Context-Aware Authorization Framework for Agentic AI: Adaptive Access Decisions, Risk Assessment, and Trust Management

Abstract

Agentic artificial intelligence systems differ from conventional software applications because they can interpret objectives, invoke tools, access heterogeneous resources, and execute multi-step actions with limited human intervention. Consequently, authorization mechanisms based exclusively on static identities, predefined roles, or binary permission checks are insufficient for environments in which access requirements change according to task context, resource sensitivity, behavioral evidence, and evolving risk. This paper proposes a Context-Aware Authorization Framework for Agentic AI that combines contextual representation, adaptive risk assessment, attribute-aware decision mechanisms, and dynamic trust management. The methodological foundation is derived from the provided literature on attention modeling, attribute-specific representation learning, similarity prediction, iterative learning, and knowledge distillation. These techniques are conceptually transferred to authorization by treating an agent's request as a multidimensional representation rather than as an isolated identity-permission pair. The framework introduces a context aggregation layer, risk evaluation engine, policy decision mechanism, and continuous trust monitor. A conceptual evaluation demonstrates how increasingly fine-grained contextual information can improve authorization discrimination while introducing computational and governance trade-offs. The resulting framework provides a structured approach for risk-adaptive authorization in agentic environments, particularly where agents operate across heterogeneous tools and dynamically changing operational contexts.