What Cybersecurity Acquisitions Actually Change (and What They Don’t)
Cybersecurity acquisitions promise simplification but usually introduce a long integration gap where complexity increases and risk quietly accumulates.
ETR-2: AI & ML for Threat Detection Lab Overview In this lab, you’ll apply machine learning techniques to detect anomalies in log data—mimicking how AI supports SOC teams. You’ll use Python tools such as scikit-learn or PyCaret to train unsupervised models and compare results to traditional rule-based detection.
Lab Instructions 1. Setting Up Your Environment You will need:
Python 3.9+ Jupyter Notebook or Google Colab Install required libraries: pip install pandas matplotlib seaborn scikit-learn pycaret Download a sample log dataset (or use the instructor-provided logs). Suggested sources:
...ETR-2: AI & ML for Threat Detection Lab