Assurance-Conditioned Continuous Authorization for Autonomous AI Agents

Authors: Mehdi Shajari

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Abstract: Autonomous software agents can act through tools, delegate work, and change their models or execution environments while earlier authorizations remain in flight. We present Assurance-Conditioned Continuous Authorization (ACCA), an approach designed to be harness-independent, for maintaining operational authority according to currently justified assurance. It distinguishes evidence validity from contextual applicability, derives assurance claims from eligible evidence, and resolves a multidimensional effective-authority envelope within a principal-declared ceiling and applicable policy. A typed dependency graph supports selective re-evaluation of authorization-relevant changes. An authority epoch changes when effective authority changes; a distinct optional authorization-basis version supports revalidation of action-specific authorization conditions. Protected effect-time enforcement can reject stale actions before a consequential resource change, subject to the available commit mechanism. A compositional Domain Governance Profile expresses domain-specific semantics without granting authority. Controlled scenario tests implemented as a regression suite using a trading profile exercise default-deny rules, evidence-conditioned contraction and recovery, policy divergence, selective non-response, and stale-action rejection at a local gateway before the simulated-broker boundary. The study establishes implementation-level evidence in one domain, not a cross-domain guarantee of harmful-behavior prevention.

Comments:

81 pages, 3 figures

Subjects:

Artificial Intelligence (cs.AI) ; Cryptography and Security (cs.CR)

Cite as:

arXiv:2611.05632 [cs.AI]

(or

arXiv:2611.05632v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.05632

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Mehdi Shajari [ view email ]

[v1]

Tue, 3 Nov 2026 21:19:46 UTC (103 KB)

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