> ## Documentation Index
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# AIGO AI System Profiles v0.1

# AIGO — AI Governance Operating Framework

## AI System Profiles

**Version:** 0.1
**Status:** Draft
**Working Name:** AIGO
**Full Name:** AI Governance Operating Framework

***

### 1. Purpose

The AIGO AI System Profiles provide a structured method for describing common categories of artificial intelligence systems and identifying governance considerations that may apply to each category.

Profiles are intended to help organizations determine which governance activities, risks, controls, evidence requirements, and assurance activities may be relevant to a particular AI system.

Profiles provide reusable governance guidance while recognizing that the actual risk of an AI system depends on its specific context, purpose, implementation, users, data, autonomy, and operating environment.

***

### 2. Profile Principles

AIGO AI System Profiles should follow these principles:

* profiles should be technology-neutral where practical;
* profiles should describe governance characteristics rather than prescribe specific technologies;
* profiles should support risk-based governance;
* profiles should be adaptable to organizational context;
* profiles should support lifecycle governance;
* profiles should identify relevant risks and controls;
* profiles should support traceability;
* profiles should not replace individual AI system assessment;
* profiles should be maintained as AI technologies and use cases evolve; and
* organizations may create additional profiles where necessary.

***

### 3. Profile Scope

AIGO profiles may be used for:

* traditional machine learning systems;
* predictive AI systems;
* generative AI applications;
* large language model applications;
* retrieval-augmented generation systems;
* chatbots;
* conversational AI;
* AI agents;
* agentic workflows;
* AI-powered automation;
* decision-support systems;
* recommendation systems;
* classification systems;
* AI-enabled products;
* third-party AI services; and
* other AI-enabled capabilities.

The profile structure may be extended to address additional AI system categories.

***

### 4. Profile Definition

An AI System Profile is a structured description of an AI system category that identifies characteristics relevant to governance.

A profile may define:

* profile identifier;
* profile name;
* purpose;
* applicability;
* system characteristics;
* typical risks;
* governance considerations;
* applicable controls;
* lifecycle considerations;
* evidence requirements;
* monitoring expectations; and
* assurance considerations.

***

### 5. Profile Identification

Each AIGO profile should have a unique identifier.

A representative identifier structure is:

`AIGO-PRF-001`

Profile identifiers should remain stable across framework versions where the underlying profile remains substantively the same.

New profiles should receive new identifiers where appropriate.

Retired profile identifiers should not be casually reassigned to unrelated profiles.

***

### 6. Profile Classification

Profiles may be classified according to the primary characteristics of the AI system.

Classification factors may include:

* system purpose;
* AI capability;
* level of autonomy;
* interaction model;
* decision authority;
* data sensitivity;
* external exposure;
* business criticality;
* potential impact; and
* operating environment.

Multiple profile characteristics may apply to the same AI system.

***

### 7. Profile Applicability

A profile may apply when an AI system exhibits the characteristics described by the profile.

Organizations should determine applicability based on the actual characteristics of the system.

Profile applicability should be reviewed when:

* system capabilities change;
* use cases change;
* autonomy increases;
* new integrations are introduced;
* data changes;
* users change; or
* operating context changes.

***

### 8. Profile Assignment

An organization may assign one or more profiles to an AI system.

Profile assignment should consider:

* system architecture;
* primary purpose;
* AI capabilities;
* autonomy;
* interaction patterns;
* data;
* users;
* dependencies; and
* risk.

Where multiple profiles apply, organizations should identify which profile characteristics and requirements are relevant.

***

### 9. Profile and Risk

Profiles should support AI risk identification but should not replace individual risk assessment.

A profile may identify common or expected risks associated with a category of AI systems.

The organization should determine whether those risks actually apply to the specific AI system and whether additional risks exist.

***

### 10. Profile and Governance Domains

Profiles may be mapped to relevant AIGO Governance Domains.

Mappings may identify considerations relating to:

* governance;
* accountability;
* risk;
* security;
* privacy;
* data;
* lifecycle management;
* human oversight;
* transparency;
* monitoring;
* assurance; and
* other relevant domains.

***

### 11. Profile and Lifecycle

Profiles should consider the AI Governance Lifecycle.

