The development of advanced detection frameworks has become critical in addressing the escalating sophistication of cyber threats targeting data security and operational stability. Hierarchical Threat Signal Profiling (HTSP) introduces a novel approach that integrates autonomous profiling mechanisms with hierarchical modeling techniques to enhance threat detection capabilities. The framework achieves precise identification of malicious activities through layered analysis of signal behaviors, effectively addressing limitations in traditional signature-based and heuristic-based methods. Detection performance was rigorously evaluated across multiple ransomware families, demonstrating high accuracy and low false positive rates in diverse testing scenarios. Behavioral profiling within the HTSP framework leverages multi-dimensional analysis to distinguish unique ransomware patterns, offering actionable insights into malicious operations. Modular system architecture ensures scalability and compatibility with existing cybersecurity infrastructures, while maintaining efficiency in high-throughput environments. Resource monitoring revealed the potential of CPU utilization and encryption speeds as auxiliary indicators for real-time detection. The framework also excels in identifying anomalies in network traffic and file access patterns, further strengthening its operational reliability. Advanced techniques, such as dynamic profiling and predictive analytics, enhance the framework's adaptability to emerging attack strategies, ensuring comprehensive protection against evolving threats. Experimental results affirm its capacity to handle diverse challenges, positioning the HTSP framework as a groundbreaking solution for securing critical digital assets. The research demonstrates the importance of autonomous systems in modern cybersecurity paradigms and sets a strong foundation for continued innovation in proactive threat mitigation.