As generative AI enters daily enterprise operations, compliant use has shifted from "nice to have" to "must have." Under the current regulatory framework, four checkpoints deserve attention.
Checkpoint 1: Data Input
Feeding customer personal information, trade secrets or undisclosed business data into external AI services may create data-leakage and compliance risks. Enterprises should define what data may or may not be used, and apply private deployment or data masking where necessary.
Checkpoint 2: Content Output
When AI-generated content is used for public communication, contracts or customer interactions, keep human review in the loop and clarify accountability; specific content regulations apply in fields such as news.
Checkpoint 3: Filing for Public-Facing Services
Under regulations such as the Interim Measures for the Management of Generative AI Services, providing generative AI services to the public in China requires security assessment and algorithm filing. Internal tools and public services face different requirements — assess them separately.
Checkpoint 4: People and Governance
Compliance ultimately lands on management: internal usage policies and approval flows, staff training, and vendor assessment mechanisms — so "using AI well" and "keeping risks in check" advance together.
Compliance is not the enemy of innovation. With boundaries clearly drawn, enterprises can use AI deeply and with confidence. (This article is general industry observation, not legal advice; consult professionals for specific compliance arrangements.)