Governance, Security & Compliance (GSC) Principles

Enterprise Architecture principles for AI tool permissions, sandboxing, access controls, and compliance.

Category GSC

Principles 10

Focus AI Tool Permissions, Sandboxing, Access Controls, and Compliance

Status 🔍 Under Peer Review

Category Overview

flowchart TB
    subgraph AIAgent["AI Coding Agent"]
        Agent["AI Agent"]
    end

    subgraph PermBoundary["Permission Boundary (GSC-002)"]
        Perm["What AI can access"]
    end

    Agent --> Perm

    subgraph Resources["Controlled Resources"]
        direction LR
        Files["Files<br/>GSC-003"]
        Terminal["Terminal<br/>GSC-003"]
        Git["Git<br/>GSC-004"]
        Network["Network<br/>GSC-003"]
        Data["Data<br/>GSC-005"]
    end

    Perm --> Files
    Perm --> Terminal
    Perm --> Git
    Perm --> Network
    Perm --> Data

GSC Principles:

ID	Principle	Focus
GSC-001	Governance Framework	Accountability & oversight
GSC-002	Permission Boundaries	AI access limits
GSC-003	Sandboxing & Isolation	Execution environment
GSC-004	Git Access Controls	Version control restrictions
GSC-005	Data Classification	Data exposure controls
GSC-006	Audit & Traceability	Logging and compliance
GSC-007	Responsible AI Ops	Ethical operations
GSC-008	Network Security	AI network access controls
GSC-009	AI Agent Identity	Service account management
GSC-010	Secret Management	Credential protection

Principles in This Category

ID	Principle Name	Statement Summary
GSC-001	Governance Framework	Accountability and oversight
GSC-002	Permission Boundaries	Define AI agent access limits
GSC-003	Sandboxing & Isolation	Isolate AI execution environments
GSC-004	Git Access Controls	Restrict AI git operations
GSC-005	Data Classification	Control data exposure to AI
GSC-006	Audit & Traceability	Maintain AI activity audit trails
GSC-007	Responsible AI Operations	Ethical AI tool usage
GSC-008	Network Security	Control AI network access
GSC-009	AI Agent Identity	Manage AI service identities
GSC-010	Secret Management	Protect AI-related credentials

GSC-001: Governance Framework

Statement

Establish clear accountability structures where humans remain responsible for all AI tool operations, with documented ownership chains and approval requirements.

Rationale

Dimension	Justification
Business Value	Clear accountability reduces risk and enables confident AI adoption
Technical Foundation	Structured governance enables safe scaling of AI tool capabilities
Risk Mitigation	Defined ownership prevents gaps in responsibility for AI operations
Human Agency	Humans remain in control; AI tools operate under human supervision

Implications

flowchart TB
    Board["AI Governance Board<br/><i>Policy, standards, oversight</i>"]

    Board --> Tool["Tool Approval<br/>• Evaluate<br/>• Approve<br/>• Retire"]
    Board --> Security["Security Controls<br/>• Sandboxing<br/>• Permissions<br/>• Git access"]
    Board --> Compliance["Compliance Controls<br/>• Audit logs<br/>• Data class<br/>• Reporting"]

Area	Implication
Development	All AI tool usage attributed and governed
Governance	AI governance board or equivalent established
Skills	Governance training for all AI tool users
Tools	Tooling supports governance workflows and tracking

Maturity Alignment

Level	Requirements
Base (L1)	Manual governance; documented AI tool policies
Medium (L2)	Automated tracking; defined approval workflows
High (L3)	Integrated governance; predictive risk management

Governance

Compliance Measures

AI governance board established
AI tool policies documented
Approval workflows defined
Exception process established
Governance training completed

Exception Process

Condition	Approval Required	Documentation
New AI tool adoption	Governance Board	Risk assessment
Policy exception	Director	Exception rationale
Emergency tool usage	Manager	Post-hoc review

GSC-002: Permission Boundaries (governance defines permissions)
GSC-006: Audit & Traceability (governance requires auditability)
TSI-001: Evaluation Framework (governance drives evaluation)

GSC-002: Permission Boundaries

Statement

Define explicit permission boundaries for AI agents, limiting access to file systems, terminals, networks, and sensitive resources based on operational requirements.

