Security and Privacy Challenges of AI Deployment in Cloud Computing: A Systematic Literature Review - Hasan, Mochammad Fuad (Universitas Komputer Indonesia, 2025)
The rapid adoption of artificial intelligence (AI) within cloud computing environments has introduced significant security and privacy challenges that demand systematic examination. This study presents a systematic literature review on security and privacy challenges in deploying Artificial Intelligence (AI) within cloud computing environments. The integration of AI and cloud platforms enables scalable intelligent services across various domains, but also introduces significant risks, including data leakage, insecure APIs, model extraction, adversarial attacks, and privacy inference threats. Following PRISMA-inspired guidelines, relevant studies published between 2019 and 2025 were systematically identified from major academic databases and analyzed using thematic synthesis. The review categorizes key security and privacy threats, summarizes commonly adopted mitigation strategies, and examines cloud deployment architectures for AI workloads. The findings indicate that existing solutions are largely fragmented and often focus on isolated technical mechanisms without providing end-to-end security integration. Moreover, trade-offs between privacy preservation, system performance, scalability, and operational cost remain insufficiently addressed. This paper highlights critical research gaps and outlines future research directions toward building trustworthy, secure, and privacy-aware AI systems in cloud computing environments