As AI agents move from demos into production, enterprises face a critical governance gap: agents lack verifiable identities, defined scopes, and persistent audit trails. Without agent identity, compliance teams cannot answer who authorized an action, what data was accessed, or whether outputs stayed within approved boundaries. The post outlines four architectural principles to address this: defining identity and permissions at creation (not runtime), applying governance to derived outputs not just source data, maintaining lifecycle records that outlast short-lived agents, and using human oversight as a periodic audit function rather than constant supervision. Snowflake's own internal Go-To-Market AI Assistant, serving 6,000+ employees and handling 35,000+ questions per week, is cited as a case study where role-based access, certified queries, and scoped permissions were built in as design constraints from the start.
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The questions every compliance team needs to answerWhy it's harder than it looksSolving agent identity starts with embedding governance into the architectureSolving agent identity is your silver bullet for enterprise AI adoptionSort: