Qualys

This study delves into applying blockchain technology to maximize trust and efficiency when it comes to the process of verifying insurance claims, in health and motor car insurance markets in this case. Through the application of a permissioned blockchain network, the study proves how blockchain transparency and decentralization can facilitate processing claims yet maintain data integrity and minimize fraud. Four core algorithms—Blockchain Consensus, Smart Contracts, Fraud Detection, and Claim Validation—were developed and implemented. The tests registered a reduction of 30

This study examines how generative AI models can be utilized for the process optimization of the semiconductor wafer design and predict the yield leading to the semiconductor wafer design. The study pays attention to the use of deep learning algorithms, such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and reinforcement learning, to advance semiconductor manufacturing efficiency, precision, and yield. A number of AI methods were used to optimize the wafer design, identify the malfunctions, and calculate the yield according to the process parameters. As the result showed, the AI-driven models were better at the defect detection and the yield prediction than the traditional approaches, as the difference in the accuracy of the established models compared to the conventional models constituted 15

Artificial Intelligence (AI) platforms are increasingly embedded within enterprise internet-of-things (IoT) and cloud systems, introducing critical security vulnerabilities related to prompt injection, inference abuse, and sensitive data leakage. These risks are amplified in cloud-native environments where distributed architectures complicate centralized control enforcement. This paper proposes a layered security architecture that operationalizes the Sherwood Applied Business Security Architecture (SABSA) framework alongside active DevSecOps engineering pipelines. To bridge the gap between abstract risk governance and technical implementation, we implement an empirical validation framework on an AWS environment using a local DistilGPT-2 instance executing simulated adversarial vectors. Unlike existing qualitative security models, our architecture introduces empirical performance benchmarks evaluated against the NIST AI Risk Management Framework (AI RMF) and structured Zero Trust parameters. Controlled experimental assessments demonstrate that integrating multitier string pattern-matching logic achieves a robust absolute mitigation rate against direct prompt injection payloads while bounding inference processing runtime overhead strictly under 41.6ms. Continuous deployment validations executed via IaC Checkov scanning demonstrate robust multi-layered architectural traceability, satisfying rigorous verification metrics necessary for scalable cloud-based AI platform assurance.

This research discusses the combination of the “federated learning and blockchain technology to optimize fraud detection” in the cross-border transactions. With the increase in digital payment systems across the world, the need for safe and privacy-involving solutions rises to the fore. The federal learning does not share the sensitive data but is able to train the machine learning models in multiple organizations at the same time which is a novel approach incorporating in fraud detection by the nature of blockchain which is decentralized and immutable. Four Machine Learning algorithms, namely, XGBoost, CatBoost, Random Forest and Logistic Regression were attempted and assessed in terms of their performance in a Federated Learning scenario. “The results of the experiment showed that the federated learning was superior to the traditional learning, which demonstrated the accuracy of 94.7

An AI and MLOps innovator with a decade of experience in designing and deploying scalable, secure, and intelligent AI-driven systems. This work focuses on pioneering advancements in AI infrastructure, MLOps automation, and cloud-scale data platforms that empower organizations to operationalize machine learning with efficiency and confidence. With a strong foundation in data engineering, the approach emphasizes building end-to-end pipelines that ensure data integrity, model reproducibility, and seamless deployment. Key contributions include the integration of robust CI/CD frameworks for ML, scalable data ingestion and processing architectures, and governance mechanisms to support compliance and security. This journey reflects a commitment to driving the next generation of intelligent systems by aligning technological innovation with real-world business outcomes.