Rationale

Dimension	Justification
Business Value	Controlled permissions prevent unintended consequences and data loss
Technical Foundation	Explicit boundaries enable safe AI tool operation
Risk Mitigation	Limited permissions reduce blast radius of AI errors
Human Agency	Humans define boundaries; AI operates within them

Implications

Permission Matrix

Permission Type	Restriction	Rationale
File System Read	Workspace only	Limit context exposure
File System Write	Workspace only	Prevent system damage
Terminal Read	Allowed	Needed for feedback
Terminal Execute	Approval req.	Commands have effects
Git Read	Allowed	Needed for context
Git Write (feature)	Allowed	Normal development
Git Write (main/master)	NEVER	Protected branches
Git Merge	NEVER	Human-only operation
Network Outbound	Filtered	Prevent exfiltration
Network Inbound	Blocked	No external access
Secrets/Credentials	NEVER	Security requirement
Production Systems	NEVER	Safety requirement

Permission Modes by Capability

Capability Level	Suggestion Mode	Auto-Edit Mode
Basic	Code completion only	Edit with explicit approval
Standard	Multi-file awareness	Edit with diff preview
Advanced	Full context access	Agent mode (file + terminal)
Autonomous	PR/MR creation	Auto-commit (feature only)

Area	Implication
Development	Configure AI tools with minimal necessary permissions
Governance	Permission policies documented and enforced
Skills	Train developers on permission configuration
Tools	Use tools that support permission controls

Maturity Alignment

Level	Requirements
Base (L1)	Manual permission configuration; documented policies
Medium (L2)	Centralized permission management; automated enforcement
High (L3)	Dynamic permission adjustment; context-aware controls

Governance

Compliance Measures

Permission matrix documented for each AI tool
Default permissions set to minimum required
Permission escalation process defined
Regular permission audits conducted
Violations tracked and remediated

Exception Process

Condition	Approval Required	Documentation
Extended file access	Tech Lead	Scope justification
Terminal auto-execute	Manager	Risk acknowledgment
Network access expansion	Security Team	Security review

GSC-003: Sandboxing (permissions within sandbox)
GSC-004: Git Access Controls (git-specific permissions)
DC-004: Agentic Development (agent permissions)

GSC-003: Sandboxing & Isolation

Statement

Execute AI agents in sandboxed environments that isolate them from production systems, sensitive data, and critical infrastructure.

Rationale

Dimension	Justification
Business Value	Isolation protects business-critical systems from AI errors
Technical Foundation	Sandboxing provides defense-in-depth for AI operations
Risk Mitigation	Contained execution limits damage from malicious or erroneous AI
Human Agency	Humans control sandbox boundaries and escape conditions

Implications

flowchart TB
    subgraph Untrusted["🔴 UNTRUSTED ZONE"]
        Sandbox["AI Agent Sandbox<br/>• Containerized execution<br/>• Network filtering<br/>• Resource limits (CPU, memory, disk)<br/>• Time limits for operations"]
    end

    subgraph Gateway["🟡 PERMISSION GATEWAY"]
        Validation["Request validation"]
        Approval["Approval workflow"]
        Audit["Audit logging"]
    end

    subgraph Trusted["🟢 TRUSTED ZONE"]
        Prod["Production systems<br/><i>NO AI access</i>"]
        Secrets["Secrets/credentials<br/><i>NO AI access</i>"]
        Protected["Protected branches<br/><i>Human-only</i>"]
    end

    Sandbox --> Validation
    Validation --> Approval
    Approval --> Audit
    Audit -.->|"Controlled Access"| Trusted

Sandboxing Capability Assessment

Capability	Required for L1	Required for L2	Required for L3
Built-in Sandbox	Recommended	Required	Required
Network Control	Recommended	Required	Required
Resource Limits	Optional	Recommended	Required
Audit Logging	Required	Required	Required

Area	Implication
Development	Use AI tools with sandboxing support when possible
Governance	Sandboxing requirements documented by risk level
Skills	Train teams on sandbox configuration and monitoring
Tools	Evaluate sandboxing capabilities during tool selection

Maturity Alignment

Level	Requirements
Base (L1)	Workspace isolation; no production access
Medium (L2)	Container-based sandboxing; network filtering
High (L3)	Full isolation with approval gateways; resource limits

Governance

Compliance Measures

Sandboxing requirements defined by use case
Tools evaluated for sandboxing capabilities
Production isolation verified
Resource limits configured
Sandbox escapes monitored and investigated

Exception Process

Condition	Approval Required	Documentation
Reduced isolation	Security Team	Risk acceptance
Extended resource limits	Manager	Justification
Network filter exception	Security Lead	Security review

GSC-002: Permission Boundaries (permissions within sandbox)
TSI-001: Evaluation Framework (evaluate sandboxing)
TQC-002: Security Scanning (scan sandbox outputs)

GSC-004: Git Access Controls

Statement

Restrict AI agent git operations to feature branches, prohibit direct commits to protected branches, and NEVER allow AI merge authority.

