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hello@monomo.com
hello@monomo.com
Welcome to the AI Safety & Compliance Hub
This hub is for data scientists and compliance professionals focused on responsible AI design and governance.
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AI Risk Coverage Overview
We built our framework on a robust taxonomy that maps the landscape of AI-related risks across categories, domains, and sub-domains. This structure ensures comprehensive oversight and regulatory alignment.
Metric | Count |
Total Distinct AI Risks | 65 |
Total Risk Categories | 492 |
Total Risk Subcategories | 1,065 |
Total Risk Domains | 7 |
Total Risk Subdomains | 31 |
Start your self-assessment now to uncover risks, validate ethical safeguards, and prove your AI system is compliant with the European AI Act.
Our AI Safety and Ethic Framework
Risk Classification
We implement a dynamic, use-case-based risk tiering model fully aligned with the EU AI Act's high-risk and general-purpose AI categories.
- Supports continuous reassessment across the AI system lifecycle
- Flags risks related to data sensitivity, bias exposure, explainability gaps, and ethical impact
- Enables model segmentation by industry, use context, and decision-criticality
Evaluation & Benchmarking
Our framework embeds robust quantitative and qualitative evaluation layers designed for both general-purpose and domain-specific LLMs.
- Key metrics: fairness, robustness, explainability, ethical safety, output consistency
- Tools: LLM-as-a-Judge, automated scoring, and human-in-the-loop evaluations
- Custom benchmarking using sector-specific datasets (legal, healthcare, financial, etc.)
- Designed to expose latent risks, hallucinations, and adversarial weaknesses
Audit & Documentation
A rigorous, transparent audit layer ensures traceability, accountability, and regulatory defensibility.
- Based on ISO/IEC 23894, NIST AI RMF, and Annex VII of the EU AI Act
- Includes full lifecycle logging: model versions, training sets, incidents, and updates
- Standardized conformity assessment templates and reproducible test logs
- Supports external audit engagement and regulatory reporting
Continuous Monitoring & Model Governance
We treat post-deployment as a critical risk window—monitoring, detecting, and acting on emerging threats.
- Drift detection pipelines track accuracy, fairness, and behavior deviation
- Real-time alerting and rollback protocols enable rapid mitigation
- Defined escalation routes, oversight roles, and governance checkpoints
- Empowers cross-functional risk management by compliance, tech, and legal teams
Stakeholder Collaboration & Ethical Oversight
Effective AI governance is not siloed. Our framework embeds continuous collaboration and ethical review across all touchpoints.
- Internal stakeholders: Compliance, Data Science, Legal, Product, and Ethics teams
- External collaboration: Regulatory bodies, certification entities, academic research institutions
- Embeds ethical safety checkpoints throughout model design and release
- Supports public trust, regulatory readiness, and institutional alignment
