Getting Teams to Actually Follow AI Governance Rules

Engineering teams are increasingly burdened by the rise in AI regulations, which threaten to decelerate production cycles due to compliance demands that often manifest as paperwork rather than integrated practices. The text discusses the challenges of AI governance, highlighting issues like compliance stalling deployment, policy creators being disconnected from practical implementation, and the inefficiencies of checkbox-driven compliance. It proposes embedding governance directly into code and using CI/CD pipelines to ensure adherence to regulations without sacrificing agility. By treating governance as part of the engineering process, rather than an external mandate, teams can transform oversight from a bureaucratic hurdle into a seamless part of development, enhancing both compliance and velocity. Automated tools like Galileo are suggested to help centralize and streamline governance, providing real-time audits and risk-based tiering to focus oversight where it's needed most, thereby aligning technical practices with business objectives and fostering trust in AI investments.

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