Efficient PII sanitization is essential for integrating large language models (LLMs) and agentic AI into enterprise workflows, particularly in regulated or sensitive data environments. Despite their capabilities, LLMs pose privacy challenges as they are not inherently privacy-aware, often retaining and potentially exposing sensitive information. To mitigate these risks, PII sanitization should be embedded into AI workflows from inception, rather than added as an afterthought. Current ad-hoc solutions, such as regex-based redaction, lack consistency and scalability, prompting the need for more structured approaches like AI gateways. These gateways function similarly to API gateways, providing control, visibility, and policy enforcement while abstracting PII sanitization from developers, thus reducing human error and focusing on core AI functionality. Kong's AI Gateway exemplifies this by integrating PII sanitization as a standard policy, which is crucial for maintaining compliance, trust, and efficient data management. This approach supports broader AI innovation by ensuring secure and compliant data handling, ultimately facilitating faster market deployment and enhancing business value.