AIOGF‑SD‑5.3 — Implementation Guidance

Document Identifier: AIOGF‑SD‑5.3
Related Control: 5.3
Framework: AI Operational Governance Framework (AIOGF)
Framework Version: 0.9 (Draft)
Document Version: 1.6
Author: Randy Manthey Date: February 25, 2026
Status: Working Draft
© 2025–2026 Randy Manthey. All Rights Reserved.

Licensing and Usage Notice

This supplemental document is part of the AI Operational Governance Framework (AI-OGF) and is protected under the AI‑OGF Limited Use License.

You may:

  • Read and reference this document for internal, non‑commercial use.

You may not:

  • Reproduce, redistribute, or create derivative works.
  • Use this document for commercial purposes, consulting, training, or resale.
  • Use this document to train AI models or automated systems.
  • Incorporate this document into tools, platforms, or governance products without written permission.

For permission requests or collaboration inquiries, visit the Permission and Collaboration page on the official AI-OGF site.

5.3.1 Purpose of the Practice

The purpose of this implementation guidance is to provide practical, repeatable methods for applying the dependency mapping requirements defined in Section 5.2. This includes:

  • documenting AI‑to‑infrastructure, AI‑to‑AI, and AI‑to‑human dependencies
  • maintaining accurate dependency maps as systems evolve
  • integrating dependency mapping into operational workflows
  • ensuring dependencies remain visible to operators and governance teams
  • reducing continuity risk by improving dependency awareness

This guidance ensures dependency mapping becomes a living operational practice, not a one‑time exercise.

5.3.2 Scope & Applicability

This supplement applies to:

  • all AI‑enabled workflows
  • infrastructure‑integrated AI
  • agentic and multi‑agent systems
  • workflow‑embedded AI
  • human‑in‑the‑loop and human‑on‑the‑loop workflows
  • environments where AI generates or modifies infrastructure

It is relevant for:

  • operators
  • governance teams
  • workflow designers
  • engineering teams
  • auditors

Dependency mapping must be applied consistently across development, test, staging, and production environments.

Organizations should implement structured, repeatable processes to identify, document, maintain, and review AI dependencies, ensuring dependency maps remain accurate, current, and operationally useful.

5.3.4 Rationale

Dependency mapping requires ongoing maintenance because:

  • AI systems evolve rapidly
  • workflows change frequently
  • new AI‑to‑AI interactions emerge over time
  • infrastructure dependencies shift as environments scale
  • human roles and approval paths change
  • circular dependencies may appear after updates
  • AI‑generated infrastructure introduces new dependencies automatically

Without structured implementation guidance, dependency maps become outdated and lose continuity value.

5.3.5 Implementation Guidance

The following guidance describes how organizations can operationalize dependency mapping in a consistent, auditable manner.

5.3.5.1 Preconditions

Before implementing dependency mapping, organizations should:

  • define dependency categories (AI‑to‑AI, AI‑to‑human, etc.)
  • establish a standard mapping format or template
  • identify authoritative sources for workflow and system state
  • ensure dependency maps can be stored and versioned
  • train operators on how to identify and document dependencies

5.3.5.2 Scope & Impact Analysis

Dependency mapping must include:

  • AI‑to‑infrastructure dependencies
  • AI‑to‑AI dependencies
  • AI‑to‑human dependencies
  • human‑to‑AI dependencies
  • circular dependencies
  • dependencies created by AI‑generated infrastructure

Impact analysis should evaluate:

  • how dependency changes affect workflows
  • how new AI systems alter dependency chains
  • how infrastructure migrations affect AI behavior
  • how human approval paths influence continuity

5.3.5.3 Standards Alignment

Dependency mapping should align with:

  • continuity and resilience planning
  • workflow governance
  • identity and access governance
  • change management processes
  • audit and compliance requirements

Acceptable enforcement methods include:

  • version‑controlled documentation
  • workflow‑integrated mapping steps
  • automated dependency scanners
  • architecture review gates

5.3.5.4 Trust Relationship Evaluation

Dependency maps must document:

