Ransomware attacks continue to evolve, with increasingly complex cryptographic payloads designed to evade detection and disrupt systems. A novel approach has been developed to address the challenge of evaluating the quality of true random bit-streams embedded within ransomware payloads, offering a fully automated method that eliminates the need for human intervention. Through entropy analysis and advanced randomness test suites, the methodology assesses the unpredictability and cryptographic strength of ransomware bit-streams, providing an objective measure of randomness. The findings reveal significant variability in the quality of randomness across different ransomware families, with some exhibiting potential cryptographic vulnerabilities. By leveraging mathematical models and statistical tools, the approach ensures scalable and efficient analysis, particularly important in high-volume ransomware datasets where traditional methods prove inadequate. The results have direct implications for improving ransomware detection and prevention strategies, particularly through identifying payloads that deviate from expected randomness levels. The research demonstrates the importance of randomness in ransomware encryption, offering valuable insights for both cybersecurity experts and automated detection systems.