The escalation of sophisticated encryption-based attacks has amplified the demand for advanced cybersecurity measures capable of proactively identifying and mitigating emerging threats. Current detection systems often rely on signature-based or behavior-based techniques, which, while effective against known threats, struggle to adapt to novel ransomware strains that deploy complex encryption mechanisms. The Dynamic Encryption Fingerprint Analysis (DEFA) framework introduces a transformative approach by autonomously identifying ransomware through unique encryption fingerprints, circumventing the limitations of static signature reliance. Employing advanced feature extraction and adaptive anomaly recognition, DEFA achieves high detection accuracy across known and unknown ransomware variants, demonstrating resilience against polymorphic and evolving threats. Comprehensive evaluations indicate that DEFA not only surpasses traditional detection methods in both accuracy and adaptability but also minimizes false positives and false negatives, showing its suitability for integration into real-time cybersecurity infrastructures. The framework's robust performance, combined with its capacity for autonomous threat recognition, suggests a significant potential impact on the future landscape of ransomware detection and broader cybersecurity defenses.