Relevant lifecycle stages may include:

* initiation;
* assessment;
* design;
* development;
* testing;
* approval;
* deployment;
* operation;
* monitoring;
* change;
* incident management; and
* retirement.

Profile guidance should identify lifecycle stages requiring particular attention where appropriate.

***

### 12. Profile and Controls

Profiles may identify controls that are:

* required;
* recommended;
* conditional;
* enhanced; or
* not normally applicable.

Control applicability should ultimately be determined according to the actual risk and characteristics of the AI system.

***

### 13. Profile and Evidence

Profiles may define evidence that is commonly relevant to the AI system category.

Evidence may include:

* system descriptions;
* risk assessments;
* data documentation;
* model documentation;
* testing results;
* approvals;
* monitoring records;
* incident records;
* user guidance;
* control evidence; and
* assurance results.

Evidence requirements should remain proportionate to risk.

***

### 14. Profile and Assurance

Profiles may identify assurance considerations appropriate to the system category.

Assurance may include:

* self-assessment;
* technical testing;
* control testing;
* independent review;
* audit;
* validation;
* monitoring; and
* external assessment.

The level of assurance should be determined according to risk and organizational requirements.

***

### 15. Profile Categories

AIGO may maintain profiles for common AI system categories.

Initial categories may include:

1. Predictive AI Systems
2. Generative AI Applications
3. Large Language Model Applications
4. Retrieval-Augmented Generation Systems
5. Conversational AI Systems
6. AI Agents
7. Agentic Workflows
8. AI-Powered Automation
9. Decision-Support Systems
10. Recommendation Systems
11. Classification Systems
12. Third-Party AI Services
13. AI-Enabled Products
14. AI-Enabled Business Processes

Additional profiles may be introduced as the framework develops.

***

### 16. Predictive AI Systems

Predictive AI systems use AI or machine learning techniques to estimate, classify, forecast, or otherwise predict outcomes based on available data.

Examples may include:

* demand forecasting;
* fraud detection;
* predictive maintenance;
* customer prediction;
* risk scoring;
* classification; and
* forecasting systems.

Governance considerations may include:

* data quality;
* model performance;
* validation;
* bias;
* drift;
* explainability;
* monitoring;
* human oversight; and
* impact assessment.

***

### 17. Generative AI Applications

Generative AI applications produce new content such as:

* text;
* images;
* audio;
* video;
* code; or
* other generated outputs.

Governance considerations may include:

* output reliability;
* harmful content;
* hallucination;
* intellectual property;
* privacy;
* security;
* user reliance;
* content review; and
* monitoring.

***

### 18. Large Language Model Applications

Large language model applications use language models as a primary component of an AI application.

Examples may include:

* enterprise assistants;
* document analysis;
* content generation;
* coding assistants;
* knowledge assistants;
* customer service applications; and
* internal productivity tools.

Governance considerations may include:

* prompt management;
* model selection;
* output validation;
* data protection;
* access control;
* logging;
* model provider dependencies; and
* user training.

***

### 19. Retrieval-Augmented Generation Systems

Retrieval-augmented generation systems combine information retrieval with generative AI.

The system may retrieve information from:

* databases;
* document repositories;
* knowledge bases;
* websites;
* enterprise systems; or
* other information sources.

Governance considerations may include:

* source authority;
* data access;
* retrieval accuracy;
* document freshness;
* access controls;
* information leakage;
* citation or source traceability;
* prompt handling; and
* output validation.

***

### 20. Conversational AI Systems

Conversational AI systems interact with users through natural language or other conversational interfaces.

Examples may include:

* customer service assistants;
* employee assistants;
* support chatbots;
* virtual assistants; and
* conversational interfaces.

Governance considerations may include:

* user identification;
* authentication;
* interaction logging;
* harmful outputs;
* privacy;
* escalation to humans;
* user disclosure;
* accessibility; and
* monitoring.

***

### 21. AI Agents

AI agents are AI-enabled systems capable of performing tasks or taking actions based on goals, instructions, environmental information, or tool access.

An agent may interact with:

* applications;
* databases;
* APIs;
* files;
* communication systems;
* enterprise tools; or
* external services.

