Universal Lifecycle Governance for the Generative AI Ecosystem: Cross-Interface Recoverable Deletion, Dependency-Aware Restoration, and Verifiable Permanent Erasure
Year: 2026
Type: preprint
Abstract: The rapid development of generative artificial intelligence (AI) is producing increasingly complex ecosystems composed of interconnected agents, memory systems, tools, plugins, workflows, knowledge repositories, prompts, model configurations, personalization profiles, permissions, and automation services. These components are frequently created, modified, interconnected, and removed through different interfaces and operational processes. Conventional deletion mechanisms, however, often treat removal as an object-level operation, leaving important questions about recoverability, dependency integrity, version preservation, and permanent erasure insufficiently coordinated across the broader AI ecosystem. This paper proposes Universal Lifecycle Governance for the Generative AI Ecosystem (ULG-GAI), a conceptual architecture that establishes a unified lifecycle management framework for heterogeneous AI ecosystem objects. Its central mechanism is a cross-interface recoverable-deletion system in which eligible objects removed through graphical user interfaces (GUIs), application programming interfaces (APIs), command-line interfaces (CLIs), programming code, agent operations, or automated workflows enter a governed temporary-retention state rather than being immediately and irreversibly destroyed. During a configurable recovery period, authorized users or processes can restore objects together with their preserved versions, configurations, dependencies, relationships, weighting parameters, permissions, and associated operational metadata. After the retention period expires, the system initiates policy-governed permanent purging, including the removal or controlled expiration of relevant derived representations. The architecture supports a single global trash repository, multiple object-specific trash repositories, and hybrid arrangements in which specialized repositories are presented through a unified management interface. It extends lifecycle governance beyond memory records to cover the broader AI ecosystem, including agents, agent configurations, memory libraries, retrieval policies, tools, plugins, connectors, workflows, prompts, knowledge bases, datasets, model-routing configurations, generation parameters, personalization settings, and governance policies. A typed object registry, shared lifecycle policy engine, dependency graph, version-preserving deletion manifest, restoration transaction manager, and purge verification service are proposed as the principal architectural components. The paper formalizes lifecycle states, cross-interface consistency requirements, dependency-aware restoration semantics, and testable research hypotheses. It also distinguishes directly restorable objects from objects requiring reconstruction, reauthorization, or external reconnection. Existing software systems already demonstrate several component mechanisms, including soft deletion, multi-object trash repositories, API and CLI management, dependency-aware restoration, and automatic expiration. Accordingly, this paper does not claim these individual mechanisms as novel. Its proposed contribution is an integrated, AI-ecosystem-oriented lifecycle governance architecture that brings heterogeneous AI resources under consistent deletion, recovery, and erasure semantics. No empirical results or implementation claims are made; the framework establishes a conceptual and testable foundation for future research.
Author: Zhang, Jincheng
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225663041
Cites:
Cited by:
Related to: