Ransomware attacks are a growing threat, impacting individuals, businesses, and organizations globally. Understanding the tactics used by ransomware operators in the pre-encryption phase is essential for developing effective defenses. This research investigates the pre-encryption tactics, techniques, and procedures (TTPs) employed by attackers before they encrypted data. Through a comprehensive analysis of real-world incidents and malware samples, the study identifies common attack patterns across various stages of the attack lifecycle, including initial access, reconnaissance, privilege escalation, and lateral movement. By studying these patterns, organizations can enhance their threat intelligence and strengthen their defenses. The research introduces a heuristic-based pre-encryption ransomware detection (HB-PERD) method, leveraging machine learning to improve detection rates and reduce false positives and negatives. This approach offers valuable insights for cybersecurity professionals, incident responders, and policymakers to implement proactive measures and reinforce access controls, ultimately aiding in the defense against evolving ransomware threats.