AIOGF‑SD‑5.2.1 — Map AI → Infrastructure

Document Identifier: AIOGF‑SD‑5.2.1
Related Control: 5.2.1
Framework: AI Operational Governance Framework (AIOGF)
Framework Version: 0.9 (Draft)
Document Version: 1.6
Author: Randy Manthey Date: March 2026
Status: Working Draft
© 2025–2026 Randy Manthey. All Rights Reserved.

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5.2.1.1 Purpose of the Practice

The purpose of this practice is to identify, document, and govern all physical and logical infrastructure components that an AI system depends on to operate. This includes compute, storage, networking, orchestration layers, identity systems, and environmental controls.

This mapping enables continuity planning, blast‑radius reduction, isolation strategies, dependency risk assessment, circular dependency detection, and recovery path validation. Without a complete infrastructure map, organizations cannot evaluate the continuity risks associated with AI‑enabled workflows.

5.2.1.2 Scope & Applicability

This practice applies to:

All AI systems deployed in cloud, on‑premises, hybrid, or edge environments
Compute, storage, network, identity, orchestration, and environmental layers
Hosted‑mode AI, agentic systems, workflow‑embedded AI, and model‑serving platforms
Infrastructure supporting training, inference, monitoring, deployment, and remediation
Systems where infrastructure failures may cause AI outages or cascading workflow failures

This practice is required before mapping AI → AI or AI → Humans, as it establishes the foundational dependency layer.

5.2.1.3 Recommended Practice Statement

Organizations must document and govern all AI‑to‑infrastructure dependencies to ensure AI systems remain resilient, recoverable, and protected from cascading failures across shared infrastructure components.

5.2.1.4 Rationale

AI systems rely on complex, multilayered infrastructure stacks. Failures in any layer — from GPUs to DNS — can disrupt AI operations. Mapping these dependencies:

exposes hidden single points of failure
reveals shared failure domains
identifies infrastructure components requiring redundancy
supports AI isolation and air‑gapping
enables accurate continuity planning
prevents cascading failures across AI systems

This mapping is essential for safe, predictable, and resilient AI operations.

5.2.1.5 Implementation Guidance

Organizations must implement a structured, repeatable process to identify, document, validate, and govern all AI‑to‑infrastructure dependencies. This includes:

Using automated discovery tools where possible
Updating dependency maps after every major change
Including AI‑generated infrastructure (IaC) in the mapping
Validating that shared infrastructure does not create circular dependencies
Maintaining separate maps for dev, test, and production
Storing dependency maps in version‑controlled repositories
Integrating dependency maps into continuity planning workflows

5.2.1.5.1 Preconditions

Before implementing this practice, organizations must have:

An inventory of infrastructure components
Defined AI system boundaries and deployment models
Access to infrastructure topology and configuration data
Monitoring and observability for infrastructure health
A continuity model for infrastructure failures
A classification of critical vs. non‑critical components

5.2.1.5.2 Scope & Impact Analysis

For each AI system:

Identify all compute dependencies
CPU nodes, GPU accelerators, TPU/NPU hardware, virtualized clusters, serverless environments.
Document: location, redundancy level, failure domain, orchestration layer.
Identify all storage dependencies
Model weights, training data, inference data, logs, telemetry, state files, IaC artifacts.
Document: storage type, replication strategy, metadata controllers, encryption dependencies.
Identify all network dependencies
L2/L3 paths, load balancers, firewalls, API gateways, service meshes, DNS, routing dependencies.
Identify all identity & access dependencies
IAM roles, service accounts, API keys, OAuth flows, certificate authorities.
Identify all orchestration dependencies
Kubernetes clusters, GPU schedulers, workflow engines, serverless orchestrators, container runtimes.
Identify environmental dependencies
Power, cooling, rack placement, data center zones, cloud availability zones.
Assess failure impact
Determine how each dependency affects AI continuity, safety, and workflow stability.