Rationale

Dimension	Justification
Business Value	Protected branches ensure code quality and review processes
Technical Foundation	Git controls prevent unauthorized changes to production code
Risk Mitigation	Merge restrictions ensure human review of all AI-generated code
Human Agency	Humans retain final authority over what enters the codebase

Implications

Git Permission Matrix

Operation	AI Agent	Human Dev	Lead/Admin
Clone/Fetch	✅	✅	✅
Create Branch	✅	✅	✅
Commit (feature)	✅*	✅	✅
Commit (main/master)	❌	❌	✅
Push (feature)	✅	✅	✅
Push (protected)	❌	❌	✅
Create PR	✅	✅	✅
Approve PR	❌	✅	✅
Merge PR	❌	⚠️**	✅
Force Push	❌	❌	⚠️
Delete Branch	❌	✅	✅
Modify Branch Rules	❌	❌	✅

* AI commits must include attribution (e.g., “Co-authored-by”) ** Requires approval from another human

Branch Naming for AI

Pattern: ai/<task-type>/<description>

Examples:

ai/bugfix/fix-auth-validation
ai/feature/implement-user-auth
ai/refactor/optimize-api-handler

Commit Attribution

Required trailer for AI-generated commits:

Co-authored-by: AI Assistant <ai@organization.com>
AI-Tool: <tool-identifier>
AI-Session: <session-id>

Area	Implication
Development	AI tools configured to use feature branches only
Governance	Branch protection rules enforced at repository level
Skills	Train developers on AI branch naming and attribution
Tools	Configure git hooks to enforce AI commit attribution

Maturity Alignment

Level	Requirements
Base (L1)	Protected branches; manual attribution
Medium (L2)	Automated branch rules; commit hooks for attribution
High (L3)	Integrated attribution; automated compliance checks

Governance

Compliance Measures

Branch protection rules configured
AI branch naming convention enforced
Commit attribution required and validated
Merge authority limited to humans
Regular audits of AI commit activity

Exception Process

Condition	Approval Required	Documentation
Direct push to protected	Never allowed	N/A
AI merge authority	Never allowed	N/A
Attribution waiver	Tech Lead	Temporary only

GSC-002: Permission Boundaries (git permissions)
DC-004: Agentic Development (agent git operations)
GSC-006: Audit & Traceability (git audit trail)

GSC-005: Data Classification

Statement

Classify all data according to sensitivity levels and enforce restrictions on what data can be exposed to AI tools based on classification.

Rationale

Dimension	Justification
Business Value	Data protection prevents breaches and maintains customer trust
Technical Foundation	Classification enables consistent data handling policies
Risk Mitigation	Restricted data never reaches AI tools that could expose it
Human Agency	Humans define classifications; AI operates within data boundaries

Implications

Data Classification Matrix

Classification	AI Tool Usage	Controls
PUBLIC	Unrestricted	Standard logging
INTERNAL	Approved tools only	Enhanced logging, no cloud
CONFIDENTIAL	On-premise AI only	Air-gapped, full audit
RESTRICTED	NO AI TOOLS	Prohibited
SECRETS	NEVER	Technical enforcement
PII/PHI	Special approval	Privacy controls, no cloud

Secrets That Must NEVER Reach AI

API keys and tokens
Database credentials
Private keys and certificates
Connection strings with credentials
OAuth client secrets
Encryption keys

Area	Implication
Development	Check data classification before including in context
Governance	Data classification policy documented and enforced
Skills	Train developers on data classification rules
Tools	Use tools with data classification awareness

Maturity Alignment

Level	Requirements
Base (L1)	Manual classification check; secret scanning
Medium (L2)	Automated classification; pattern detection
High (L3)	Integrated data catalog; AI-aware classification

Governance

Compliance Measures

Data classification policy documented
Secret scanning enabled in AI tools
PII/PHI detection configured
Classification violations tracked
Regular data exposure audits

Exception Process

Condition	Approval Required	Documentation
Confidential in cloud AI	Never allowed	N/A
PII exposure	Privacy Officer	Privacy review
Internal data exception	Manager	Business case

GSC-002: Permission Boundaries (data access permissions)
GSC-006: Audit & Traceability (data access logging)
TQC-002: Security Scanning (secret detection)

GSC-006: Audit & Traceability

Statement

Maintain comprehensive audit trails of all AI tool activities, enabling traceability, compliance verification, and incident investigation.

Rationale

Dimension	Justification
Business Value	Audit trails enable compliance and build stakeholder trust
Technical Foundation	Logging infrastructure supports investigation and improvement
Risk Mitigation	Traceability enables rapid incident response and root cause analysis
Human Agency	Audit trails ensure human oversight of AI activities

Implications

Minimum Audit Fields

Field	Description
Timestamp	When the AI activity occurred
User ID	Who initiated the AI operation
Tool Name	Which AI tool was used
Tool Version	Version of the AI tool
Activity Type	Completion, chat, agent, etc.
Input Hash	Hash of input for traceability
Output Hash	Hash of output for traceability
Files Accessed	Which files were read/written
Commands Run	Terminal commands executed
Git Operations	Commits, branches, PRs created
Review Status	Pending, approved, rejected
Reviewer ID	Who reviewed (if applicable)

Retention Requirements

Standard activities: Minimum 1 year
Compliance-relevant activities: Minimum 7 years
Storage: Immutable for audit integrity
Access: Searchable for incident investigation

Area	Implication
Development	AI tools configured with audit logging enabled
Governance	Audit requirements documented and verified
Skills	Train teams on audit log interpretation
Tools	Select tools with comprehensive logging capabilities

Maturity Alignment

Level	Requirements
Base (L1)	Basic logging; manual audit review
Medium (L2)	Centralized logging; automated compliance reports
High (L3)	Real-time monitoring; anomaly detection; AI forensics

Governance

Compliance Measures

Audit logging enabled for all AI tools
Retention policies configured
Regular audit reviews conducted
Compliance reports generated
Incident investigation procedures documented

Exception Process

Condition	Approval Required	Documentation
Reduced logging	Security Team	Risk acceptance
Shortened retention	Compliance Lead	Regulatory review
Log deletion	Never allowed	N/A

GSC-001: Governance Framework — audit supports governance
GSC-004: Git Access Controls — git audit trail
DM-002: Observability First — operational monitoring

GSC-007: Responsible AI Operations

Statement

Operate AI tools ethically, ensuring fairness, transparency, and accountability in all AI-assisted development activities.

Rationale

Dimension	Justification
Business Value	Responsible operations build trust and prevent reputation damage
Technical Foundation	Ethical guidelines translate into operational controls
Risk Mitigation	Proactive ethics prevents harm and regulatory issues
Human Agency	Humans define ethical boundaries; AI operates within them

Implications

Operational Ethics Principles

1. TRANSPARENCY

Disclose AI assistance in code and documentation
Be clear about AI capabilities and limitations

2. ACCOUNTABILITY

Humans remain responsible for all AI outputs
No “the AI did it” excuses

3. FAIRNESS

Equal access to AI tools within teams
Consider accessibility in AI tool selection

4. SECURITY

Prioritize security over convenience
Never bypass security controls for AI efficiency

Prohibited Practices

❌ Prohibited	Rationale
Using AI to bypass code review	Undermines quality controls
Claiming AI code as solely human-written	Violates transparency
Using AI on unauthorized data	Data classification violation
Disabling AI safety controls	Security risk
Using AI to generate malicious code	Ethical violation

Area	Implication
Development	Follow responsible AI practices in daily work
Governance	Responsible AI guidelines documented and enforced
Skills	Ethics training for all AI tool users
Tools	Select tools that support responsible AI practices

Maturity Alignment

Level	Requirements
Base (L1)	Documented guidelines; basic training
Medium (L2)	Integrated into workflows; regular reinforcement
High (L3)	Culture of responsibility; proactive improvement

Governance

Compliance Measures

Responsible AI guidelines documented
Training completed by all AI tool users
Prohibited practices communicated
Ethics violations tracked and addressed
Regular ethics assessments conducted

