An interactive tool for evaluating AI governance readiness in real-world environments.
Designed for CROs, CIOs, and CISOs who need a structured view of how AI risk, control, and accountability actually operate across the enterprise.
- Repository:
ai-assessment - Type: public portfolio prototype
- Runtime: static browser app opened from
index.html - Data posture: synthetic and illustrative only
- Primary audience: risk, governance, audit, and technology leaders evaluating AI governance readiness concepts
This repository is a conceptual AI governance risk assessment demo. It shows how governance readiness can be assessed through an in-browser workflow and how assessment outputs can connect to system-level control concepts such as TrustLayer.
- Not a production application.
- Not connected to real client, employer, or regulated institution data.
- Not a complete proprietary assessment methodology.
- Not a backend service or data collection system.
- Not a place to enter confidential, personal, client, or employer information.
- Do not assume this repository has a production backend; it runs entirely in the browser.
- Do not treat assessment content as real organizational evidence.
- Do not confuse this assessment prototype with TrustLayer, which demonstrates control and enforcement.
- Do not add secrets, API keys, or telemetry credentials to the repository.
Keep changes demo-safe and portfolio-safe. Preserve the synthetic-data boundary, avoid inventing regulated-client facts, and keep the README accessible to non-developer reviewers.
Most AI governance conversations focus on frameworks, policies, and principles.
This tool explores a more practical question:
How ready is an organization to govern AI systems in practice?
It provides a structured way to assess governance maturity across key domains that regulators, auditors, and risk leaders care about — while connecting those gaps to how systems are actually controlled.
A three-stage workflow that produces a governance report in under 15 minutes:
-
Risk Screener
Capture institution profile, AI maturity, and regulatory context -
Deep Assessment
Score 40+ questions across core governance domains, dynamically adjusted based on profile -
Governance Report
Generate a structured output including:- executive summary
- domain-level scoring
- regulatory gap analysis
- prioritized recommendations
- 90-day roadmap
- TrustLayer integration guidance
Aligned to iTechLaw RAIIA and major regulatory expectations:
- AI Strategy & Governance
- Data Governance & Quality
- Model Risk & Validation
- Transparency & Explainability
- Fairness & Bias Mitigation
- Security & Adversarial Robustness
- Human Oversight & Accountability
- Regulatory Compliance & Reporting
As AI systems move from experimentation to operation:
- risk is not limited to model performance
- governance must extend beyond documentation
- control depends on how systems are designed and managed in practice
Assessment is often the first step.
But without a clear link to how systems actually behave, it remains incomplete.
This framework focuses on evaluation.
TrustLayer focuses on control and enforcement.
Together, they explore:
- how governance gaps are identified
- how those gaps can be addressed at the system level
Explore how this translates into system-level control:
👉 TrustLayer Demo
- Vanilla HTML / CSS / JavaScript
- Chart.js via CDN
- Runs entirely in-browser (no data transmitted)
Open index.html in any modern browser.
This is a conceptual prototype intended to explore how AI governance and risk assessment translate into real-world system design and operation.
This repository is a public portfolio demonstration. Assessment content is synthetic and illustrative, runs in the browser, and is not connected to production systems.
Do not submit confidential, client, employer, or personal information into the demo. No secrets or credentials are intended to be stored in this repository.