2026· American International Journal of Computer Science and Technology· 0 citations
TL;DR
A Cloud-Native, Security-centric and Compliant AI-Driven Automation Framework for ServiceNow is articulated, which allows for various AI services to be developed, operated and maintained safely, securely and in a controlled responsive manner aligned to AI Governance principles throughout the lifecycle.
Abstract
Enterprises are transforming to embrace Artificial Intelligence (AI) and accelerate business outcomes. The application of AI technology needs to occur in a controlled environment, with appropriate risk management in place to ensure longevity of the systems. Hence the desired outcome is to adopt an AI governance framework mapped to an enterprise service delivery solution that supports the detection and remediation of trust, safety, security and compliance risks relating to the AI operations across its lifecycle, as well as in the data used to train, develop and run the AI models. Therewith, a Cloud-Native, Security-centric and Compliant AI-Driven Automation Framework for ServiceNow is articulated. The framework allows for various AI services to be developed, operated and maintained safely, securely and in a controlled responsive manner aligned to AI Governance principles throughout the lifecycle. The framework operates within a Cloud-Native model with all applications comprising SaaS/IaaS/PaaS being developed and deployed under the principles of zero-trust with least-privilege identity and access management considerations. The completed framework provides the foundation for continuously improving the Security-Centric and Compliance controls.
Cloud security operations must process high-volume, rapidly changing telemetry within short response windows while maintaining governance and accountability. However, the literature on cloud defense, SOC modernization, retrieval-augmented generation (RAG), and Agentic AI remains fragmented. This paper combines a legacy...
Abdulaziz Al Humaidi, Faisal A. Al-Qadda, Albandari Alsumayt et al.· Network· 0 citations
An AI-driven DevSecOps approach focusing on governance with connections between Secure Software Development, Software Provenance, Runtime Observability, AI for analysis, Deterministic Policy Enforcement, and Responsible Human Decision Making is proposed.
Mir Fawad, Mir Jawad Yaqoob, Khawar Muhammad Saad· American Journal of Engineer...· 0 citations
A clear governance model can enable sustainable Cloud AI Automation for the enterprises and the ecosystem through the consideration of a Cloud Automation Control Plane that governs the core events and processes of Cloud Automation on behalf of initiation parties.
Sridhar Mahadevan· International journal of com...· 0 citations
A Security-by-Design and risk-based certification framework that combines a six-layer IoT-AI reference architecture with STRIDE-based threat analysis augmented to capture AI-specific threats, including prompt injection and data poisoning is proposed.
Iván Ortiz-Garcés, Roberto O. Andrade· Future Internet· 0 citations
The fast adoption of enterprise infrastructures into distributed, cloud-native, and API-based ecosystems has presented sophisticated security requirements that cannot be effectively handled with more traditional frameworks based on perimeters and rules. The growing number of multi-cloud ecosystems, micro services, Inte...
Supreet Nagi, M. Panesar, Svarmit Singh Pasricha· Discover Internet of Things· 0 citations