Frontiers in Emerging Technology

An Open Access Peer Reviewed International Journal.
Publication Frequency- Bimonthly
Publisher Name-APEC Publisher.

ISSN Online- 2945-3437
Country of Origin-South Africa
Language- English

Evidence to Policy Assurance for Agentic AI: A Verifiable Provenance Framework for Regulated Enterprises

Conventional execution traces record what an agent did, but do not by themselves establish whether the evidence supporting a consequential action was authoritative, current, and sufficient for a specified policy decision. This study develops the Evidence to Policy Assurance Graph (EPAG), a typed representation linking sources, qualified evidence, claims, policy predicates, compliance determinations, authorizations, executed actions, and observed outcomes. A design-science process drew on 38 selected sources and evaluated a constructed $250,000 funds-transfer fixture. The evaluation comprised one valid baseline (S0), five single-condition perturbations (S1–S5), and six predefined criteria: completeness, qualification, localization, determinacy, reconstruction, and separability. The baseline satisfied all six criteria under the fixture rules. The perturbations exposed separate failures involving stale ledger evidence, a non-authoritative spreadsheet, insufficient funds, a missing second approval, and an execution amount that differed from the authorization. Each failure mapped to a defined primary disposition and, where applicable, a follow-up action. The 629 AgentDojo security cases and 144 ToolEmu cases cited in the paper provide published risk context; none was rerun or evaluated with EPAG. These findings establish representational adequacy for six constructed graph instances, not measured performance in a deployed system or proof of regulatory compliance. Independent studies must test tool-gateway enforcement, decision errors, audit-path reconstruction, latency, throughput, storage costs, and adversarial behavior.

Evidence to Policy Assurance for Agentic AI: A Verifiable Provenance Framework for Regulated Enterprises

Keywords

Agentic AI Evidence to Policy Assurance Graph (EPAG) Execution Provenance Evidence Qualification Policy Compliance Continuous Assurance.

Authors

Aditya Krishnan Independent Researcher USA

Abstract

Conventional execution traces record what an agent did, but do not by themselves establish whether the evidence supporting a consequential action was authoritative, current, and sufficient for a specified policy decision. This study develops the Evidence to Policy Assurance Graph (EPAG), a typed representation linking sources, qualified evidence, claims, policy predicates, compliance determinations, authorizations, executed actions, and observed outcomes. A design-science process drew on 38 selected sources and evaluated a constructed $250,000 funds-transfer fixture. The evaluation comprised one valid baseline (S0), five single-condition perturbations (S1–S5), and six predefined criteria: completeness, qualification, localization, determinacy, reconstruction, and separability. The baseline satisfied all six criteria under the fixture rules. The perturbations exposed separate failures involving stale ledger evidence, a non-authoritative spreadsheet, insufficient funds, a missing second approval, and an execution amount that differed from the authorization. Each failure mapped to a defined primary disposition and, where applicable, a follow-up action. The 629 AgentDojo security cases and 144 ToolEmu cases cited in the paper provide published risk context; none was rerun or evaluated with EPAG. These findings establish representational adequacy for six constructed graph instances, not measured performance in a deployed system or proof of regulatory compliance. Independent studies must test tool-gateway enforcement, decision errors, audit-path reconstruction, latency, throughput, storage costs, and adversarial behavior.

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