What question did this study set out to answer?

The research aims to establish a robust methodology for measuring false positive rates in AI anomaly detection systems.

April 11, 20260 citationsOpen Access

False Positive Rate Measurement Methodology for AI Agent Behavioral Anomaly Detection Systems

Key Points

The research aims to establish a robust methodology for measuring false positive rates in AI anomaly detection systems.
Developed a specification-first corpus construction protocol to reduce bias.
Created a 9-category behavioral taxonomy for enterprise agent types.
Employed Clopper-Pearson exact confidence intervals for assessing zero false positive observations.
Introduced a production measurement protocol with customer-verifiable SQL.
Implemented an auto-rollback circuit breaker for compliance control.
Establishes a repeatable methodology for measuring false positives.
Addresses the lack of rigorous statistical measurement in AI evaluation.
Potentially improves the reliability of AI agent deployments.

Abstract

Security products are routinely evaluated on true positive rates alone. False positive rates are rarely published, rarely measured with statistical rigor, and almost never independently verified. In AI agent behavioral anomaly detection, a single false positive that blocks a production agent can terminate a pilot deployment permanently. This paper proposes a complete false positive rate measurement methodology including: a specification-first corpus construction protocol eliminating co-design bias, a 9-category behavioral taxonomy covering the full range of legitimate enterprise agent archetypes, Clopper-Pearson exact confidence intervals for zero-FP observations, a production measurement protocol using customer-verifiable SQL, and an auto-rollback circuit breaker as a compliance control. Implemented in AgentRepEngine (DOI: 10.5281/zenodo.19169185).

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