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Santosh Kumar Vududala

Concord, United States

Santosh Vududala is a GenAI Automation Architect and QA Engineering Manager with over 18 years of experience in software quality engineering, intelligent automation, data engineering, and enterprise technology solutions. His work focuses on Generative AI, AI quality engineering, Retrieval-Augmented Generation (RAG), large language models (LLMs), and trustworthy AI systems.

He specializes in designing and validating AI-powered enterprise applications, developing automation frameworks, and establishing quality engineering strategies for complex business systems. His technical interests include AI testing and evaluation, agentic AI, cloud-based AI architectures, data validation, and the application of intelligent automation to real-world business challenges.

Santosh is an active researcher and technical author with 30+ research publications, including IEEE papers, and has contributed to scholarly peer review through Web of Science. He is an IEEE Senior Member and participates in technical conferences, professional communities, and knowledge-sharing activities.

He is also passionate about mentoring technology professionals and helping practitioners understand emerging AI technologies. Through research, technical presentations, mentoring, and community engagement, he aims to advance practical, reliable, and responsible applications of Generative AI and intelligent automation.

Santosh Kumar Vududala Points
Academic 0
Author 330
Influencer 0
Speaker 0
Entrepreneur 0
Analyst 0
Total 330

Points based upon Thinkers360 patented algorithm.

Thought Leader Profile

Portfolio Mix

Company Information

Areas of Expertise

AI 34.26
AI Governance
Big Data
Blockchain
Cloud 37.45
Cybersecurity 31.58
DevOps
Emerging Technology 30.67
Generative AI 39.74
Predictive Analytics

Industry Experience

Publications & Experience

21 Analyst Reports
Blockchain and AI Fusion for Financial Traceability and Cyber-Resilience in Industry 5.0 Environments
IEEE
March 06, 2026
This paper proposes a hybrid Blockchain-AI framework to improve financial traceability, fraud detection, and cyber-resilience in Industry 5.0 environments.

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Tags: AI, Cybersecurity, Generative AI

Smart Mobile Phone Malware Detection with KELM Model
IEEE
January 02, 2026
Research on machine learning and deep learning approaches for detecting mobile phone malware using a Kernel Extreme Learning Machine (KELM) model, with the goal of improving mobile security and identifying evolving cyber threats.

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Tags: AI, Cybersecurity, Generative AI

Early Earthquake Prediction Using Machine Learning Technique for North Part of India Saving Human Life
Journal of Information Systems Engineering and Management
October 10, 2025
This paper presents a machine learning-based approach for early earthquake prediction using seismic data from different regions of India, with XGBoost demonstrating strong predictive performance.

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Tags: AI, Cloud, Generative AI

Electro Cardio Gram Using Different Machine Learning Techniques for Early Heart Attack Prediction
Journal of Neonatal Surgery
September 05, 2025
his paper presents a machine learning-based approach for analyzing ECG data to enable early prediction of heart attacks and support timely cardiovascular risk detection.

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Tags: AI, Cybersecurity, Generative AI

Real-Time Cloud-Native Processing Techniques for Enhancing Federated Learning in Distributed Applications
IEEE
July 17, 2025
This paper presents a cloud-native framework for real-time federated learning, integrating microservices, streaming, and Kubernetes to improve scalability, latency, and system performance in distributed applications.

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Tags: AI, Cloud, Generative AI

DataOps Meets LLMOps: Automating Cloud-Based AI Workflows from Data Ingestion to Prompt Optimization
IEEE
July 11, 2025
This paper presents a unified DataOps-LLMOps framework that automates cloud-based AI workflows from data ingestion and validation to model management and prompt optimization.

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Tags: AI, Cloud, Generative AI

Federated Data Modeling for LLM Deployment in Secure Cloud-Native Architectures
IEEE
July 04, 2025
This paper proposes a Federated Data Modeling framework for secure, privacy-preserving, and scalable LLM deployment across distributed cloud-native environments

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Tags: AI, Cloud, Generative AI

Secure LLM-Oriented Data Engineering Pipelines for Scalable AI Workflows in Multi-Cloud Environments
IEEE
July 04, 2025
This paper presents a secure and scalable LLM-oriented data engineering framework for multi-cloud environments, integrating federated orchestration, metadata management, zero-trust security, and automated AI workflows

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Tags: AI, Cloud, Cybersecurity

Federated Learning Meets Data Engineering: Building Trustworthy AI with Decentralized Data Pipelines
IEEE
June 27, 2025
This paper explores how Federated Learning can support trustworthy data engineering by enabling secure, privacy-preserving, and decentralized data processing.

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Tags: AI, Cybersecurity, Generative AI

Hybrid AI Models for Privacy-Preserving Big Data Analytics in Distributed Environments
IEEE
May 09, 2025
Short Description:
This paper presents a hybrid AI framework combining Federated Learning, Homomorphic Encryption, and Swarm Intelligence for secure, privacy-preserving, and scalable big data analytics in distributed environments. Hybrid AI Models for Privacy-Pr…

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Tags: AI, Cybersecurity, Generative AI

Federated Learning Meets Swarm Intelligence: A Privacy-Centric Framework for Big Data Processing
IEEE
May 09, 2025
Short Description:
This paper presents a privacy-centric framework that combines Federated Learning and Swarm Intelligence to enable secure, scalable, and optimized big data processing across distributed environments.

