Simor Consulting
Category: AI Governance
The guardrail problem in AI is a tension between two failure modes. Too few guardrails and the system produces harmful, inaccurate, or brand-damaging outputs. Too many guardrails and the system refuse
Bias in AI systems is not a theoretical risk. It is a measurable property that can be detected, quantified, and mitigated at every stage of the pipeline. The teams that treat bias as an audit problem
The regulatory focus on AI is narrowing from the models themselves to the data that trains them. The EU AI Act requires documentation of training data provenance and composition. The US Copyright Offi
A regional bank with $12 billion in assets wanted to use machine learning to improve its commercial loan underwriting process. The existing process was manual, relying on credit analysts who spent fou
Responsible AI is not a checklist you complete before deployment. It is a set of architectural decisions that you make throughout the design process, each of which involves trade-offs that are real an
The first enforcement window of the EU AI Act opened in February 2026, and the grace periods that protected early movers are expiring on a rolling schedule through 2027. This is no longer a policy dis
A healthcare system deployed an AI triage assistant. It worked well in testing. In production, it started routing patients with chest pain to low-priority queues. The error was subtle and infrequent.
# Metadata Management for AI Governance AI systems in production require metadata management to support compliance, auditing, and model oversight. Without systematic tracking of model lineage, traini