AIOGF‑SD‑5.4 — Examples

Document Identifier: AIOGF‑SD‑5.4
Related Control: 5.4
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.

5.4.1 Purpose of the Practice

The purpose of this document is to provide practical, real‑world examples that illustrate how AI dependency mapping is applied across operational environments. These examples demonstrate:

  • AI‑to‑infrastructure dependencies
  • AI‑to‑AI dependencies
  • AI‑to‑human dependencies
  • human‑to‑AI dependencies
  • circular dependencies

These examples support understanding of the requirements in Section 5.2 and the implementation guidance in Section 5.3, helping organizations visualize, validate, and document their own dependency structures.

5.4.2 Scope & Applicability

This supplement applies to:

  • operational workflows involving AI systems
  • 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, modifies, or depends on infrastructure or automation

It is relevant for:

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

Organizations may extend these examples with environment‑specific scenarios.

Organizations should use real‑world examples to validate, train, and refine their AI dependency mapping processes, ensuring operators and governance teams can recognize AI‑to‑AI, AI‑to‑human, and circular dependencies in practice.

5.4.4 Rationale

Examples are required because:

  • dependency chains can be complex and difficult to visualize
  • AI‑to‑AI interactions may not be obvious without concrete scenarios
  • circular dependencies often emerge unintentionally
  • operators benefit from seeing real‑world patterns
  • governance teams require reference models for audits and reviews

Examples ensure dependency mapping is understood not only conceptually but also practically.

5.4.5 Implementation Guidance

Organizations should use examples to:

  • train operators on identifying dependency types
  • validate dependency maps during quarterly reviews
  • identify hidden or circular dependencies
  • test workflow resilience and continuity
  • support onboarding for governance and engineering teams
  • create internal reference libraries of common dependency patterns

Examples should be updated as new AI‑enabled workflows emerge.

5.4.5.1 Preconditions

Before using examples for training or validation, organizations should:

  • have a defined dependency mapping methodology (Section 5.2)
  • have implementation guidance in place (Section 5.3)
  • ensure operators understand dependency categories
  • maintain current dependency maps for comparison

5.4.5.2 Scope & Impact Analysis

Examples should be used to analyze:

  • how AI systems depend on infrastructure, humans, and other AI
  • how workflow changes affect dependency chains
  • how circular dependencies form and how to resolve them
  • how AI‑generated outputs create new dependencies automatically

5.4.5.3 Standards Alignment

Examples support alignment with:

  • dependency mapping requirements
  • continuity and resilience planning
  • workflow governance
  • identity and access governance
  • audit and review processes

5.4.5.4 Trust Relationship Evaluation

Examples should highlight:

  • trust boundaries between AI systems
  • human approval paths
  • cross‑AI validation patterns
  • escalation loops and failure modes

5.4.5.5 Privilege Escalation Assessment

Examples should help identify:

  • when AI systems indirectly escalate privileges
  • when AI‑to‑AI interactions expand blast radius
  • when human approvals become dependency bottlenecks

5.4.5.6 Automated Validation

Examples should be used to test:

  • dependency graph analyzers
  • workflow simulators
  • AI‑to‑AI interaction validators
  • circular dependency detection tools

5.4.5.7 Human Review Requirements

Examples should be used to train reviewers to:

  • identify missing dependencies
  • detect circular or hidden dependencies
  • validate AI‑generated dependency maps
  • understand escalation paths

5.4.5.8 Downstream Impact Analysis

Examples should help teams evaluate:

  • how dependency changes affect continuity
  • how AI‑generated infrastructure affects dependency chains
  • how human approvals influence workflow timing
  • how AI‑to‑AI interactions propagate across systems

5.4.5.9 Documentation Requirements

Organizations should document:

  • example scenarios used for training
  • dependency patterns identified
  • lessons learned
  • updates required to dependency maps
  • environment‑specific variations

5.4.6 Expected Outcomes

Organizations should expect:

  • improved operator understanding of dependency structures
  • more accurate and complete dependency maps
  • earlier detection of circular dependencies
  • improved audit readiness
  • better continuity planning
  • clearer understanding of AI‑to‑AI and AI‑to‑human relationships

5.4.7 Examples

Example 1 — AI Remediation Workflow

A remediation AI depends on:

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

Demonstrates AI‑to‑AI, AI‑to‑infrastructure, and AI‑to‑human dependencies.

Example 2 — AI Deployment Pipeline

An AI code generator produces infrastructure as code that is:

  • validated by a second AI
  • approved by a human reviewer
  • deployed by an automation engine

Demonstrates multi‑layer AI‑to‑AI dependencies and human approval paths.

Example 3 — Human Approval Dependency

A workflow requires human approval for AI‑generated configuration changes. The dependency map documents:

  • AI‑to‑human approval
  • human‑to‑AI context injection
  • escalation paths when approval is delayed

Demonstrates human‑to‑AI and AI‑to‑human dependencies.

Example 4 — Infrastructure Dependency

An AI system relies on:

  • a specific database cluster
  • a message queue
  • a monitoring endpoint

When the database cluster is migrated, the dependency map must be updated.

Example 5 — Circular Dependency

A workflow stalls because:

  • AI requires human approval
  • the human requires AI‑generated context
  • the context cannot be generated until approval is granted

Demonstrates a circular dependency requiring redesign.

Example 6 — AI‑Generated Infrastructure

An AI system generates Terraform code that:

  • provisions new infrastructure
  • is validated by another AI
  • becomes part of the dependency chain

Demonstrates how AI‑generated outputs create new dependencies automatically.

Example 7 — Escalation Loop

AI escalates an anomaly to a human operator, but the operator:

  • relies on AI to analyze the anomaly
  • cannot proceed without AI context

Creates a mutual escalation loop.

5.4.8 Notes

  • Examples should be updated as new AI workflows are introduced.
  • Organizations may add environment‑specific examples for internal training.
  • Examples should be reviewed during quarterly dependency map validation cycles.
  • This document supports understanding of Sections 5.2 and 5.3.

5.4.9 Cross‑References

Internal AI-OGF Controls

  • 5.2 Dependency Mapping Requirements
  • 5.3 Dependency Mapping Implementation Guidance
  • 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/

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