Personalized medicine is medical care tailored to the characteristics of an individual patient, such as genetics, clinical history, biomarkers, lifestyle, and response to prior treatment. Instead of relying only on population averages, clinicians use patient-specific data to choose therapies, predict risk, and adjust dosage or monitoring. In practice, it often depends on analytics, machine learning, and other decision-support tools that turn many data sources into a care recommendation.
From a cyber security perspective, personalized medicine is important because it depends on highly sensitive and high-value data. If records are stolen, altered, or mixed with incorrect data, the resulting treatment guidance can become unsafe or misleading. Defenders therefore focus on access control, encryption, logging, data provenance, and integrity checks, as well as validation of the models that interpret the data. In real systems, personalized medicine appears in genomic testing, precision oncology, drug-response prediction, and digital twin-based planning.