
This document defines the foundational AI governance lifecycle for the AIGO framework.
Organizations may adapt the lifecycle according to their organizational structure, AI systems, risk profile, regulatory environment, and governance maturity while maintaining appropriate accountability, risk management, control, evidence, and assurance.