5.2.1.5.3 Standards Alignment

This practice aligns with:

Least privilege — infrastructure access must be scoped to AI needs
Separation of duties — infrastructure changes require human approval
Governance policies — infrastructure dependencies must be documented and auditable
Regulatory requirements — infrastructure supporting AI must meet compliance standards
Operational continuity — infrastructure failures must not cause uncontrolled AI outages

5.2.1.5.4 Trust Relationship Evaluation

Evaluate:

Whether AI systems implicitly trust infrastructure components
Whether infrastructure components rely on AI‑generated configuration
Whether trust crosses system, zone, or tenant boundaries
Whether infrastructure dependencies create shared failure domains
Whether trust relationships change after updates or redeployments

Trust relationships must be explicit, validated, and continuously monitored.

5.2.1.5.5 Privilege Escalation Assessment

AI‑to‑infrastructure dependencies can create privilege escalation risks when:

AI systems generate infrastructure (IaC) consumed by privileged systems
AI systems inherit permissions through orchestration layers
AI systems access identity systems with broad authority
Infrastructure components trust AI‑generated configuration without validation

Organizations must detect:

Direct escalation (AI modifies infrastructure directly)
Indirect escalation (AI influences privileged systems through IaC)
Lateral escalation (AI actions affect adjacent infrastructure components)

5.2.1.5.6 Automated Validation

Automated checks must verify:

Infrastructure dependencies are documented and authorized
Redundancy and replication strategies meet continuity requirements
Identity dependencies are valid and scoped appropriately
Network paths are monitored and resilient
IaC‑generated infrastructure is validated before deployment
Environmental dependencies meet operational thresholds

5.2.1.5.7 Human Review Requirements

Human review is required when:

Infrastructure changes affect AI systems
AI‑generated infrastructure is deployed
Identity or access dependencies change
Shared failure domains are introduced
Environmental dependencies shift (e.g., zone migration)
New orchestration layers or schedulers are added

Reviewers must have:

Visibility into infrastructure topology
Context for AI system dependencies
Authority appropriate to the risk level

5.2.1.5.8 Downstream Impact Analysis

Organizations must evaluate how infrastructure dependencies affect:

Workflow continuity
AI model availability
Monitoring and observability
Identity and access boundaries
Other AI systems sharing infrastructure
Escalation chains and fallback behavior

Infrastructure failures must not create hidden continuity or blast‑radius risks.

5.2.1.5.9 Documentation Requirements

Organizations must document:

All compute, storage, network, identity, orchestration, and environmental dependencies
Redundancy levels and failure domains
Shared infrastructure components
IaC‑generated infrastructure
Quarterly reviews of infrastructure dependencies
Updates after architectural changes or migrations

All documentation must be retained for auditability and continuity planning.

5.2.1.6 Expected Outcomes

When implemented correctly, organizations will observe:

Clear visibility into all AI‑to‑infrastructure dependencies
Reduced risk of cascading failures
Improved continuity and recovery planning
Stronger isolation and blast‑radius control
Fewer hidden single points of failure
More predictable and resilient AI operations

5.2.1.7 Examples

Example 1 — AI Inference System

Dependencies include:
GPU Cluster A, Object Storage Bucket B, API Gateway C, DNS Zone D, IAM Role E, Kubernetes Cluster F.

Example 2 — AI Remediation System

Dependencies include:
Monitoring Agent G, Log Storage H, Workflow Engine I, Service Mesh J, Certificate Authority K.

Example 3 — AI Deployment System

Dependencies include:
IaC Repository L, Build Pipeline M, Artifact Registry N, Deployment Orchestrator O, Network Firewall P.

5.2.1.8 Notes

Dependency maps must be reviewed quarterly or after major architectural changes.
Cloud environments require mapping across availability zones and regions.
AI systems with high autonomy require deeper dependency mapping.
Dependency maps must be accessible during outages.
This document is a prerequisite for 5.2.5 Identify Circular Dependencies.

5.2.1.9 Cross‑References

Internal AI-OGF Controls

5.2.2 — Map AI → AI
Infrastructure mapping is required before identifying AI‑to‑AI dependencies.
5.2.3 — Map AI → Humans
Infrastructure failures directly affect human oversight and intervention.
5.2.4 — Map Humans → AI
Human‑provided inputs may depend on infrastructure availability.
7.2.x — Workflow Continuity Controls
Infrastructure dependencies feed directly into continuity planning.

External Standards (Informative References — To Be Developed)

NIST CSF, NIST SP 800‑53, ISO/IEC 27001, ISO/IEC 42001, CIS Controls, SOC 2.

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/