We design and implement governance structures that let your organization move fast with AI while managing risk, ensuring accountability, and maintaining public trust.
Our framework covers every dimension of responsible AI deployment
Clear ownership structures for AI decisions. RACI matrices for model development, deployment, and monitoring. Escalation pathways when AI systems underperform or produce unexpected outputs.
Explainability requirements matched to risk level. Documentation standards for model cards, data lineage, and decision logic. Stakeholder-appropriate disclosure frameworks.
Bias testing protocols across protected classes. Disparate impact assessments for high-stakes decisions. Ongoing monitoring to detect model drift and fairness degradation.
Data minimization principles embedded in AI design. Privacy impact assessments for new AI deployments. Security controls aligned with NIST CSF and Zero Trust architecture.
Human-in-the-loop requirements for high-stakes decisions. Override mechanisms and escalation protocols. Regular human review of automated decision outputs and error analysis.
Audit trails for model training, validation, and deployment decisions. Regulatory mapping to NIST AI RMF, EO 14110, GDPR Article 22, and sector-specific requirements.
Our governance frameworks map directly to the standards your regulators, auditors, and partners expect
From discovery to ongoing operations in four phases
Inventory AI systems, map risk levels, identify regulatory obligations, and benchmark against current governance maturity.
Develop policies, standards, and procedures tailored to your risk profile, sector requirements, and organizational culture.
Embed governance controls into your AI development lifecycle, train teams, and stand up monitoring infrastructure.
Quarterly reviews, annual audits, regulatory update tracking, and ongoing advisory to keep your governance current.
Every organization that deploys AI needs a governance foundation. Let's build yours before you need it.