Vol. 7 No. 2 (2023): International Research Journal of Innovative Engineering (IRJIE)

Cloud computing environments have become the backbone of modern digital infrastructure, enabling scalable computing, distributed storage, and on-demand services. However, this rapid adoption has significantly expanded the attack surface, making cloud platforms prime targets for advanced cyber threats such as misconfiguration attacks, API exploitation, ransomware, and insider threats. Traditional security mechanisms struggle to keep pace with the speed, scale, and sophistication of these evolving threats

This paper proposes an AI-powered threat intelligence framework designed to enhance automated cloud security monitoring and response systems. The framework integrates machine learning, deep learning, and real-time behavioral analytics to detect anomalies, predict potential attacks, and enable autonomous response actions. By leveraging continuous threat intelligence feeds and security telemetry from cloud environments, the system dynamically adapts to emerging attack vectors

The study emphasizes the role of AI in improving detection accuracy, reducing false positives, and enabling near real-time incident response through automation. Additionally, it explores how orchestration tools and security information and event management (SIEM) systems can be enhanced using AI-driven decision-making models.

The proposed framework aims to strengthen cloud resilience, minimize human intervention, and improve overall cybersecurity posture in highly dynamic cloud ecosystems

Enterprise customer relationship management (CRM) environments are increasingly required to be seamlessly integrated with heterogeneous enterprise systems, including enterprise resource planning (ERP), billing, configure-price-quote (CPQ), customer portals and legacy applications and environments. Deep point-to-point integration with Salesforce can be tightly coupled, contain redundant integration code, lack scalability, and be complex. I will propose a reference architecture of API-directed connectivity in enterprise CRM in this paper that are based on three complementary layers of API layers: System APIs, Process APIs and Experience APIs, Salesforce being the primary CRM interaction layer. The abstracted enterprise systems are system APIs, which offer reusable services, and Process APIs are used to coordinate between systems. Experience APIs are Salesforce, customer portal and consumer-specific interfaces to external applications. The architecture has encompassed API security, lifecycle management, integration patterns and observability features to improve on reliability, traceability and operational control. The new framework offers a systematic way of merging the heterogeneous enterprise resources and decreasing system dependency and encouraging reuse of API. How the layered API-connectivity might provide scalable integration, controlled data exchange, and standard operations of business via the enterprise CRM ecosystems is also discussed in the paper. The reference architecture provides beneficial foundation to organizations seeking credible, lasting and monitors Salesforce integrating environment.