August 1, 2025Open Access

Integrating Artificial Intelligence into Cloud Security: A Layered Framework for Threat Detection, Compliance, and Automation Across the SDLC

Key Points

MAIN FINDING: Integrating artificial intelligence improves cloud security and enhances threat detection.
KEY EVIDENCE: The framework enables more effective anomaly detection within the software development lifecycle.
APPROACH: This work examines tools for security automation focusing on AI-driven anomaly detection models.
SIGNIFICANCE: Implementing AI in cloud security can significantly reduce attack surfaces and enhance compliance.

Abstract

Cloud Computing provides a new landscape for application development and infrastructure management, and introduces a dynamic thread space due to its distributed and elastic nature. Classic security methods are not enough to fight off cyber-threats despite modern agile and cloud-native environments. This paper explores how to integrate Artificial Intelligence (AI) in an efficient manner across the Software Development Life-Cycle (SDLC) that would enhance cloud security. Add more-attractive threat detection, anomaly discovery, and policy-based automation into the development, testing, deployment, and runtime life cycles, so that you can detect threats more quickly, as well as more proactively break down risk. In this work we explore the state of the art and tools of security automation, with a particular focus on AI models for anomaly detection for security and case demonstrations of its real applications. A holistic framework for AI-driven security orchestration in Cloud application Pipelines: Towards Reducing Attack Surfaces, Compliance and Resilience.

Integrating Artificial Intelligence into Cloud Security: A Layered Framework for Threat Detection, Compliance, and Automation Across the SDLC

Key Points

Abstract

Cite This Study

Also Consider

Also Consider