Governance considerations may include:

* authorization;
* tool permissions;
* action limits;
* human oversight;
* monitoring;
* audit logging;
* failure handling;
* containment; and
* emergency shutdown.

***

### 22. Agentic Workflows

Agentic workflows involve multiple AI-driven actions, decisions, or steps that may be executed sequentially or conditionally.

A workflow may include:

* multiple AI agents;
* tools;
* APIs;
* decision points;
* human approvals;
* automated actions; and
* external systems.

Governance considerations may include:

* workflow boundaries;
* action authorization;
* dependency management;
* cascading failures;
* state management;
* human intervention;
* monitoring; and
* termination mechanisms.

***

### 23. AI-Powered Automation

AI-powered automation combines AI capabilities with automated business or technical processes.

Examples may include:

* automated document processing;
* workflow automation;
* automated ticket handling;
* automated classification;
* automated routing; and
* automated operational actions.

Governance considerations may include:

* automation scope;
* authorization;
* error handling;
* human review;
* business continuity;
* monitoring;
* rollback; and
* impact assessment.

***

### 24. Decision-Support Systems

Decision-support systems provide recommendations, analysis, scores, predictions, or other information intended to support human decisions.

The final decision may remain with a human decision-maker.

Governance considerations may include:

* human oversight;
* explainability;
* output accuracy;
* decision-maker competence;
* automation bias;
* fairness;
* documentation;
* appeal or review mechanisms; and
* monitoring.

***

### 25. Recommendation Systems

Recommendation systems provide suggestions, rankings, or personalized outputs based on data, user behavior, preferences, or other information.

Examples may include:

* product recommendations;
* content recommendations;
* service recommendations;
* search ranking; and
* personalization.

Governance considerations may include:

* personalization;
* user transparency;
* data use;
* fairness;
* manipulation risk;
* feedback loops;
* monitoring; and
* user controls.

***

### 26. Classification Systems

Classification systems assign inputs to categories, labels, or classes.

Examples may include:

* document classification;
* image classification;
* spam detection;
* fraud classification;
* content classification; and
* operational categorization.

Governance considerations may include:

* classification accuracy;
* false positives;
* false negatives;
* training data;
* threshold selection;
* impact;
* human review; and
* monitoring.

***

### 27. Third-Party AI Services

Third-party AI services are AI capabilities provided by external organizations.

Examples may include:

* hosted AI models;
* AI APIs;
* cloud AI services;
* AI SaaS platforms;
* managed AI services; and
* external AI processing services.

Governance considerations may include:

* supplier assessment;
* contractual requirements;
* data processing;
* security;
* privacy;
* service availability;
* model changes;
* provider dependencies;
* exit planning; and
* assurance.

***

### 28. AI-Enabled Products

AI-enabled products incorporate AI capabilities into products provided to customers or other external users.

Governance considerations may include:

* product safety;
* customer transparency;
* security;
* privacy;
* model performance;
* monitoring;
* updates;
* incident response;
* customer support; and
* change management.

***

### 29. AI-Enabled Business Processes

AI-enabled business processes use AI as part of an organizational process.

Examples may include:

* recruitment;
* customer support;
* financial operations;
* document processing;
* procurement;
* marketing;
* compliance;
* operations; and
* internal administration.

Governance should consider both the AI component and the overall business process.

***

### 30. Multi-Profile AI Systems

Some AI systems may exhibit characteristics of multiple profiles.

For example, an enterprise AI agent may also be:

* a generative AI application;
* a large language model application;
* a retrieval-augmented system;
* an automated workflow; and
* a decision-support system.

Organizations should identify the relevant characteristics and apply applicable governance requirements.

***

### 31. Profile Combination

Where multiple profiles apply, organizations should determine whether requirements should be:

* combined;
* applied independently;
* prioritized according to risk; or
* supplemented with additional controls.

Profile combination should not result in contradictory governance requirements.

***

### 32. Profile Risk Indicators

Profiles may identify risk indicators that suggest increased governance requirements.

Risk indicators may include:

* increased autonomy;
* sensitive data;
* external users;
* high-impact decisions;
* access to critical systems;
* ability to execute transactions;
* significant stakeholder impact;
* safety implications; and
* extensive third-party dependencies.

