Intent Governance in Artificial Intelligence A Purpose-Aware Framework for Human Authorization, Intent Drift Detection, Persistent Memory, and Lifecycle Accountability within AIVORIX™
Year: 2026
Type: article
Abstract: Zenodo Publication Description Intent Governance in Artificial Intelligence: A Purpose-Aware Framework for Human Authorization, Intent Drift Detection, Persistent Memory, and Lifecycle Accountability within AIVORIX OS™ This research paper introduces Intent Governance and Purpose Validation (IGPV) as a proposed architectural capability within AIVORIX OS™, a governance-centric artificial intelligence operating system architecture developed by Nicole Aleece Crossleytownsend, Founder and Chief Architect of Crossley Enterprises, Inc. As artificial intelligence becomes increasingly autonomous and interconnected across enterprise environments, conventional identity verification, access controls, and policy enforcement may not adequately establish whether AI operations remain consistent with the purposes for which they were originally authorized. This research explores a critical additional dimension of AI governance: not only determining who is authorized to use artificial intelligence and what actions are permitted, but also establishing why an AI operation is being performed, whether its purpose is authorized, and whether subsequent actions remain consistent with that purpose. The proposed Intent Governance framework introduces five interconnected capabilities: Intent Declaration, Purpose Authorization, Intent–Action Consistency Evaluation, Intent Drift Detection, and Intent Evidence and Accountability. Strengthening Persistent Memory A central contribution of the proposed framework is its potential to extend AIVORIX OS's persistent organizational memory beyond retaining operational context, policy decisions, and historical activities. Through Intent Governance, persistent memory could preserve the authorized purpose behind AI-mediated activities, including the original human objective, applicable authority, permitted operational boundaries, policy versions, subsequent purpose amendments, and associated human approvals. This creates the foundation for purpose-aware institutional memory, allowing authorized AI systems to reference not only what occurred previously, but also why an operation was authorized, under what conditions it was permitted, and whether those conditions remain valid. Such continuity could help prevent AI agents from losing or misinterpreting governing objectives during workflow transitions, system recovery, delegated operations, and interactions involving multiple AI providers. Advancing Lifecycle Traceability and Accountability Intent Governance also proposes strengthening AIVORIX OS's lifecycle traceability by establishing verifiable relationships among human-declared objectives, authorization decisions, AI actions, policy restrictions, and execution outcomes. Rather than maintaining isolated activity records, the proposed architecture would associate each governed operation with its originating purpose and subsequent authorization history. This approach could enable organizations to reconstruct an AI decision lifecycle and determine: What objective the human originally declared. Whether the purpose and requested action were authorized. Which policies and human approvals governed execution. Whether an AI agent remained within its approved operational boundaries. When a material purpose deviation occurred. How the system responded through restriction, escalation, denial, or authorized amendment. What evidence was preserved to support accountability and subsequent review. By connecting intent-related governance records with persistent memory and lifecycle evidence, AIVORIX OS could improve the continuity, explainability, and auditability of AI-assisted operations. Intent Drift and Cross-System Governance The paper further introduces Intent Drift Detection, a proposed mechanism for identifying material deviations between an approved purpose and subsequent AI actions. Such deviations may arise through changing human instructions, agent delegation, unauthorized data transfers, prompt injection, workflow expansion, or changing operational circumstances. The proposed framework would evaluate observable actions and authorization boundaries rather than claim to determine hidden human motivations. Integrated with AIVORIX OS's governance and orchestration capabilities, Intent Governance could support continuous purpose validation across heterogeneous AI models, agents, enterprise applications, and software-defined systems. Research Scope and Future Validation The paper presents a conceptual technical architecture, formal authorization model, six illustrative operational scenarios, cross-industry applications, legal and ethical safeguards, and an experimental validation methodology. Potential applications include healthcare, financial services, government, scientific research, automotive software-defined systems, and critical infrastructure. The research maintains the existing ten-layer AIVORIX OS architecture, proposing Intent Governance as a cross-layer capability principally involving Persistent Memory Infrastructure, Governance and Risk Management, Human Oversight, and Runtime Orchestration. The proposed framework emphasizes privacy, proportionality, human authority, and the distinction between declared operational purpose and unobservable psychological intent. Intent Governance is presented as a proposed research capability requiring further implementation, controlled experimentation, and independent technical validation. Ultimately, this research advances the AIVORIX OS architectural hypothesis that persistent governance state can support accountable AI operations by preserving not only organizational knowledge and decision history, but also the authorized purposes that give those decisions meaning. The central proposition is that responsible AI governance should preserve not only what an AI system did, but why the action was authorized, whether its execution remained within that purpose, and who retained accountability throughout its lifecycle. Author: Nicole Aleece CrossleytownsendAffiliation: Crossley Enterprises, Inc.Research Platform: AIVORIX OS™ — Powered by AIVORIX™Publication: November 2026Research Category: Artificial Intelligence Governance, Persistent Memory, Intent Governance, and Lifecycle Accountability
Author: Crossleytownsend, Nicole
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225819898
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