  • trust boundaries between AI systems
  • human approval paths
  • cross‑AI validation patterns
  • escalation paths and fallback mechanisms

Trust relationships should be reviewed during:

  • architecture reviews
  • workflow design sessions
  • incident postmortems

5.3.5.5 Privilege Escalation Assessment

Dependency mapping must identify:

  • when AI systems indirectly escalate privileges
  • when AI‑to‑AI interactions expand blast radius
  • when human approvals become dependency bottlenecks
  • when AI‑generated infrastructure introduces new privileged paths

5.3.5.6 Automated Validation

Organizations should use automated tools to:

  • detect new or changed dependencies
  • identify circular dependencies
  • validate workflow and system relationships
  • detect AI‑generated infrastructure changes
  • compare current state to previous versions

Tools may include:

  • dependency graph analyzers
  • workflow simulators
  • architecture scanners
  • IaC validation tools

5.3.5.7 Human Review Requirements

Human review is required when:

  • new AI systems are introduced
  • workflows are modified
  • infrastructure changes occur
  • autonomy levels or permissions change
  • new AI‑to‑AI interactions are added
  • human approval paths change

Reviewers must:

  • validate dependency accuracy
  • confirm no circular dependencies exist
  • ensure fallback paths remain intact
  • document any deviations

5.3.5.8 Downstream Impact Analysis

Dependency mapping must evaluate:

  • continuity impacts
  • workflow impacts
  • identity and access impacts
  • monitoring and logging impacts
  • AI‑to‑AI propagation effects

This analysis should be included in:

  • change requests
  • deployment reviews
  • architecture reviews

5.3.5.9 Documentation Requirements

Organizations must document:

  • all identified dependencies
  • version history of dependency maps
  • changes introduced by new workflows or AI systems
  • AI‑generated infrastructure and its dependencies
  • review results and validation notes

Dependency maps must be:

  • stored in a durable, version‑controlled repository
  • accessible to operators and governance teams
  • included in continuity planning and risk assessments

5.3.6 Expected Outcomes

Organizations should expect:

  • accurate, current dependency maps
  • improved visibility into AI‑to‑AI and AI‑to‑human relationships
  • earlier detection of circular dependencies
  • improved continuity and resilience planning
  • better audit readiness
  • consistent mapping across teams and environments

5.3.7 Examples

Example 1 — AI Remediation Workflow

A remediation AI depends on:

  • monitoring AI for alerts
  • infrastructure APIs for actions
  • human approval for high‑risk steps

Example 2 — AI Deployment Pipeline

An AI code generator produces IaC that is validated by another AI before deployment.

Example 3 — Human Approval Path

A workflow requires human approval for AI‑generated changes; this is documented as a human‑to‑AI dependency.

Example 4 — Infrastructure Dependency

An AI system relies on a specific database cluster; the dependency is updated after a migration.

5.3.8 Notes

  • Dependency maps must be stored in a durable, version‑controlled location.
  • Maps must be updated after incidents, outages, or workflow redesigns.
  • Dependency mapping is foundational for Sections 6, 7, 8, and 9.
  • This document is a prerequisite for 5.4 Examples.

5.3.9 Cross‑References

Internal AI-OGF Controls

  • 5.2 Dependency Mapping Requirements
  • 5.4 Examples
  • 6.2.1 Define Autonomy Levels
  • 6.2.2 Define Decision Authority
  • 6.2.3 Define Permission Scope
  • 6.2.4 Require Escalation Triggers
  • 6.3 Implementation Guidance

External Standards (Informative References — To Be Developed)

Cross‑framework mappings to NIST CSF, NIST SP 800‑53, ISO/IEC 27001, ISO/IEC 42001, CIS Controls, and SOC 2 will be added in a future AI-OGF Annex.

This document is part of the AI Operational Governance Framework (AI-OGF) and is protected under the AI-OGF Limited Use License. Official source: https://rmanthey-mantheyco.github.io/ai-ogf/

This site uses Just the Docs, a documentation theme for Jekyll.