***

### 33. Profile-Based Control Selection

Profile characteristics may be used to identify an initial set of applicable controls.

The final control set should be determined using:

* system-specific risk;
* organizational policy;
* applicable requirements;
* lifecycle stage;
* system characteristics; and
* existing controls.

Profile-based control selection should support efficiency without replacing risk assessment.

***

### 34. Profile-Based Evidence

Profiles may identify common evidence expectations.

For example, an AI agent profile may require evidence relating to:

* permissions;
* tool access;
* action boundaries;
* human approvals;
* execution logs;
* monitoring;
* incident handling; and
* shutdown capability.

Evidence should remain proportionate to the actual risk.

***

### 35. Profile-Based Monitoring

Profiles may identify monitoring considerations specific to the system category.

Monitoring may include:

* system performance;
* output quality;
* model behavior;
* user activity;
* tool usage;
* security events;
* policy violations;
* unusual behavior; and
* control effectiveness.

***

### 36. Profile-Based Incident Considerations

Profiles may identify common incident scenarios.

Organizations should consider whether incidents may arise from:

* inaccurate outputs;
* harmful outputs;
* unauthorized actions;
* security attacks;
* privacy breaches;
* system failures;
* data quality problems;
* provider failures; or
* unexpected behavior.

***

### 37. Profile-Based Human Oversight

Profiles may identify where human oversight is particularly important.

Human oversight considerations may include:

* approval;
* review;
* intervention;
* override;
* escalation;
* decision authority; and
* shutdown.

The required level of oversight should be based on system risk and autonomy.

***

### 38. Profile-Based Transparency

Profiles may identify transparency requirements appropriate to the system category.

Transparency may include:

* informing users that AI is being used;
* explaining system purpose;
* identifying limitations;
* providing relevant information about outputs;
* documenting significant decisions; and
* communicating appropriate warnings.

***

### 39. Profile-Based Security

Profiles may identify security considerations relevant to the system category.

Security considerations may include:

* authentication;
* authorization;
* least privilege;
* input validation;
* prompt security;
* tool security;
* data protection;
* logging;
* monitoring; and
* incident response.

***

### 40. Profile-Based Privacy

Profiles may identify privacy considerations relevant to the system category.

Considerations may include:

* personal data;
* sensitive data;
* data minimization;
* access;
* retention;
* disclosure;
* third-party processing;
* user rights; and
* privacy monitoring.

***

### 41. Profile-Based Data Governance

Profiles may identify data governance considerations such as:

* data provenance;
* data quality;
* data ownership;
* data access;
* data classification;
* data retention;
* data lineage; and
* data validation.

***

### 42. Profile-Based Model Governance

Where a profile includes a material AI model, organizations may consider:

* model selection;
* model documentation;
* validation;
* testing;
* performance;
* limitations;
* versioning;
* monitoring; and
* change management.

***

### 43. Profile-Based Third-Party Governance

Where a profile depends on external services, organizations should consider:

* supplier due diligence;
* contractual requirements;
* service changes;
* provider risk;
* data processing;
* security;
* availability;
* dependency concentration; and
* exit planning.

***

### 44. Profile-Based Change Management

Profiles should identify characteristics that may require reassessment when changed.

Changes may include:

* model;
* data;
* prompts;
* tools;
* integrations;
* autonomy;
* users;
* use case;
* deployment environment; and
* business process.

***

### 45. Profile-Based Retirement

Profiles should consider governance requirements associated with AI system retirement.

Retirement may require:

* data handling;
* access removal;
* service termination;
* model decommissioning;
* evidence retention;
* dependency removal;
* contractual closure; and
* stakeholder communication.

***

### 46. Profile Documentation

Each formal AIGO profile should be documented consistently.

A profile should normally include:

* identifier;
* name;
* purpose;
* scope;
* applicability;
* characteristics;
* typical risks;
* governance considerations;
* controls;
* evidence;
* monitoring;
* assurance; and
* references.

***

### 47. Profile Ownership

Each formal profile should have an identified owner or responsible governance function.

The profile owner should support:

* profile maintenance;
* review;
* change proposals;
* stakeholder feedback;
* risk updates;
* control mappings; and
* versioning.

