Responsible, trustworthy, and auditable AI deployment — from policy to technical enforcement.
Three years at the forefront of enterprise AI governance — deploying Microsoft Copilot, an enterprise AI chatbot, and Agentic AI with RAG for a 40,000-person Fortune 500 firm, while building the governance framework that makes each deployment responsible, defensible, and compliant.
My AI governance frameworks follow the NIST AI Risk Management Framework (AI RMF 1.0) — organizing activities across four core functions. Each function is operationalized with policies, controls, and monitoring mechanisms aligned to the organization's risk profile and regulatory environment.
Policy, accountability, and risk tolerance for responsible AI deployment.
Identifying and categorizing AI risks across all use cases before deployment.
Controls and metrics to measure AI risk exposure and monitor adherence to policy.
Risk response plans, escalation pathways, and remediation workflows.
The primary US framework for managing AI risk across its lifecycle. My programs directly follow the Govern–Map–Measure–Manage structure, operationalizing each function with policies, controls, and monitoring. Aligns with OMB AI policy for federal environments.
The world's first binding AI regulation, requiring risk classification of AI systems and compliance obligations for high-risk systems. My governance frameworks incorporate risk classification methodology aligned to EU AI Act principles — including prohibited practices, high-risk system requirements, and transparency obligations.
Federal compliance frameworks governing how AI tools can access, process, and output CUI and sensitive data. My AI governance programs established data classification prerequisites and access controls ensuring AI tools operate within CMMC compliance boundaries at Fortune 500 firm in the Defense Industrial Base.
The international standard for AI management systems providing a systematic framework for responsible AI development, deployment, and monitoring. Governance structures I build incorporate ISO 42001 principles on transparency, explainability, and accountability.
End-to-end governance for M365 Copilot deployments — from data readiness assessment and sensitivity label prerequisites to acceptable use policy and monitoring. Deployed and governed Copilot for 40,000 users at a Fortune 500 firm, including data permissibility rules, prohibited use definitions, and executive reporting.
Specialized governance for Retrieval-Augmented Generation and agentic AI systems — addressing the unique risks of AI agents that retrieve, synthesize, and act on enterprise data. Led governance for RAG-based RFP response system, including data source boundaries, agent permissions, and output audit trail requirements.
Maintaining enterprise AI risk registers — tracking use case risk assessments, control gaps, remediation plans, and compliance status. Delivering AI risk posture reporting to executive and audit stakeholders on regular cadence with quantifiable metrics.
Governing enterprise adoption of AI tools through structured change management — acceptable use training, role-based communications, adoption measurement, and behavior tracking. Achieved 20% Help Desk demand reduction through AI-enabled automation governance.