Confidential AI factories are end‑to‑end ML development and deployment pipelines that embed data governance, model provenance and hardware‑backed isolation so sensitive training data, weights and inference workloads never leave a trusted boundary. Built around confidential computing (trusted execution environments, enclave/SEV‑style isolation, and tightly controlled key management), these platforms reconcile enterprise requirements for compliance, IP protection and low‑latency inference with modern agentic and MLOps workflows. This topic is timely as of 2025: enterprises face growing regulatory scrutiny and heightened risk of data leakage to third‑party services, driving demand for private or hybrid deployments. Practical options span fully on‑prem stacks (maximum control, integration with internal data platforms and governance) to cloud providers’ confidential instances (operational elasticity and managed hardware‑backed isolation). Key tradeoffs are control vs operational overhead, scalability vs predictable latency, and governance integration vs vendor lock‑in. Tools and categories in play include AI Security Governance and AI Data Platforms plus developer and agent tooling. Examples: Warp (Agentic Development Environment) accelerates secure developer workflows and can be paired with private model endpoints; IBM watsonx Assistant supports enterprise virtual agents and multi‑agent orchestrations that can be deployed with governance controls; Tabnine and Tabby offer private/self‑hosted coding assistants to reduce code‑exfiltration risk; Cline provides client‑side, auditable coding agents; MindStudio and Anakin.ai enable low‑code agent design but require strong deployment controls. Together, these components illustrate how confidential AI factories combine IDE/agent workflows, model serving, and platform‑level confidential computing to meet enterprise security and compliance needs.