Exception Process

Condition	Approval Required	Documentation
Ethics guideline waiver	Ethics Board	Ethical review
New use case evaluation	Governance Board	Ethics assessment
Reported violation	HR + Legal	Investigation

GSC-001: Governance Framework (ethics governance)
TTA-002: Adoption Governance (responsible adoption)
DC-001: AI-Human Collaboration (ethical collaboration)

Category Summary

Key Takeaways

Permissions define boundaries - Explicit limits on what AI can access
Sandboxing contains risk - Isolate AI from critical systems
Git controls protect code - AI never gets merge authority
Data classification prevents exposure - Secrets never reach AI
Audit trails enable oversight - Log everything for accountability
Responsible operations build trust - Ethics are non-negotiable
Network controls prevent exfiltration - AI cannot access unauthorized endpoints
Identity management enables accountability - Every AI action has an identity
Secret management protects credentials - No credentials in AI context

GSC-008: Network Security

Statement

Control and monitor all network communications for AI coding tools, preventing unauthorized data transmission and ensuring secure connectivity.

Rationale

Dimension	Justification
Business Value	Prevents intellectual property and sensitive data leakage
Technical Foundation	Network controls are fundamental security layer
Risk Mitigation	Blocks data exfiltration and unauthorized API access
Human Agency	Humans define network boundaries; AI operates within them

Implications

flowchart TB
    subgraph AITool["AI Coding Tool"]
        Agent["AI Agent"]
    end

    subgraph NetworkControls["Network Security Layer"]
        Egress["Egress Filtering"]
        TLS["TLS/mTLS"]
        DNS["DNS Controls"]
    end

    subgraph Endpoints["Authorized Endpoints"]
        Approved["Approved AI APIs"]
        Internal["Internal Services"]
    end

    subgraph Blocked["Blocked"]
        Unauthorized["Unauthorized APIs"]
        External["External Destinations"]
    end

    Agent --> NetworkControls
    NetworkControls --> Endpoints
    NetworkControls -.->|"BLOCKED"| Blocked

Network Controls Matrix

Control Type	Requirement	Purpose
Egress Filtering	Required	Block unauthorized destinations
TLS Encryption	Required	Protect data in transit
DNS Control	Recommended	Prevent rogue endpoint access
Network Segmentation	Recommended	Isolate AI workloads
Rate Limiting	Required	Prevent abuse and exfiltration
Traffic Logging	Required	Enable security monitoring

Area	Implication
Development	Configure allowed endpoints for AI tools
Governance	Network policies documented and enforced
Skills	Train teams on network security configuration
Tools	Use tools that support network isolation

Maturity Alignment

Level	Requirements
Base (L1)	Basic egress filtering; TLS required
Medium (L2)	Network segmentation; traffic logging
High (L3)	Zero-trust network; real-time threat detection

Governance

Compliance Measures

Allowed endpoint list maintained
Network policies configured and tested
Traffic monitoring enabled
Incident response procedures defined

GSC-003: Sandboxing & Isolation
GSC-005: Data Classification
GSC-006: Audit & Traceability

GSC-009: AI Agent Identity

Statement

Assign distinct, managed identities to all AI agents and tools, enabling accountability, access control, and audit trail attribution.

Rationale

Dimension	Justification
Business Value	Enables accountability for all AI-initiated actions
Technical Foundation	Identity is the foundation of access control
Risk Mitigation	Prevents over-privileged AI operations
Human Agency	Humans control identity assignment and permissions

Implications

flowchart TB
    subgraph IdentityMgmt["Identity Management"]
        Managed["Managed Identity"]
        ServicePrincipal["Service Principal"]
        OAuth["OAuth Tokens"]
    end

    subgraph AIAgent["AI Agent"]
        Agent["Agent Instance"]
    end

    subgraph Controls["Access Controls"]
        RBAC["Role-Based Access"]
        Conditional["Conditional Access"]
        Scope["Scope Limits"]
    end

    subgraph Resources["Resources"]
        Code["Code Repositories"]
        APIs["APIs"]
        Data["Data Stores"]
    end

    IdentityMgmt --> AIAgent
    AIAgent --> Controls
    Controls --> Resources

Identity Requirements

Requirement	Base (L1)	Medium (L2)	High (L3)
Distinct AI Identity	Required	Required	Required
No Shared Credentials	Required	Required	Required
Identity Lifecycle Mgmt	Manual	Automated	Automated
Scope Limitation	Basic	Granular	Dynamic
Activity Attribution	Required	Required	Required

