The journey of data through AI systems begins with its ingestion. This process must be handled with utmost care to prevent sensitive information from being compromised. Establishing robust data controls is essential for securing data throughout its lifecycle, from the moment it enters AI models to its ultimate use in AI systems. These controls include inline classification, conversion, redaction, anonymization, and sanitization of data. By aligning these processes with enterprise data policies and user entitlements, you can ensure that only the necessary and approved data is processed and utilized by AI models.
The importance of consent cannot be overstated in this context. As you navigate through the complexities of data handling, honoring user preferences for opt-outs, access, and deletion in accordance with Data Subject Rights (DSR) fulfillments becomes a cornerstone of ethical AI use. Moreover, compliance-driven user disclosures further reinforce the transparency and accountability of AI systems.
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