## 1 Introduction  
The rapid advancement of ICT (information and communication technologies), including AI (Artificial Intelligence), IoT (Internet of Things), and telecommunications, has led to an exponential increase in data generation and utilization [1, 2]. Ensuring robust data security is essential, particularly for sensitive information such as financial records, medical data, and digital multimedia content, which are vulnerable to piracy, copyright infringement, and data breaches [3, 4]. As data size and complexity grow, efficient computational methods and strong encryption algorithms have become indispensable [5, 6]. The surge in big data has introduced challenges in maintaining data confidentiality and integrity during storage, transmission, and processing [7]. Traditional CPU (Central Processing Unit)-based encryption methods, though reliable, often struggle with the computational demands of large-scale operations, leading to performance bottlenecks [8, 9]. Parallel computing solutions, particularly GPUs (Graphics Processing Units), have emerged as effective tools due to their high throughput and parallel processing capabilities [10]. GPUs, initially designed for graphics rendering, have evolved into versatile parallel processors capable of handling complex mathematical operations, making them suitable for cryptographic tasks [2; 11; 12]. GPU acceleration enhances encryption and decryption processes and supports real-time data security applications in cloud computing, multimedia streaming, and secure communications [13; 14; 15]. AES (Advanced Encryption Standard) [9; 10], standardized by NIST (National Institute of Standards and Technology), remains a cornerstone of symmetric encryption for its balance of security, performance, and adaptability [16]. AES-128 is widely used for its efficiency and strong security [17]. However, the sequential nature of CBC (Cipher Block Chaining) mode poses challenges for parallel encryption, whereas CTR (Counter Mode) mode's inherent parallelism is well suited for GPU acceleration [18]. This study benchmarks AES encryption in CBC and CTR modes across multiple CPU and GPU models, comparing their encryption performance. It emphasizes the benefits of GPU acceleration and presents a hybrid CPU-GPU workflow that optimizes performance and resource utilization based on data size and operational requirements. The growing demand for scalable and efficient encryption solutions in data-intensive applications underscores the significance of this study. By harnessing the parallelism of GPUs and the flexibility of CPUs, the proposed hybrid approach balances speed and resource efficiency, making it suitable for modern encryption needs. Experiments applied AES encryption in CBC and CTR modes across different CPU and GPU hardware models, analyzing performance under varying data sizes. Performance data and mathematical models were used to calculate encryption times, GPU resource utilization, and cost-performance metrics. Results demonstrated that GPUs significantly accelerate large-scale data encryption, while the hybrid CPU-GPU approach enhances performance for specific workloads. This study highlights the importance of hardware selection for cryptographic tasks and illustrates the effectiveness of integrating theoretical models with performance analysis. Practical applications of the proposed hybrid workflow include cloud computing, data centers, and real-time processing systems, with detailed background on GPU architecture, encryption challenges, and prior research on parallel cryptographic processing to provide a comprehensive context. The existing CPU-only encryption methods face limitations in processing large-scale data efficiently, while GPU-only methods can suffer from suboptimal resource utilization and I/O bottlenecks. This study aims to bridge this gap by proposing and evaluating a hybrid CPU-GPU encryption workflow.