Area	Implication
Development	Configure AI tools with managed identities
Governance	Identity policies and lifecycle documented
Skills	Train teams on identity configuration
Tools	Use tools that support managed identity

Maturity Alignment

Level	Requirements
Base (L1)	Distinct identities; no embedded credentials
Medium (L2)	Managed identities; automated lifecycle
High (L3)	Just-in-time access; continuous identity validation

Governance

Compliance Measures

AI identity inventory maintained
No embedded credentials in tools
Identity lifecycle procedures documented
Regular access review conducted

GSC-001: Governance Framework
GSC-002: Permission Boundaries
GSC-006: Audit & Traceability

GSC-010: Secret Management

Statement

Protect all credentials, API keys, and tokens used with AI tools through centralized secret management, automatic rotation, and strict access controls.

Rationale

Dimension	Justification
Business Value	Prevents credential compromise and unauthorized access
Technical Foundation	Secrets are primary attack vector for AI tool abuse
Risk Mitigation	Automatic rotation limits exposure window
Human Agency	Humans control secret policies and access

Implications

flowchart TB
    subgraph SecretMgmt["Secret Management"]
        Vault["Secret Vault"]
        Rotation["Auto-Rotation"]
        Audit["Access Audit"]
    end

    subgraph AITool["AI Tool"]
        Agent["AI Agent"]
        Config["Configuration"]
    end

    subgraph NEVER["NEVER IN"]
        Prompts["Prompts"]
        Context["Context Windows"]
        Logs["Log Output"]
    end

    Vault --> Agent
    Agent -.->|"NEVER"| NEVER
    Rotation --> Vault
    Audit --> Vault

Secret Handling Rules

Secret Type	Storage	Rotation	AI Exposure
API Keys	Secret Vault	90 days	NEVER
Service Credentials	Managed Identity	Automatic	NEVER
Database Passwords	Secret Vault	90 days	NEVER
Encryption Keys	Key Management	Annual	NEVER
Session Tokens	Memory Only	Per-session	NEVER

Area	Implication
Development	Use environment variables for secret injection
Governance	Secret policies and rotation schedules documented
Skills	Train teams on secure secret handling
Tools	Use tools with secret vault integration

Maturity Alignment

Level	Requirements
Base (L1)	Centralized vault; no hardcoded secrets
Medium (L2)	Automatic rotation; access logging
High (L3)	Just-in-time secrets; anomaly detection

Governance

Compliance Measures

All secrets in centralized vault
Rotation schedules configured
No secrets in prompts or context
Secret access audited

GSC-005: Data Classification
GSC-006: Audit & Traceability
GSC-009: AI Agent Identity

Category Summary

Action	Link
View all principles	Principles Index
Related: Development	DC Principles
Related: Tools	TSI Principles
Constitution	Framework Constitution

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Governance, Security & Compliance (GSC) Principles

Category Overview

Principles in This Category

GSC-001: Governance Framework

Statement

Rationale

Implications

Maturity Alignment

Governance

Compliance Measures

Exception Process

Related Principles

GSC-002: Permission Boundaries

Statement

Rationale

Implications

Permission Matrix

Permission Modes by Capability

Maturity Alignment

Governance

Compliance Measures

Exception Process

Related Principles

GSC-003: Sandboxing & Isolation

Statement

Rationale

Implications

Sandboxing Capability Assessment

Maturity Alignment

Governance

Compliance Measures

Exception Process

Related Principles

GSC-004: Git Access Controls

Statement

Rationale

Implications

Git Permission Matrix

Branch Naming for AI

Commit Attribution

Maturity Alignment

Governance

Compliance Measures

Exception Process

Related Principles

GSC-005: Data Classification

Statement

Rationale

Implications

Data Classification Matrix

Secrets That Must NEVER Reach AI

Maturity Alignment

Governance

Compliance Measures

Exception Process

Related Principles

GSC-006: Audit & Traceability

Statement

Rationale

Implications

Minimum Audit Fields

Retention Requirements

Maturity Alignment

Governance

Compliance Measures

Exception Process

Related Principles

GSC-007: Responsible AI Operations

Statement

Rationale

Implications

Operational Ethics Principles

Prohibited Practices

Maturity Alignment

Governance

Compliance Measures

Exception Process

Related Principles

Category Summary

Key Takeaways