Intent-Adaptive Implicit Permission Decision in Generative Artificial Intelligence: A Novel Architecture for Contextual Memory Isolation and Autonomous Privacy-Preserving Permission Management

The increasing integration of persistent memory, conversational history retrieval, personalized context, and autonomous decision-making into generative artificial intelligence (AI) systems creates new challenges for conversational privacy and user-controlled information management. Although memory-management mechanisms allow users to configure whether historical information can influence subsequent interactions, conventional approaches generally depend on users manually selecting and maintaining appropriate settings. This creates an interaction burden, particularly when users frequently switch between independent reasoning, exploratory research, sensitive discussions, personalized assistance, and other conversational activities requiring different contextual boundaries. A fundamental research question therefore emerges: can a generative AI system infer the operational requirements of a conversation from the user's underlying intent and proactively determine which memory-isolation and information-access policies should be recommended or applied? This paper proposes Intent-Adaptive Implicit Permission Decision (IAIPD), a novel architectural framework for intent-driven, context-sensitive permission management in generative AI. To the best of the author's knowledge, IAIPD is the first proposed architecture specifically formulated to integrate inferred conversational intent, adaptive memory-isolation policy selection, implicit permission assessment, and graduated policy enforcement into a unified conversation-level permission-decision framework. Unlike conventional manually configured memory controls, the proposed architecture continuously evaluates the relationship between conversational intent, contextual sensitivity, historical-memory relevance, potential contextual interference, and existing user authorization boundaries. Based on this evaluation, the system determines whether to maintain conventional memory access, recommend an alternative isolation configuration, or automatically apply a previously authorized low-risk policy. The framework introduces five principal architectural components: Intent Recognition and Classification, Contextual Risk Assessment, Adaptive Permission Decision, Permission Enforcement, and Policy Feedback and Adaptation. It further distinguishes three operational decision levels: passive permission recommendation, authorized implicit application, and explicit user confirmation. This distinction is particularly important because implicit decision-making should reduce unnecessary user interaction without transforming inferred intent into unrestricted authority to access, modify, preserve, or disclose personal information. The proposed architecture incorporates confidence thresholds, risk-sensitive decision boundaries, policy provenance, reversibility, uncertainty-based abstention, and user-defined authorization constraints. The paper establishes a formal decision model, operational workflow, integration strategy with Memory-Isolated, History-Preserved, and Future-Integrable Conversations (MIHFI), research propositions, and an experimentally testable evaluation methodology. Potential evaluation dimensions include intent-classification accuracy, inappropriate policy-activation rate, unauthorized memory retrieval, contextual independence, permission-decision latency, user intervention frequency, perceived autonomy, and privacy protection. No experimental results or demonstrated performance improvements are claimed. The principal contribution is the conceptual transition from manually configured conversational memory permissions toward intent-adaptive, user-governed permission decisions, in which the AI system can proactively recognize when contextual isolation may be appropriate without requiring users to repeatedly navigate complex configuration settings.