AIOGF‑SD‑4.5 — Dependency Awareness

Document Identifier: AIOGF‑SD‑4.5
Related Principle: 4.5
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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4.5.1 Purpose of the Practice

The purpose of this practice is to ensure organizations identify, document, and continuously monitor all dependencies that influence AI behavior, including infrastructure, data sources, humans, workflows, and other AI systems. Dependency awareness enables safe autonomy, prevents cascading failures, and supports continuity planning by ensuring AI systems never operate with hidden or unknown dependencies.

This principle establishes the foundation for all dependency‑mapping controls in the AI-OGF.

4.5.2 Scope & Applicability

This practice applies to:

All AI systems, agents, and automation pipelines
Infrastructure components (compute, storage, networking, identity)
Human inputs, approvals, context, and oversight
AI‑to‑AI interactions and multi‑AI workflow chains
Data pipelines, sensors, telemetry, and external APIs
Orchestration layers, workflow engines, and integration systems
Safety‑critical, compliance‑critical, and operationally sensitive environments

Dependency awareness is required for all autonomy levels and all deployment models.

4.5.3 Recommended Practice Statement

Organizations must identify, document, and continuously monitor all dependencies that influence AI behavior, ensuring no AI system operates with hidden, implicit, or ungoverned dependencies.

4.5.4 Rationale

AI systems rely on a complex network of dependencies:

infrastructure (compute, storage, networking, identity)
humans (context, approvals, constraints, oversight)
other AI systems (validation, summarization, remediation)
data sources (sensors, pipelines, external APIs)
workflows (orchestration, automation, escalation)

Without explicit dependency awareness:

AI may fail unpredictably
cascading failures may propagate across systems
workflow chains may exceed safe limits
autonomy boundaries may be violated
human oversight may be bypassed
circular dependencies may form
continuity planning becomes impossible

Dependency awareness is the prerequisite for safe autonomy, continuity, and governance.

4.5.5 Implementation Guidance

Organizations must implement a structured, repeatable process to identify, validate, and monitor dependencies across all AI systems. This includes:

mapping infrastructure dependencies
mapping AI‑to‑AI dependencies
mapping AI‑to‑Human dependencies
mapping Human‑to‑AI dependencies
mapping data and sensor dependencies
mapping workflow and orchestration dependencies
validating dependencies during design reviews
updating dependency maps after system changes
monitoring dependency health at runtime

4.5.5.1 Preconditions

Before implementing this practice, organizations must have:

an inventory of AI systems
an inventory of infrastructure components
defined workflow maps
identity‑bound authority for all AI actions
monitoring and telemetry for dependency health
escalation and fallback procedures
a continuity model for dependency failures

4.5.5.2 Scope & Impact Analysis

For each AI system:

Identify infrastructure dependencies
Compute, storage, networking, identity, orchestration, environmental controls.
Identify AI‑to‑AI dependencies
Direct calls, indirect dependencies, shared data, shared memory, emergent relationships.
Identify AI‑to‑Human dependencies
Approvals, oversight, validation, escalation, exception handling.
Identify Human‑to‑AI dependencies
Prompts, context, constraints, rules, training data, feedback.
Identify data dependencies
Sensors, pipelines, external APIs, telemetry, logs.
Identify workflow dependencies
Orchestration layers, workflow engines, automation pipelines.
Assess failure impact
How dependency failures affect autonomy, continuity, and safety.
Define monitoring requirements
Drift detection, anomaly detection, dependency health checks.

4.5.5.3 Standards Alignment

This practice aligns with:

Transparency — dependencies must be visible and documented
Least Autonomy — autonomy must respect dependency boundaries
Human Override — humans must intervene when dependencies fail
Isolation by Design — dependencies must not create unsafe coupling
Fail‑Safe Defaults — dependency failures trigger safe behavior
Workflow Layer Limits — dependency chains influence workflow depth
Constrained Autonomy Envelope (CAE) — CAE boundaries depend on dependency health
Identity‑Bound Authority — dependencies must not grant implicit authority

Dependency awareness is foundational to all continuity and safety standards.

