The continuous evolution of encryption-based threats has pushed the development of robust and adaptive detection frameworks capable of addressing the complexities introduced by modern attack strategies. In this study, a novel approach leveraging cryptographic execution profiling is proposed, focusing on runtime behavioral telemetry to identify anomalous encryption patterns associated with malicious activities. The method employed entropy analysis, timing metrics, and clustering-based anomaly detection to capture distinctive features of malicious encryption processes, enabling precise differentiation between benign and harmful operations. Experimental evaluations demonstrated high detection accuracy across diverse ransomware families, achieving superior performance compared to traditional signature-based and heuristic approaches. The modular and lightweight architecture of the framework facilitated efficient deployment across heterogeneous environments. Comparative analyses highlighted the framework's capability to address the limitations of machine learning-based detection systems, particularly in terms of data dependency and operational complexity. Resource utilization metrics revealed consistent memory usage and manageable CPU demands. Entropy pattern analyses uncovered irregular but distinct distributions across various encryption schemes, reinforcing the method's effectiveness in isolating ransomware behaviors. The findings contribute a transformative perspective to behavioral detection strategies, showing the importance of telemetry-driven approaches in enhancing the resilience of cybersecurity systems.