# AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources

arXiv: https://arxiv.org/abs/2503.05780 · HTML: https://mlsanity.com/paper/2503.05780/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2025.

## Built on by (most cited first, 8 of 8)

- [Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting](https://mlsanity.com/paper/2606.09809/) (2026)
- [Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond](https://mlsanity.com/paper/2607.02197/) (2026)
- [Interpreting LLM-as-a-Judge Policies via Verifiable Global Explanations](https://mlsanity.com/paper/2510.08120/) (2025)
- [Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment](https://mlsanity.com/paper/2602.08565/) (2026)
- [Auto-BenchmarkCard: Automated Synthesis of Benchmark Documentation](https://mlsanity.com/paper/2512.09577/) (2025)
- [The Eticas AI Risk Taxonomy: Open Infrastructure for Operationalizing AI Audits](https://mlsanity.com/paper/2607.02201/) (2026)
- [Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applications](https://mlsanity.com/paper/2608.23870/) (2026)
- [From Legal Text to AI-specific Risk Sources: A Systematic Analysis of the EU AI Act's High-Risk Requirements](https://mlsanity.com/paper/2609.13535/) (2026)

## Abstract (arXiv)

The rapid evolution of generative AI has expanded the breadth of risks associated with AI systems. While various taxonomies and frameworks exist to classify these risks, the lack of interoperability between them creates challenges for researchers, practitioners, and policymakers seeking to operationalise AI governance. To address this gap, we introduce the AI Risk Atlas, a structured taxonomy that consolidates AI risks from diverse sources and aligns them with governance frameworks. Additionally, we present the Risk Atlas Nexus, a collection of open-source tools designed to bridge the divide between risk definitions, benchmarks, datasets, and mitigation strategies. This knowledge-driven approach leverages ontologies and knowledge graphs to facilitate risk identification, prioritization, and mitigation. By integrating AI-assisted compliance workflows and automation strategies, our framework lowers the barrier to responsible AI adoption. We invite the broader research and open-source community to contribute to this evolving initiative, fostering cross-domain collaboration and ensuring AI governance keeps pace with technological advancements.