Universal Lifecycle Governance for Generative AI Memory Ecosystems: Cross-Interface Recoverable Deletion, Configuration Preservation, and Dependency-Aware Restoration

Generative artificial intelligence systems increasingly rely on persistent memory infrastructures composed of heterogeneous objects, including individual memory records, memory collections, configuration profiles, retrieval rules, weighting schemes, contextual selection policies, knowledge graphs, indexes, permissions, and automation procedures. Although conventional deletion operations are commonly designed to remove individual records or configuration resources, these operations can create substantial risks when users, applications, APIs, scripts, or automated processes unintentionally delete objects that remain operationally or structurally important. A further difficulty arises when a deleted object is connected to other memory components: restoring the object alone may not restore its original configuration, version, access permissions, or dependency relationships. This paper proposes Universal Lifecycle Governance for Generative AI Memory Ecosystems (ULG-ME), a conceptual architecture that treats deletion, temporary retention, recovery, and permanent erasure as coordinated lifecycle operations across the entire memory ecosystem. The architecture introduces a unified recoverable-deletion layer applicable to both memory content and memory-related configuration objects, including weighting configurations, retrieval policies, dynamic adaptation strategies, collection definitions, contextual presets, metadata, indexes, and dependency relationships. It supports multiple deletion interfaces—including graphical user interfaces (GUIs), application programming interfaces (APIs), command-line interfaces (CLIs), direct programmatic operations, model-context-protocol interfaces, and automated workflows—through a shared lifecycle policy engine. The architecture further accommodates both a unified global trash and multiple object-specific trash repositories presented through an integrated view. Its principal technical mechanisms include object-aware deletion interception, dependency-aware deletion manifests, version-preserving snapshots, configurable recovery windows, transactional restoration, conflict detection, authorization reconstruction, and policy-governed permanent purging. Importantly, the framework distinguishes logical removal from physical erasure and recognizes the need to address indexes, caches, replicas, backups, and derived representations. The paper formalizes lifecycle states, object models, deletion and restoration semantics, system invariants, research propositions, and an evaluation methodology. It does not claim empirical validation or implementation results. Rather, it establishes a testable architectural foundation for safer, more consistent, and more accountable memory lifecycle management in generative AI systems.