Analyzing the efficiency of AI-powered encryption solutions in safeguarding financial data for SMBs
Year: 2024
Type: article
Abstract: The study explores the significance of artificial intelligence (AI) in advancing encryption technologies, focusing on small and medium-sized businesses (SMBs) and their financial data security needs. As cyber threats evolve and quantum computing looms on the horizon, traditional encryption methods—such as symmetric encryption (AES) and asymmetric encryption (RSA)—are increasingly challenged. The study assesses how AI-driven encryption solutions address these challenges and enhance data protection. Key objectives include reviewing the effectiveness of traditional encryption methods and comparing them with cutting-edge AI-powered approaches. The methods reviewed encompass homomorphic encryption, which allows for computations on encrypted data without decryption, quantum-resistant algorithms designed to withstand quantum computing threats, and adaptive encryption that adjusts security measures based on real-time risk assessments. Major findings indicate that while traditional encryption methods remain foundational, they are often insufficient to address modern threats and future uncertainties. AI-enhanced solutions offer significant improvements, such as real-time threat detection, scalability, and adaptive security. In particular, homomorphic encryption and quantum-resistant algorithms present promising advancements for protecting sensitive financial data against emerging threats. The study highlights the practical implications of integrating AI into existing encryption infrastructures, including potential cost implications, scalability challenges, and the need for compatibility with legacy systems. It underscores the importance of a hybrid approach, combining traditional and AI-driven encryption methods, to build a resilient and future-proof security framework for SMBs. This approach ensures robust financial data protection in a rapidly evolving digital landscape.
Institution: Worcester Polytechnic Institute
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949568600
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