The claim that training data availability doesn’t matter for privacy/security overlooks critical risks. Even a local model trained on compromised data could inadvertently leak sensitive information through outputs or vulnerabilities. For example, if the training data includes personal health records (as in HIPAA-regulated scenarios), the model might reproduce patterns that re-identify individuals, regardless of network access. Stanford’s research highlights how AI systems can expose private data via prompts or connections to law enforcement, suggesting that training data’s origins matter deeply. IBM also notes AI’s unique privacy risks, emphasizing that data governance isn’t just about deployment but *collection* and *usage*. While federated learning avoids raw data exposure, it’s not universally adopted, leaving many models vulnerable. Arguing that this is “redundant” ignores the foundational role of data ethics in AI—without rigorous safeguards, even offline systems risk undermining trust.

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