Background. UK central government has built a substantial body of pre-deployment AI assurance practice, anchored in departmental assurance questionnaires, the Artificial Intelligence Playbook for the UK Government (Government Digital Service, 2025), the Algorithmic Transparency Recording Standard (ATRS), and instruments such as Data Protection and Equality Impact Assessments. These instruments establish a strong foundation for assessing whether an AI system is fit to enter live service. The next step, building on this strength, is to extend the same structured rigour across the rest of the system lifecycle.Aim. This paper proposes and theoretically grounds a four-pillar reference model for responsible AI (RAI) assurance designed to augment existing UK government instruments across the full lifecycle: Pre-Deployment, Model Activation, Operational Response, and Closed-Loop Learning. The distinctive moves are the treatment of Model Activation as a discrete baseline-setting phase and the specification of Closed-Loop Learning as a cross-government or cross-deployer institutional function, both of which are thinly addressed in existing reference frameworks.Approach. The model is developed using a design science research approach. It is derived through structured comparative document analysis of the four reference frameworks UK departments most directly encounter (the NIST AI Risk Management Framework, the OECD AI Principles, the EU AI Act, and the UK AI Playbook) using the AI system lifecycle as the analytical organising frame. It is theoretically anchored in Value Sensitive Design (Friedman, Kahn and Borning, 2006; Friedman and Hendry, 2019; Umbrello and van de Poel, 2021) and refined through expert-informed practitioner engagement at the Department for Science, Innovation and Technology (DSIT) and a second UK central government department in early 2026.Findings. The comparative analysis shows that the four reference frameworks converge densely at pre-deployment, address operational response with varying degrees of prescriptiveness, are thin on activation as a distinct lifecycle phase across three of the four frameworks, and consistently underspecify closed-loop learning. The four-pillar model addresses the cells where the leading frameworks are weakest, in a form compatible with each.Contribution. The paper contributes a lifecycle reference model for public sector AI assurance, a cell-level mapping of four leading frameworks against that lifecycle, and an institutional fit analysis for UK government adoption. Its distinctive contribution lies in specifying activation as calibration and closed-loop learning as institutional memory. The paper is offered as a basis for cross-government conversation and as the design phase of a research programme whose evaluation phase is described in Section 8.