4.5.5.4 Trust Relationship Evaluation

Evaluate:

whether AI trusts data sources without validation
whether AI trusts other AI systems implicitly
whether humans trust AI outputs without oversight
whether trust relationships cross system or vendor boundaries
whether trust changes after model updates or retraining

Trust must be explicit, validated, and continuously monitored.

4.5.5.5 Privilege Escalation Assessment

Dependencies may create privilege escalation risks through:

AI‑to‑AI workflow chaining
inherited authority from orchestration layers
misconfigured identity systems
shared data or memory systems
recursive or circular dependencies

Organizations must detect:

Direct escalation — AI uses dependencies to exceed authority
Indirect escalation — AI influences privileged systems
Lateral escalation — dependency failures affect adjacent workflows

4.5.5.6 Automated Validation

Automated checks must verify:

dependency maps are complete and current
dependency health is monitored continuously
drift indicators remain within safe thresholds
dependency failures trigger escalation or fallback
AI does not exceed CAE boundaries due to dependency behavior
workflow depth limits are enforced across dependency chains

4.5.5.7 Human Review Requirements

Human review is required when:

new dependencies are introduced
dependency failures occur
dependency boundaries are modified
AI requests actions dependent on degraded systems
circular or recursive dependencies are detected

Reviewers must have:

visibility into dependency maps
context for dependency behavior
authority appropriate to the risk level

4.5.5.8 Downstream Impact Analysis

Organizations must evaluate how dependencies affect:

workflow continuity
AI‑to‑AI interactions
human oversight and intervention
infrastructure stability
escalation and fallback behavior
CAE boundaries and autonomy levels

Dependency failures must not create hidden continuity risks.

4.5.5.9 Documentation Requirements

Organizations must document:

all infrastructure, human, AI, data, and workflow dependencies
dependency health and monitoring requirements
dependency failure modes and fallback behavior
quarterly reviews of dependency maps
updates after system changes or model retraining

All documentation must be retained for auditability and continuity planning.

4.5.6 Expected Outcomes

When implemented correctly, organizations will observe:

complete visibility into all AI dependencies
reduced risk of cascading failures
predictable and bounded AI behavior
improved continuity and recovery planning
safer AI‑to‑AI and AI‑to‑Human interactions
stronger alignment between AI behavior and human intent

4.5.7 Examples

Example 1 — AI Remediation System

AI depends on monitoring, logging, workflow engines, and identity systems; dependency failures trigger fallback.

Example 2 — Medical AI

AI depends on sensors, clinician input, and safety controllers; missing data triggers safe‑mode behavior.

Example 3 — Autonomous Vehicles

AI depends on sensor fusion, mapping systems, and safety controllers; sensor drift triggers escalation.

Example 4 — Facility Operations

AI depends on HVAC sensors, electrical load data, and environmental controls; dependency failures halt optimization.

4.5.8 Notes

Dependency maps must be reviewed quarterly.
Dependencies must be updated after system or model changes.
Dependency awareness is required for CAE and compensating controls.
Hidden dependencies are a primary cause of AI workflow failure.

4.5.9 Cross‑References

Internal AI-OGF Controls

4.1 Transparency — dependencies must be visible.
4.2 Least Autonomy — autonomy must respect dependency limits.
4.6 Fail‑Safe Defaults — dependency failures trigger safe behavior.
4.7 Workflow Layer Limits — dependency chains influence workflow depth.
4.8 Constrained Autonomy Envelope (CAE) — CAE boundaries depend on dependency health.
4.9 Compensating Controls — dependency failures must not compromise safety.
4.10 Identity‑Bound Authority — dependencies must not grant implicit authority.
5.2.x Dependency Mapping — this principle is the foundation for all 5.2.x controls.
6.2.x Autonomy Boundaries — autonomy depends on dependency stability.
7.2.x Continuity Controls — dependency failures drive continuity planning.

External Standards (Informative References — To Be Developed)

ISO 42001, NIST CSF, NIST SP 800‑53, IEC 61508.

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/