Replacing manual sample testing and static spreadsheet audits with programmatically enforced compliance pipelines. Specializing in automated evidence collection, NIST AI Risk Management Framework (AI RMF) mapping, EU AI Act risk classification, and Continuous Controls Monitoring (CCM) via custom Python, Webhooks, and API integrations.
Combining traditional enterprise risk management with modern dev-sec-ops automated compliance pipelines.
Demonstrating functional automation tools built to remove manual effort from GRC and AI Compliance workflows.
Visualizing how continuous control monitoring replaces point-in-time sampling (Mock Data Used).
Example of an automated script output checking IAM and infrastructure controls in real-time (SOC 2 CC6.1 / ISO 27001 A.9.2.1).
Real-world scenarios demonstrating risk reduction, framework implementation, and process engineering.
Designed and deployed an end-to-end AI governance architecture for evaluating third-party and internally built machine learning models prior to production release.
Business units were adopting generative AI tools rapidly without centralized oversight, exposing the organization to data leakage, IP risk, and unvetted algorithmic bias.
Created a standardized AI Model Intake process mapped to NIST AI RMF 1.0. Developed automated Python scripts to validate model cards, data provenance, and security controls.
Reduced AI system review time from 3 weeks to 48 hours while achieving 100% visibility over deployed organizational models.