Category
AI Model Risk Management
AI model risk management is the discipline of identifying, measuring, monitoring, and mitigating risks created by AI systems. For enterprise LLMs and agents, model risk extends beyond the model itself. Risk can appear in prompts, retrieval systems, tools, workflows, memory, orchestration logic, and production operations.
Traditional model risk practices often focus on validation, performance measurement, documentation, and governance. LLM and agent systems add new challenges: open-ended outputs, non-deterministic behavior, rapid model upgrades, prompt injection, retrieval dependency, tool use, and user-specific context.
A failure-mode approach helps make AI model risk operational. It translates abstract risk into specific patterns that can be detected, monitored, scored, and mitigated.
FailureModes.ai helps enterprise teams connect AI reliability with governance. Teams can build taxonomies, map controls to failure modes, monitor production behavior, track severity, and provide clearer reporting to engineering, security, risk, compliance, and executive stakeholders.
In scope
Risks a failure-mode program tracks
Hallucination
Hallucination and unsupported claims.
Retrieval failure
Retrieval failure and stale context.
Tool misuse
Tool misuse and invalid API calls.
Refusal drift
Refusal drift after model or prompt changes.
Data leakage
Data leakage and policy violations.
Cascading agent failures
Local errors propagating into workflow failures.
Cost runaway
Cost runaway and excessive retries.
Evaluation blind spots
Production failures missed by current eval coverage.
Where FailureModes.ai fits
FailureModes.ai gives risk, governance, and engineering teams a shared, operational view of AI risk: a living taxonomy mapped to monitors, evals, severity scores, and mitigations.