AI Governance Must Be Built Into Release Process, Not Added Later

Traditional compliance approaches treat governance as a final review step after product development. This model fails for AI systems that continuously evolve—retrieval indices update, new tools are added, and evaluations become outdated between review cycles. Most organizations still govern AI like traditional software: build, ship, then seek legal approval. This leaves critical changes unmonitored. Chinese AI companies demonstrate an alternative: embedding governance directly into deployment pipelines as release infrastructure. Compliance checkpoints become mandatory gates before launch, not post-release reviews. This approach requires tracking live retrieval indices, setting output-monitoring thresholds, and tying model evaluations to enforceable release gates. Making governance part of the product development process rather than an external audit layer better addresses AI's dynamic nature and ensures safety throughout the system's lifecycle. Source: Original tech publication.