CARE: A Practical Governance Model for AI Deployment Decisions

As artificial intelligence systems are increasingly deployed in operational, security, and decision-making contexts, organizations face a persistent governance gap: while existing frameworks identify and manage AI risk, they often leave deployment authority, ethical override, and accountability mechanisms undefined. This paper introduces CARE (Confirm, Align, Risk, Ethics), a lightweight governance model that structures AI deployment decisions through four independent review functions. CARE complements existing risk management frameworks, including the NIST AI Risk Management Framework, by governing whether an AI system should proceed to deployment rather than prescribing technical controls. CARE explicitly separates evidence verification, intent alignment, risk evaluation, and ethical assessment, prohibits score averaging, and grants ethical review override authority. The model is designed for zero-budget implementation using existing organizational roles and produces auditable decision records suitable for public-and private-sector oversight.