SGAEIA: A Reference Architecture for Secure Governed Autonomous Edge Intelligence

Artificial intelligence becomes an architectural security problem when a model can do more than generate content. Tool use, persistent memory, delegated tasks, code execution, credentials, network access, and physical actuation allow model-generated intentions to cross technical and organizational boundaries. In that environment, model capability alone cannot determine whether an action is legitimate, whether authority remains valid, or whether the resulting effect matches what policy allowed. Governance must therefore become part of the execution architecture rather than remain an external review activity. This article presents SGAEIA — Secure Governed Autonomous Edge Intelligence Architecture — as a public reference architecture for governing autonomous and distributed AI systems. It organizes the problem through twelve responsibility planes and seven cross-cutting invariants that connect identity, authority, policy, execution, observation, evidence, revocation, recovery, and lifecycle change. The architecture is intended to support different deployment models without prescribing a single product, platform, or implementation topology. Its central proposition is that autonomous capability must remain bounded by independently enforceable authority and by assurance that can be demonstrated with evidence. The public description deliberately communicates architectural properties rather than sensitive implementation mechanisms. It does not disclose internal enforcement sequences, operational thresholds, repository-specific controls, or reconstruction-enabling schemas. It also does not claim that the architecture eliminates risk or that a reference implementation proves production readiness. The purpose is to provide a rigorous vocabulary for designing, evaluating, and governing systems in which AI can act. Capability is not authority. Identity is not authority. Evidence is not assertion.