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Leonda MaddenHealthcare Data & AI

AI Governance / Healthcare Technology

Healthcare AI Governance & Risk Platform

A governance framework and technology solution designed to help healthcare organizations inventory AI systems, evaluate risk, document controls and monitor responsible AI implementation.

In ProgressAll data shown is synthetic or independently generated for demonstration. No PHI is used.
AI GovernanceRisk ManagementData GovernanceRBACAudit LoggingHealthcare PrivacyResponsible AI

The Business Problem

As AI adoption accelerates, healthcare organizations need a structured way to know which AI systems are in use, what risks they carry and which controls are in place.

The Vision

A single AI inventory and risk register with documented controls, ownership and monitoring for responsible AI in healthcare.

My Role

Solution ArchitectureBusiness RequirementsSecurity DesignProduct StrategyStakeholder Discovery

Challenges

Changing Requirements
Multiple Stakeholders
Security Requirements
User Adoption

The Solution

Detailed solution narrative in development.

Inside the Solution

Dashboard, application and admin screenshots will appear here.

Behind the Build

A polished interface is only the visible layer. This section shows the data, architecture, engineering and design decisions behind the finished solution.

SQL, Python, data model, pipeline and repository screenshots will appear here.

Solution Architecture

  1. 1AI System Intake
  2. 2Inventory & Classification
  3. 3Risk Assessment
  4. 4Control Documentation
  5. 5Monitoring & Review
  6. 6Governance Committee

Technology Stack

Security

  • RBAC
  • Audit Logging

AI

  • AI Governance
  • Responsible AI

Security, Privacy & Governance

Healthcare and AI solutions require more than functionality. They require deliberate attention to privacy, security, access, governance and responsible use.

Role-Based Access Control
Audit Logging
AI Governance
Human-in-the-Loop Review
Model Monitoring

Designed using HIPAA-aligned privacy and security principles.

Testing & Data Quality

  • User acceptance testing
  • Business-rule testing

Outcome

Demonstrated / Simulated Outcome

Outcome documentation is in progress. Results will be published here once validated against the demonstration dataset.

What I Would Build Next

  • Alignment mapping to emerging AI regulations
  • Automated model monitoring alerts