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Tags: AI, Cybersecurity, Generative AI

Leveraging TOSCA Automation Testing for Streamlining Enterprise Processes in Capital Markets
International Journal of Innovative Research and Creative Technology
February 07, 2025
This paper explores how TOSCA automation testing streamlines capital market operations by reducing manual interventions, improving transaction accuracy, accelerating workflows, and strengthening regulatory compliance through model-based testing.

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Tags: AI, Cloud, Generative AI

Enhancing Anti Money Laundering (AML) with Mantas Oracle A Modern Behavior Detection Approach
IJFANS International Journal of Food and Nutritional Sciences
January 19, 2024
This paper examines how Oracle Mantas uses AI-driven behavioral analytics, real-time transaction monitoring, and anomaly detection to strengthen anti-money laundering (AML) compliance, improve suspicious activity detection, reduce false positives, and enhance operational efficiency in financial institutions.

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Tags: AI, Cloud, Emerging Technology

INFORMATICA INTELLIGENT CLOUD SERVICE (IICS)
IJFANS INTERNATIONAL JOURNAL OF FOOD AND NUTRITIONAL SCIENCES
January 05, 2024
This paper examines Informatica Intelligent Cloud Services (IICS), focusing on AI-powered data integration, cloud data governance, automation, and multi-cloud connectivity. It discusses the CLAIRE AI engine, key benefits, implementation challenges, and best practices for improving enterprise data management and operational efficiency.

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Tags: AI, Cloud, Generative AI

Workday Prism Analytics Transforming Data Integration and Business Intelligence
International Journal of Engineering and Techniques
February 10, 2023
This paper explores Workday Prism Analytics as a cloud-based platform for integrating and analyzing Workday and external data. It discusses its architecture, data transformation, security, real-time reporting, business intelligence benefits, and implementation challenges for enterprise data-driven decision-making.

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Tags: AI, Cloud, Generative AI

Enhancing Image Detection and Classification with Artificial Intelligent and ML Models
International Journal of Food and Nutritional Sciences
January 02, 2023
This paper explores AI- and ML-based image detection and classification using deep learning techniques such as Convolutional Neural Networks (CNNs), Transfer Learning, and Vision Transformers (ViTs). It highlights improvements in detection accuracy and classification efficiency, with applications in healthcare, security, agriculture, and autonomous systems.

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Tags: AI, Cloud, Generative AI

QA Role and Challenges in Robotics Automation Testing
International Journal of Computer Techniques
July 08, 2022
This paper examines the role of Quality Assurance (QA) in robotics automation testing, highlighting challenges in hardware-software integration, real-time processing, sensor accuracy, AI-driven decision-making, and safety compliance. It explores testing approaches such as digital twins, hardware-in-the-loop testing, and AI-driven automation to improve robotic system reliability, performance, and safety.

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Tags: AI, Cloud, Generative AI

Robotic Process Automation (RPA) Testing Using Automation Anywhere - A Study on Task Bots MetaBots and IQ Bots
International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences
January 07, 2022
This paper examines how Automation Any where's Task Bots, Meta Bots, and IQ Bots improve software testing by automating repetitive tasks, enabling reusable test components, and applying AI-driven insights to enhance test accuracy, coverage, efficiency, and software quality.

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Tags: AI, Cloud, Generative AI

Automating the Future - Enhancing ETL Workflows with RPA and Intelligent Automation
International Journal of Innovative Research and Creative Technology
March 05, 2021
This paper explores how Robotic Process Automation (RPA) and Intelligent Automation (IA) can improve ETL workflows by reducing processing time, minimizing data errors, and enhancing scalability. It presents an automation framework for data extraction, transformation, loading, validation, and monitoring, highlighting the potential benefits of integrating AI and automation into modern data management systems.

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Tags: AI, Cloud, Generative AI

Intelligent Healthcare Systems - The Role Of Machine Learning and Deep Learning In Modern Medical Applications
International Journal of Food and Nutritional Sciences
January 01, 2021
This paper explores how machine learning and deep learning improve healthcare through disease prediction, medical image analysis, real-time patient monitoring, and personalized treatment. It evaluates models such as Random Forest, CNN, ResNet-50, and LSTM to support faster, more accurate diagnosis and better patient outcomes.

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Tags: AI, Cloud, Generative AI

Enhancing Software Quality through Automation Testing with Unified Functional Testing (UFT)
International Journal Of Core Engineering & Management
July 17, 2020
short desc
This paper discusses how Unified Functional Testing (UFT) automation improves software quality by increasing test coverage, accelerating test execution, improving defect detection, and supporting efficient regression testing in Agile and DevOps environments.

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Tags: AI, Emerging Technology, Generative AI

2 Books
The AI Cloud Infrastructure Blueprint
Taylor and Francis Group
July 11, 2025
This book provides a practical guide to designing and managing cloud infrastructure for scalable, secure, cost-effective, and reliable AI model development and deployment.

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Tags: AI, Cloud, Generative AI

Cloud Infrastructure Requirements for Effective AI Model Execution
From Code to Consciousness: Leveraging AI in Software Development
July 04, 2025
This book provides a practical guide to cloud infrastructure for AI, covering AI model deployment, scalability, performance optimization, security, governance, cost management, and MLOps for effective AI execution.

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Tags: AI, Cloud, Generative AI

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