The accelerating adoption of artificial intelligence across enterprise environments has transformed operational efficiency, decision-making, and competitive advantage, while simultaneously introducing complex governance, security, and compliance challenges. At a broad level, organizations increasingly rely on AI-driven systems to automate processes, analyze large-scale data, and support mission-critical functions, thereby expanding both operational dependencies and systemic risk exposure. As AI becomes embedded within core business workflows, traditional governance and cybersecurity models prove insufficient to address the combined technical, organizational, and regulatory risks associated with algorithmic decision-making. This study narrows the discussion to the operationalization of AI governance through the integration of security operations, risk management, and compliance controls within enterprise settings. It conceptualizes AI governance not as a standalone policy construct, but as an executable framework embedded in day-to-day operational processes. By aligning security monitoring, incident response, risk assessment, and regulatory compliance with the AI system lifecycle, enterprises can translate governance principles into enforceable and measurable controls. The paper emphasizes how integrated security operations enable continuous oversight, how risk management supports proportional and context-aware control deployment, and how compliance mechanisms ensure accountability and auditability. The proposed perspective positions integrated operational controls as the practical foundation for trustworthy AI adoption, enabling enterprises to scale innovation while maintaining resilience, regulatory alignment, and stakeholder trust.