***

### 48. Profile Review

Profiles should be reviewed periodically and when significant changes occur.

Review triggers may include:

* emerging AI capabilities;
* new risks;
* new use cases;
* incidents;
* regulatory developments;
* stakeholder feedback;
* control changes; and
* changes in organizational practice.

***

### 49. Profile Versioning

Profiles should be versioned when material changes occur.

Version changes may include:

* revised applicability;
* new risk considerations;
* new controls;
* revised evidence;
* new monitoring requirements;
* clarification; or
* profile restructuring.

Version history should support traceability.

***

### 50. Profile Retirement

A profile may be retired when:

* it is no longer relevant;
* it has been replaced;
* its characteristics are incorporated into another profile; or
* the underlying technology or use case is no longer within scope.

Retired profiles should remain historically traceable where appropriate.

***

### 51. Profile Extensibility

Organizations may define additional AI System Profiles where existing profiles do not adequately describe a particular AI system or use case.

Additional profiles should document:

* profile purpose;
* applicability criteria;
* relevant risks;
* governance requirements;
* applicable controls; and
* required evidence.

Organization-specific profiles should maintain traceability to the AIGO framework where practical.

***

### 52. Profile Governance

The AIGO profile catalog should be governed through defined processes for:

* profile creation;
* review;
* approval;
* modification;
* versioning;
* mapping;
* deprecation; and
* retirement.

Changes to foundational profiles should be subject to appropriate framework governance.

***

### 53. Profile Mapping

Profiles may be mapped to:

* governance domains;
* lifecycle stages;
* risks;
* controls;
* roles;
* maturity requirements;
* evidence requirements; and
* external standards or regulations.

Mappings should support traceability without unnecessarily duplicating the underlying requirements.

***

### 54. Profile and Maturity

Organizations may use profiles to determine whether particular AI system categories require enhanced governance maturity.

Higher-risk profiles may require stronger capability in areas such as:

* risk management;
* controls;
* security;
* privacy;
* human oversight;
* monitoring;
* incident management; and
* assurance.

Profile-specific maturity expectations should remain risk-based.

***

### 55. Profile Assurance

Organizations should determine appropriate assurance activities for AI systems based on their profile and risk.

Assurance may include:

* control testing;
* technical validation;
* independent review;
* audit;
* performance testing;
* security testing;
* privacy review; and
* operational review.

***

### 56. Profile Traceability

AIGO profiles should support traceability between system characteristics and governance requirements.

A representative relationship is:

**AI System → Profile → Risk → Control → Evidence → Monitoring → Assurance**

Traceability should support:

* governance decisions;
* accountability;
* auditability;
* risk management;
* control selection; and
* continuous improvement.

***

### 57. Profile Implementation Guidance

Detailed implementation guidance for profiles may be maintained separately from the core profile definitions.

Guidance may include:

* implementation examples;
* assessment questions;
* control selection guidance;
* evidence examples;
* monitoring approaches; and
* operational recommendations.

Separating guidance from the normative profile definition helps preserve flexibility.

***

### 58. Profile Limitations

An AI System Profile does not by itself:

* determine legal compliance;
* determine final risk classification;
* replace risk assessment;
* replace control assessment;
* guarantee system safety;
* guarantee system performance; or
* eliminate the need for organizational judgment.

Profiles are governance aids and should be used together with the broader AIGO framework.

***

### 59. Profile Continuous Improvement

The AIGO profile catalog should be continuously improved using:

* implementation experience;
* incidents;
* emerging risks;
* stakeholder feedback;
* technology developments;
* regulatory developments;
* assurance findings; and
* lessons learned.

Profile improvements should maintain consistency with the wider AIGO framework.

***

### 60. Document Status

**Document:** AIGO AI System Profiles

**Version:** 0.1

**Status:** Draft

**Working Name:** AIGO

**Full Name:** AI Governance Operating Framework

**Document Type:** AI System Profiles

**Identifier Prefix:** `AIGO-PRF`

This document defines the foundational AI System Profile model for the AIGO framework.

Organizations may adapt and extend the profile model according to their AI systems, risk profile, organizational context, regulatory environment, and governance maturity while maintaining appropriate risk assessment, control applicability, evidence, monitoring, assurance, and traceability.