The rapid adoption of hybrid cloud environments blending public and private infrastructures has transformed enterprise IT architectures but also introduced new layers of complexity to cybersecurity. As organizations increasingly store sensitive data and run mission-critical workloads across distributed clouds, adversaries exploit misconfigurations, insecure APIs, lateral movement opportunities, and multi-tenancy vulnerabilities. Traditional security controls are insufficient against the sophistication of these evolving threats, underscoring the need for an integrated, multi-layered defense framework. This study presents a comprehensive model for enhancing cloud security postures through layered detection and mitigation strategies tailored for hybrid environments. The framework integrates network segmentation, identity and access management, behavioral analytics, and continuous compliance monitoring with advanced detection capabilities such as AI-driven anomaly detection, zero-trust enforcement, and automated threat intelligence sharing. By adopting defense-in-depth principles, the model ensures visibility, resilience, and adaptive response across heterogeneous infrastructures. Case analyses from financial, healthcare, and manufacturing enterprises illustrate the effectiveness of the proposed framework in mitigating insider threats, data exfiltration attempts, and cross-cloud lateral attacks. The findings highlight measurable improvements in reducing mean time to detection (MTTD), strengthening regulatory compliance, and enhancing operational continuity. At a broader level, the framework contributes to the strategic objective of building proactive, intelligence-driven cloud security postures. Narrowing the focus, it emphasizes governance mechanisms, automation, and orchestration tools that enable organizations to manage risks holistically across hybrid ecosystems while adapting to the evolving cyber threat landscape.