arXiv:2607.02201v1 Announce Type: cross 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 proliferation of AI systems across critical domains necessitates standardized, operationalized auditing frameworks to manage increasing risks and regulatory pressures.
This development represents a critical step towards maturing AI governance and accountability, moving beyond theoretical risk identification to practical, auditable implementation, which is essential for broad AI adoption and trust.
The focus in AI auditing shifts from merely cataloging risks to providing a concrete, open infrastructure for executing audits, enabling measured values, calibrated severities, and defensible grades for AI systems.
- · AI audit firms
- · Regulatory bodies
- · Developers of transparent AI systems
- · Sectors deploying high-stakes AI
- · Developers of opaque AI systems
- · Organizations relying on proprietary, non-standardized risk assessments
Increased clarity and accountability in AI system deployment, fostering greater trust.
Standardized auditing frameworks could accelerate AI adoption in highly regulated industries by reducing uncertainty and demonstrating compliance.
The open infrastructure could lead to a commoditization of basic AI auditing services, shifting value towards advanced, domain-specific AI assurance.
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Read at arXiv cs.AI