Building Safe and Secure AI: From Computer Architecture to Cloud Computing

Abstract

Abstract: As artificial intelligence (AI) technologies rapidly evolve and permeate various sectors, ensuring their safety and security has become paramount. This paper presents a comprehensive examination of the frameworks, methodologies, and best practices necessary for developing safe and secure AI systems. The discussion spans multiple domains, including computer architecture, data management, and cloud computing. We explore the challenges and vulnerabilities inherent in traditional architectures and identify how modern AI frameworks can address these weaknesses. Specifically, the paper highlights the importance of adopting secure coding practices, robust data governance, and continuous monitoring of AI systems to mitigate risks associated with adversarial attacks and data breaches. Furthermore, we discuss the role of cloud computing in providing scalable resources for AI development while emphasizing the need for secure cloud environments. By integrating principles of secure software engineering and machine learning, this paper proposes a holistic approach to building AI systems that are resilient against threats. Through case studies and empirical evidence, we illustrate the effectiveness of various security measures and architectural designs in real-world applications. Ultimately, this work aims to provide a roadmap for researchers and practitioners in the AI field, guiding them towards creating systems that not only perform optimally but also uphold the highest standards of safety and security. As AI continues to shape our future, it is essential that we prioritize the development of technologies that inspire trust and safeguard user interests.