Episode 12 — Fine-Tune Safely: Epochs, Learning Rates, and Catastrophic Forgetting Risks
This episode teaches fine-tuning as a controlled engineering activity with security consequences, not a casual “make it better” step, because SecAI+ expects you to understand how tuning choices can change behavior, expose data, and increase risk. You will learn what epochs and learning rates mean operationally, how they influence convergence and overfitting, and why a tuning run that is too aggressive can destabilize a model’s safety behavior or degrade performance in previously reliable areas. We will explain catastrophic forgetting as a real risk where a model loses important general capability when narrowly tuned, then connect that to security and compliance failures such as inconsistent policy responses, broken classification logic, or unexpected handling of sensitive inputs. You will also practice selecting safe tuning approaches, including using carefully scoped datasets, maintaining strict separation between tuning data and evaluation data, capturing reproducible configurations, and defining acceptance tests that explicitly include safety and privacy requirements. The goal is to help you answer exam questions about tuning tradeoffs and to avoid production regressions that look like “mystery behavior” later. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
