The Security Gap Hiding Inside Pharma’s A.I. Revolution

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From prompt injection to MLOps vulnerabilities to models that inadvertently memorize patient data, the attack surfaces introduced by A.I. in pharmaceutical research have moved well beyond what traditional compliance frameworks were ever built to address.

Safeguarding sensitive information has become a defining challenge for modern organizations, especially in high-stakes fields such as drug development, where clinical trial datasets and patient health information are critical to innovation.