AI Risk Screener is a practical assessment tool that helps organizations identify potential AI risk areas, highlight governance gaps, and assess evidence readiness in line with the EU AI Act.
The tool guides users through a structured questionnaire about an AI use case, including:
- organizational context
- intended use
- affected people
- data and privacy considerations
- decision impact
- human oversight
- governance and evidence
Based on the answers, it provides:
- risk level
- risk score
- key findings
- improvement areas
- focused recommendations for critical issues
- evidence and documentation view
- maturity profile in the report
This version is designed as a screening and prioritization tool. It helps structure internal review and identify areas that may need further attention.
It does not provide legal advice or a formal compliance determination.
- Use Case Overview
- Risk Classification
- Key Findings
- Improvement Areas
- Recommended Actions
- Evidence & Documentation
- Maturity Profile
- React
- Vite
- Tailwind CSS
- Rules-based assessment engine
- Azure Static Web Apps deployment
The current version represents an initial MVP focused on structured AI risk screening and clear, explainable outputs. Further development is planned in the following areas:
The tool currently uses a focused subset of questions (11 out of 30 defined).
The full questionnaire will be expanded step by step to improve coverage and accuracy across different AI use cases.
This includes:
- gradual activation of additional questions
- validation and refinement of combinations
- testing with real-world use cases
This phase is intended to be supported by structured testing and user feedback, including external testers.
The report will be further improved to provide clearer and more actionable insights.
Planned improvements include:
- more refined UI and structure of the report
- clearer separation of positive findings and improvement areas
- prioritization of key findings
- highlighting of critical risk signals
- more targeted and context-aware recommendations
The goal is to make the output easier to interpret and more useful for decision-making across business, risk, and technical teams.
These improvements aim to strengthen the practical usability and reliability of the tool, particularly for organizations that currently lack structured AI governance processes.