The adoption of cloud computing technology has revolutionized the IT landscape, offering scalability, flexibility, and cost-efficiency. However, as organizations increasingly migrate their data and applications to the cloud, concerns about security have grown in tandem. This study presents a comprehensive analysis of cloud computing security challenges, offering a conceptual framework tailored to early adopters. The framework provides a structured approach to address security concerns, enabling organizations to harness the benefits of cloud technology while safeguarding their digital assets.

This study introduces an innovative approach, Earthquake Sentinel, utilizing Statistical Control Charts to monitor and unveil global seismic patterns. By leveraging statistical methodologies, our system aims to provide timely anomaly detection for seismic events, enabling proactive measures for risk mitigation and disaster preparedness. The methodology involves the continuous monitoring of earthquake frequency worldwide, identifying deviations from expected patterns, and triggering alerts for further investigation. Earthquake Sentinel contributes to the advancement of early warning systems, offering a valuable tool in the quest for improved global seismic risk management.

The retail business is experiencing difficulties in maintaining accurate inventory levels with low latency because of old batch processing systems. The proposed solution is a real-time application that uses the data stream architecture based on event-sourcing with Apache Kafka and Google BigQuery to provide streaming analytics. The immutability of the events makes it possible to reliably stream to Kafka such events as sales, returns, and stock adjustments. The scalability and fault-tolerant design of Kafka and the fact that BigQuery is serverless ensure a persistent foundation and real-time analytics in a sub-second query resolution. Significant improvements were the elaborate schema design, optimal Kafka topic setup, and introduction of real-time dashboards to gain insights into inventory. The results also show less than one-second update latency up to 100,000 events per second, and cost-efficient benchmarks of cloud infrastructure. The solution improves the real-time monitoring of the stock, thus reducing instances of out-of-stock and facilitating better decision making. The possibilities are immense for retail operations, providing better control of inventory management, pricing, and forecasting of demand. This model represents a significant step away from legacy batch ETL processes towards having the correct data as needed within retail to support a high-velocity, globalised sector. Future efforts will be dedicated to multi-region deployment, automatic schema change, and incorporating edge computing in offline shop scenarios.

The security use of DevSecOps is becoming very important to ensure the applications are secure throughout their life cycle, as the adoption of DevSecOps continues to rise. By incorporating security into the entire software development life cycle, DevSecOps can allow for more secure code at the earliest stage, thus minimizing vulnerabilities. This paper explores current challenges and innovations in DevSecOps, focusing on how scalable security integration is open to discussion. It outlines the most relevant obstacles, including the ineffectiveness of the cooperation between the teams of security and developers, challenges of choosing compatible tools related to security, and the security culture that should be shifted toward putting security on the development stage early. The study points out the significance of automated security testing, monitoring around the clock, and advanced security solutions that are an inseparable part of the development chain. The future research in the area of this study is aimed at increasing automation and integration of AI, modifying security procedures to work with a cloud-native architecture and microservices, and improving anomaly detection techniques. The study also suggests the need to close the skills gap in companies and the creation of efficient metrics to measure the effectiveness of DevSecOps implementations. The results contribute to the need for organizations to embrace security as a collective responsibility, enhancing the release cycles without compromising security. DevSecOps frameworks are evolving to become a decisive factor in the creation of secure, scalable, and effective software applications against the rising cybersecurity threats.

Aerospike is a low-latency NoSQL database, and an important application area is in high-frequency trading (HFT) and fraud detection in the financial services field. The paper will examine how Aerospike’s distributed architecture, in-memory storage, and a key-value data model make it fast, scalable, and reliable to process massive amounts of data in real-time. The latency between trade execution and order book management is kept to a minimum in HFT, where milliseconds mean the difference between profitability and loss, with Aerospike. It also enables real-time processing of the market data, which authorizes financial institutions to develop quick decisions that could maximize trading strategies. With machine learning being used in identifying fraud, Aerospike uses machine learning to read highly transactional data and detect fraudulent actions before they ever take place. Another prominent feature of Aerospike described in the paper is its scalability, which can provide data volumes that grow over time without compromising performance, and its compatibility with other financial technologies, including machine learning models and predictive analytics. This paper shows the comparison of the results of the experiments carried out in the study, where Aerospike proved its excellent performance relative to traditional relational databases, such as MySQL, and other databases like MongoDB and Cassandra, as NoSQL databases. These results show that Aerospike is far superior in terms of latency and throughput to these systems. In conclusion, Aerospike is a software that can prove helpful to financial institutions interested in strengthening real-time decision-making and fraud prevention capacities. Its fusion with other technologically advanced technologies, such as artificial intelligence, blockchain, and so on, could be researched in the future, also to enhance its functionalities as the world of financial services is changing rapidly.

Modern infrastructure teams have to impose least-privilege to multi-cloud environments and maintain privacy, all the while providing constant audit-readiness evidence. This paper introduces a portable governance architecture that (a) divide’s identity, decision, and enforcement; (b) integrates RBAC, ABAC, and ReBAC in a canonical schema; and (c) implements policy-as-code in terms of formal verification, static analysis, and safe roll-out patterns. Multi-source telemetry - cloud control-plane and cluster logs, identity provider events, and HRIS/ticketing data - are brought in as an entitlement graph to analyze reachability and problematic combinations. Policies are created in versioned repositories with unit/property testing, signed provenance, shadow evaluation, and progressive enforcement; recertifications of executed decisions also leave immutable evidence artifacts in auditable formats. Evaluation is provided with the use of KPIs, such as the least-privilege score, segregation-of-duties violation rate, policy coverage, review latency, and break-glass incidence. Experimental data show fewer emergency access points, infringement rates, faster reviews, and better policy coverage, as well as privacy-aware logging, tokenization, and differential-privacy budgets that limit exposure compared with heuristics and cloud-native analyzers. Operational guidance also involves a phased adoption playbook (pilot, guardrails), operational RACI of shared ownership, drift-reconciliation, and rollback runbooks. Limitations can be summarized as sporadic labels, imbalance of classes, semantic drift among providers, and telemetry gaps in legacy systems; mitigations can be characterized as time awareness validation, cost-sensitive thresholds, contract tests, and fallbacks. Future efforts will focus on sub-10-ms streaming PDPs, graph and unsupervised analytics with causal attribution, federated learning with privacy budgets, UX coupling assists drafting with verification gates, and synthetic datasets with simulated incidents.

Healthcare, environmental, and financial systems generate large volumes of data that require rapid processing and intelligent interpretation. Geospatial analytics (GA) provides spatial insights for risk mapping and resource allocation, but its potential is limited without optimized database architectures that ensure scalability, low latency, and data integrity. This study developed an integrated framework combining GA, database optimization, and predictive modeling for intelligent decision-support systems (DSS). Data from healthcare (EHR, patient flow), environment (compliance records), and finance (credit risk data) were collected, cleaned, geocoded, and processed. Optimized indexing, partitioning, and caching strategies were implemented. Predictive models and dashboards were developed, and stakeholder workshops validated usability and interpretability. The framework reduced query execution times by 42%, dashboard latency by 52%, and model inference runtime by 33%. Healthcare applications improved patient bed reallocation efficiency by 25%, environmental compliance coverage increased by 18%, and credit risk prediction accuracy improved by 9%. Stakeholders reported enhanced decision-making speed and clarity. Integrating GA and optimized databases enables scalable, real-time DSS that improve operational efficiency, compliance, and predictive accuracy across sectors, providing a model for future cross-domain analytics solutions.