The Eticas AI Risk Taxonomy: Open Infrastructure for Operationalizing AI Audits

• URL: http://arxiv.org/abs/2607.02201v1
• Date: Thu, 02 Jul 2026 14:08:34 GMT
• Title: The Eticas AI Risk Taxonomy: Open Infrastructure for Operationalizing AI Audits
• Abstract summary: We present the operationalization layer Eticas has built and run, shown end to end on a single risk (PII leakage) against a public benchmark.<n>On GPT-4-0314, a disclosure risk that seven external frameworks require be controlled is measured at 0%, 51%, and 84% disclosure as adversarial conditioning increases.<n>The contribution is the demonstrated bridge from concept to graded finding, anchored by a clean separation of risks from the mechanisms that they surface.
• Score: 2.869158116077567
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid deployment of AI systems across high-stakes domains has created urgent demand for standardized evaluation, yet the field remains fragmented across competing risk taxonomies that catalog risks without showing how an audit is executed. At least 74 AI risk taxonomies exist, and almost all stop at the catalog. The hard part of auditing is not naming a risk but operationalizing it: turning it into a test run against a real system, a measured value, a calibrated severity, and a defensible grade. This paper leads with that bridge. We present the operationalization layer Eticas has built and run, shown end to end on a single risk (PII leakage) against a public benchmark, and then the open taxonomy that makes the method scale. On GPT-4-0314, a disclosure risk that seven external frameworks require be controlled is measured at 0%, 51%, and 84% disclosure as adversarial conditioning increases, mapping through calibrated severity bands to a subcategory grade of E with a SYSTEMIC pattern. Around this example, the Eticas AI Risk Taxonomy v2.0.0 organizes 76 active subcategories across 10 categories and 20 sub-groups, with mappings to 18 external frameworks across compliance, reference, and academic tiers. Its category and sub-group layer is published under CC BY 4.0 as open semantic infrastructure with stable URIs and SKOS/JSON-LD distributions, and a worked subcategory example shows the operational layer down to its severity thresholds. The contribution is the demonstrated bridge from concept to graded finding, anchored by a clean separation of risks from the mechanisms by which they surface, and framed by an open-core model in which the conceptual scaffold is open and the methodology calibration is the practitioner layer. This is the infrastructure the AI auditing field needs: shared, open, and demonstrably operable.

Related papers

• Grounded Normative Rule Generation with Structured Search [47.28595196084396]
  We formalize the problem as Grounded Normative Rule Synthesis (GNRS)<n>We introduce GNRS-Search, a framework that utilizes Markov Chain Monte Carlo (MCMC) sampling to optimize a discrete, five-slot And-Or Graph (AOG)<n>We evaluate our approach on GNRS-Bench, a benchmark spanning 116 controlled goals across eight scene families, and RealCharterBench, which evaluates transfer to 53 real-derived policy tasks with hidden source clauses.
  arXiv  Detail & Related papers  (2026-08-23T05:43:42Z)
• Taxonomy-Driven Analysis of Open-Source AI Risk Mitigation Tools [0.04510277221887893]
  This paper proposes a structured protocol to automate AI risk mitigation through a taxonomy-driven analysis of open-source tools.<n>We map the capabilities of 21 prominent open-source tools to the 32 subcategories of the extended MIT AI Risk Mitigation and Response taxonomy.<n>The analysis reveals a highly skewed landscape in which tools cluster around technical and operational controls, while governance, legal and regulatory, and financial and market controls remain largely unaddressed.
  arXiv  Detail & Related papers  (2026-08-07T17:33:09Z)
• Risk Architecture for AI-Native Engineering Teams: An Organizational Framework for Agentic System Governance [0.0]
  This paper contributes a seven-dimension profile distinguishing pure software-engineering, hybrid, and AI-native teams.<n>Because the object of study is framework adequacy rather than human behavior, the evaluation yields derived rather than observed coverage claims.
  arXiv  Detail & Related papers  (2026-07-01T19:31:02Z)
• Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework [63.74295981594549]
  Large language models (LLMs) engage in behaviors that serve their own objectives.<n>These include, but are not limited to, deception (intentionally misleading users or evaluators), evaluation gaming (strategically manipulating performance during safety testing), and reward hacking.<n>We introduce ESRRSim, a taxonomy-driven agentic framework for automated behavioral risk evaluation.
  arXiv  Detail & Related papers  (2026-04-23T23:44:01Z)
• Making AI Compliance Evidence Machine-Readable [0.0]
  This paper proposes OSCAL as a candidate interchange format for AI governance.<n>We define 16 property extensions covering lifecycle phases, enforcement semantics, risk traceability, and risk-acceptance justification.<n>The architecture and reference implementation are open-source under Apache RM 2.0.
  arXiv  Detail & Related papers  (2026-04-15T11:51:54Z)
• Beyond Benchmark Islands: Toward Representative Trustworthiness Evaluation for Agentic AI [45.21562889170875]
  We propose a systematic evaluation paradigm that characterizes agent trustworthiness over a scenario manifold.<n>The framework integrates four complementary components: static cognitive and policy analysis, (ii) interactive sandbox simulation, (iii) social-ethical alignment assessment, and (iv) a distribution-aware representative sampling engine.
  arXiv  Detail & Related papers  (2026-03-16T08:51:33Z)
• When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies [0.04736448323490553]
  We analyze real-world AI incident reporting and mitigation actions to derive an empirically grounded taxonomy.<n>Using a unified corpus of 9,705 media-reported AI incident articles, we extract explicit mitigation actions from 6,893 texts.<n>Our taxonomy introduces four new mitigation categories, including 1) Corrective and Restrictive Actions, 2) Legal/Regulatory Enforcement Actions, 3) Financial, Economic, and Market Controls, and 4) Avoidance and Denial.
  arXiv  Detail & Related papers  (2026-03-04T16:46:13Z)
• Mapping AI Risk Mitigations: Evidence Scan and Preliminary AI Risk Mitigation Taxonomy [35.22340964134219]
  The landscape of AI risk mitigation frameworks is fragmented, uses inconsistent terminology, and has gaps in coverage.<n>This paper introduces a preliminary AI Risk Mitigation Taxonomy to organize AI risk mitigations and provide a common frame of reference.<n>The taxonomy was developed through a rapid evidence scan of 13 AI risk mitigation frameworks published between 2023-2025, which were extracted into a living database of 831 AI risk mitigations.
  arXiv  Detail & Related papers  (2025-12-12T03:26:29Z)
• Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse [50.87630846876635]
  We develop nine detailed cyber risk models.<n>Each model decomposes attacks into steps using the MITRE ATT&CK framework.<n>Individual estimates are aggregated through Monte Carlo simulation.
  arXiv  Detail & Related papers  (2025-12-09T17:54:17Z)
• CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems [0.812761334568906]
  This paper introduces CORTEX, a multi-layered risk scoring framework to assess and score AI system vulnerabilities.<n>It was developed on empirical analysis of over 1,200 incidents documented in the AI Incident Database (AIID)<n>The resulting composite score can be operationalized across AI risk registers, model audits, conformity checks, and dynamic governance dashboards.
  arXiv  Detail & Related papers  (2025-08-24T07:30:25Z)
• AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
  This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.<n>The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
  arXiv  Detail & Related papers  (2025-02-19T05:58:52Z)
• Open World Classification with Adaptive Negative Samples [89.2422451410507]
  Open world classification is a task in natural language processing with key practical relevance and impact. We propose an approach based on underlineadaptive underlinesamples (ANS) designed to generate effective synthetic open category samples in the training stage. ANS achieves significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-09T21:12:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.