AI Assurance: A Comprehensive Testing Strategy for Enterprise AI Systems
Chitra Badagi, Divye Singh, Animesh Sen, Adinath Shirsath
Why It Matters
What makes this one worth your time
AI engineers and researchers should care because it addresses the unique challenges of assuring enterprise AI systems, which differ from traditional software, and provides structured guidance for managing these systems effectively.
A strategy for AI assurance focusing on risk reduction and evaluation-driven development.
Summary
The paper proposes a comprehensive assurance strategy for enterprise AI systems, focusing on continuous risk reduction, evaluation as a core discipline, and recognizing unique organizational impacts of AI failures. It introduces an AI Failure Taxonomy, a revised AI Assurance Pyramid, and offers guidance on evaluation-driven development and governance.
Key contributions
- Introduction of a structured AI Failure Taxonomy.
- Proposal of a revised five-layer AI Assurance Pyramid.
- Operational guidance on evaluation-driven development and governance.
Notable insights
- Evaluation must be treated as a core engineering discipline alongside development.
- AI failures can lead to organizational impacts fundamentally different from traditional software systems.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2605.23459v1 Announce Type: cross Abstract: Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software quality assurance was never designed to address. These systems are probabilistic, context-sensitive and emergent: they cannot be verified to be correct in the classical sense, but only evaluated with increasing confidence. This paper presents a comprehensive assurance strategy for enterprise AI systems built around three key principles: first, that AI testing should focus on continuous risk reduction rather than strict correctness verification; second, that evaluation must be treated as a core engineering discipline alongside development; and third, that failures in AI assurance can lead to organizational impacts that are fundamentally different from those seen in traditional deterministic software systems. We introduce a structured AI Failure Taxonomy, propose a revised five-layer AI Assurance Pyramid and provide operational guidance on evaluation-driven development, RAG system testing, model lifecycle management and governance. The goal is to equip engineering leaders and practitioners with a strategy that is both philosophically grounded and operationally deployable.