risk
Insufficient AI resilience and fallback mechanisms
AI systems lacking fallback, redundancy, or graceful degradation fail catastrophically under adversarial conditions, infrastructure outages, or out-of-distribution inputs, disrupting dependent business processes.
In catalog since 2026-09-17T22:28:00Z · Last changed 2026-09-17T22:28:00Z (f368a6cce277)
Record JSON · Open in map · Data retrieval guide
Catalog revision: 791ff2dd3a45707290badee660f185e514d15f1cf518908628f425c2f2c56ee4. A connection does not establish full coverage.
Attributes
- category
- ai_governance
- domain
- AI Governance
- Business Continuity & Disaster Recovery
- taxonomy
- nist-ai-rmf-risk
- inherent_rating
- medium
Details
- risk_id
- ai-model-resilience-fallback-gap
- category
- ai_governance
- likelihood
- medium
- impact
- high
- inherent_rating
- medium
- treatment
- mitigate
- taxonomies
- nist-ai-rmf-risk
Source
No record-specific source URL is provided.
Connections
- UC-AI-07 — Verify, validate, and control AI deployment and changes mitigates Insufficient AI resilience and fallback mechanisms
- strength
- related
- rationale
- Acceptance testing can exercise robustness/fallback under out-of-distribution inputs before release.
- UC-AI-08 — Log and monitor AI system behavior in operation mitigates Insufficient AI resilience and fallback mechanisms
- strength
- related
- rationale
- Anomaly alerting and escalation trigger failure response before dependent business processes break.