AIOGF‑SD‑9.2.1 — AI‑Dependent Recovery Paths

Document Identifier: AIOGF‑SD‑9.2.1
Related Control: 9.2.1
Framework: AI Operational Governance Framework (AIOGF)
Author: Randy Manthey
Version: 1.6
Date: March 24, 2026
Status: Working Draft
© 2025–2026 Randy Manthey. All Rights Reserved.

Licensing & Usage Notice (Template v1.6)

[Same as template]

9.2.1.1 Purpose of the Practice

The purpose of this practice is to define how recovery paths that rely on AI systems must be governed, validated, and monitored.
AI‑dependent recovery paths introduce unique risks because the AI system may be degraded, unavailable, or contributing to the outage.

9.2.1.2 Scope & Applicability

This practice applies to:

AI‑driven remediation
AI‑assisted provisioning
AI‑based configuration repair
AI‑dependent orchestration pipelines
AI systems that maintain infrastructure or identity

9.2.1.3 Recommended Practice Statement

Organizations should identify, validate, and continuously monitor all recovery paths that depend on AI systems to ensure they remain safe, predictable, and executable during outages.

9.2.1.4 Rationale

AI‑dependent recovery paths may fail when:

the AI system is offline
the model is corrupted
identity or permissions are unavailable
telemetry is missing
cross‑AI dependencies form loops

Without governance, AI‑dependent recovery becomes a single point of failure.

Foundational Principle

Recovery paths that rely on AI must be explicitly validated and must not be the sole method of restoring critical systems.

9.2.1.5 Implementation Guidance

Organizations should:

Identify all AI‑dependent recovery steps
Validate that AI systems remain available during outages
Ensure fallback paths exist
Monitor AI health during recovery
Define breakpoints where AI must be removed from the loop

9.2.1.5.1 Preconditions

AI dependency mapping
workflow depth limits
telemetry retention
identity fallback procedures

9.2.1.5.2 Scope & Impact Analysis

Evaluate:

which recovery steps require AI
whether AI is safe to use during outages
whether AI contributes to the failure

9.2.1.5.3 Standards Alignment

Aligns with:

operational continuity
risk management
separation of duties

9.2.1.5.4 Trust Relationship Evaluation

Evaluate:

AI systems with elevated privileges
AI systems that maintain their own dependencies

9.2.1.5.5 Privilege Escalation Assessment

Assess:

whether AI gains unintended privileges during recovery

9.2.1.5.6 Automated Validation

Automated systems should:

detect AI unavailability
validate AI health
block unsafe AI‑dependent recovery steps

9.2.1.5.7 Human Review Requirements

Human review is required when:

AI is degraded
AI behavior is anomalous
recovery involves privileged systems

9.2.1.5.8 Downstream Impact Analysis

Evaluate:

impact on identity
impact on automation
impact on cross‑AI workflows

9.2.1.5.9 Documentation Requirements

Document:

AI dependencies
validation results
fallback paths
recovery outcomes

9.2.1.6 Business Impact

Failure to govern AI‑dependent recovery paths may result in:

failed recovery
extended outages
cascading failures
loss of critical infrastructure

9.2.1.7 Expected Outcomes

Organizations should expect:

validated AI‑dependent recovery
predictable behavior
reduced risk of recovery failure

9.2.1.8 Examples

Example 1 — AI‑Driven Remediation

AI attempts to repair a configuration but is offline.
Fallback path executes manually.

Example 2 — AI‑Maintained Identity

AI cannot restore identity policies.
Human‑controlled identity recovery is used.

9.2.1.9 Alignment to External Frameworks

NIST AI RMF — Manage (extension)
ISO/IEC 42001 — Operational Control (extension)

9.2.1.10 Notes

AI‑dependent recovery must never be the only recovery path.

9.2.1.11 Cross‑References

Internal AI-OGF Controls:

9.2.2 AI‑Independent Fallback Paths
9.2.3 Circular Dependency Breakpoints

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/