Episode 25 — Synthetic Data
Synthetic data is artificially generated to mimic real datasets while reducing reliance on sensitive information. This episode explains how it can protect privacy, expand small datasets, and create scenarios for testing. Learners explore generation techniques including statistical sampling, generative adversarial networks (GANs), and simulation models. Synthetic data is framed as both an opportunity to reduce risks and a tool for fairness by improving representation of underrepresented groups.
Examples show adoption across multiple domains. In healthcare, synthetic datasets enable research collaborations without exposing patient identities. In finance, organizations use synthetic transaction data to test fraud detection algorithms. In transportation, simulation-based synthetic data supports training autonomous vehicles in rare or dangerous scenarios. Risks are also highlighted, including potential for re-identification if data is poorly generated and the danger of introducing artificial biases. Learners gain insight into how to validate synthetic data for realism, balance privacy with utility, and integrate it responsibly into the AI lifecycle. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.
