In 2024, GitGuardian identified a 25% increase in hardcoded secrets on public GitHub, with 23.7 million new incidents discovered, highlighting the challenge of managing vast numbers of security alerts. To address this, GitGuardian employs a machine learning model to prioritize incidents based on risk, enhancing the efficiency of security teams by enabling them to identify and remediate critical threats three times faster than traditional methods. This model, trained on data labeled by experts, uses XGBoost to evaluate the contextual risk of secrets, focusing on factors such as location, type, and accessibility. As a result, it provides a more accurate and reliable alert ranking system that significantly reduces false positives and improves incident detection and management. This approach transforms the overwhelming task of handling security alerts into a manageable process, ensuring that critical threats are addressed promptly and effectively, thereby bridging the gap between detection and prevention in cybersecurity.