React + TypeScript portfolio project demonstrating model criticality, release gating, evaluation drift, policy exceptions, review backlog pressure, and executive AI risk visibility.

Recruiter takeaway: "This person understands AI governance as enterprise risk oversight, not just prompt experiments and dashboards."

Model Risk Oversight Hub is a recruiter-ready frontend project built to feel like a real internal oversight workspace for organizations running AI in production. Instead of framing governance as a policy archive or evaluation spreadsheet, it turns release gates, drift signals, policy exceptions, and reviewer backlog into one command surface that product, legal, security, and executive stakeholders can all read.

This repo is designed to show that model governance becomes more useful when it behaves like an operating system for risk decisions, not a static checklist.

Model risk becomes difficult to manage when release gates, evaluation findings, and policy exceptions are scattered across tools and teams. That creates familiar problems:

• critical models ship under inconsistent release standards
• evaluation drift is seen too late
• exceptions age in high-risk lanes
• executives can see volume, but not where decisions are stuck

Teams need one workspace that makes AI risk legible, actionable, and operationally accountable.

This project reframes model governance as an operator-grade product surface for:

• model inventory and criticality
• release gating and approval pressure
• evaluation drift visibility
• policy exception tracking
• board-readable AI risk posture

flowchart LR
    A["Model inventory"] --> B["Typed frontend data model"]
    C["Evaluation signals"] --> B
    D["Policy exceptions and reviews"] --> B
    B --> E["Oversight shell"]
    E --> F["Risk trend analytics"]
    E --> G["Executive spotlight cards"]
    E --> H["Review queue"]
    E --> I["Gate pressure view"]
    E --> J["Criticality and findings comparison"]

1. Stakeholders land on one oversight posture surface.
2. Trends show whether evaluations are improving and whether exception pressure is falling.
3. Spotlight cards translate risk into leadership-readable language.
4. The review queue shows what needs release, exception, or reviewer decisions next.
5. Domain comparison exposes where criticality and findings overlap most dangerously.

• frontend execution grounded in enterprise AI governance reality
• ability to turn risk, review, and release concepts into usable product structure
• strong internal-tool UX thinking for multi-stakeholder workflows
• broader portfolio range across AI ops, identity, security, compliance, and revenue systems

• Node.js 20+
• npm

git clone https://github.com/mizcausevic-dev/model-risk-oversight-hub.git
cd model-risk-oversight-hub
npm install
cp .env.example .env
npm run dev

Open:

• http://localhost:5173

npm test

npm run build

• model governance and AI oversight understanding
• release and exception workflow design
• executive-facing risk product thinking
• React + TypeScript delivery with production-minded repo hygiene
• portfolio depth beyond prompt demos and chatbot wrappers

• model-card drilldowns and evidence packages
• review assignment and escalation routing
• evaluation scenario diffing
• deployment history overlays
• risk forecasting across release trains

• LinkedIn
• Skills Page
• Medium
• GitHub

Part of mizcausevic-dev's GitHub portfolio — demonstrating enterprise AI oversight product thinking, executive-readable governance UX, and release-aware model risk workflows.