Taxonomy-Driven Analysis of Open-Source AI Risk Mitigation Tools
Rapid adoption of large language models (LLMs) in enterprise settings has introduced operational, security, and governance risks. As generative AI applications move from pilot to production, manual harm identification and mitigation are becoming difficult to scale. Although many tools support model evaluation, adversarial testing, runtime guardrails, and observability, the tooling landscape remains fragmented. Tools are typically designed for specific engineering tasks and described in technical terms that do not align with governance frameworks or risk taxonomies, making it difficult to determine which tools address which risks and where critical gaps remain. This paper proposes a struct...
| Authors | Afreen Alam, Evgenija Popchanovska, Ana Gjorgjevikj et al. |
|---|---|
| Primary category | cs.SE |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.