Data Lifecycle Management
The policy-based management of data flow throughout its lifecycle: from creation and initial storage to the time it becomes obsolete and is deleted.
Defines standardized processes and tools for data ingestion, classification, storage tiering, archival, retention, and secure deletion. Lifecycle governance ensures that data remains accessible and secure when needed, but is purged when obsolete, reducing storage costs, limiting exposure, and supporting compliance with retention regulations.
A retailer’s customer-transaction data is stored in hot databases for six months, then moved to cold storage for two years, and deleted after three years. Automated policies enforce these tiers and generate audit reports confirming deletion - ensuring both business needs and GDPR retention limits are met.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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