Related Insights
Governance & Risk Framework
EisnerAmper's Six-Pillar AI Risk Management Framework ensures responsible AI adoption at every stage. Governance isn't a checkpoint — it's embedded in everything we design, build, and scale.
Align AI initiatives with business strategy, risk appetite, and organizational readiness before development begins.
Establish data quality, lineage, privacy, and access controls that underpin trustworthy AI systems and meet regulatory requirements.
Validate, monitor, and document AI models with bias testing, explainability requirements, and performance drift detection.
Implement security-by-design principles, adversarial testing, and privacy-preserving techniques aligned with OWASP AI guidelines.
Navigate evolving AI regulations with frameworks mapped to NIST AI RMF, ISO 42001, and industry-specific requirements.
Establish ongoing oversight with automated monitoring, audit trails, incident response protocols, and stakeholder reporting.
AI risk assessment, stakeholder impact analysis, ethical review, data governance requirements, and regulatory mapping before any build begins.
Model validation checkpoints, bias testing protocols, security reviews, privacy impact assessments, and documentation standards enforced at every sprint.
Production readiness reviews, continuous monitoring dashboards, incident response playbooks, audit trail verification, and compliance certification.
Related Insights
