The forensic examination of email is a critical component in digital investigations, offering insights into various aspects of cybercrime, data breaches, and security incidents. This study explores innovative techniques and tools designed to enhance the efficacy of email forensic analysis. We review and evaluate state-of-the-art methods and technologies employed in the extraction, examination, and interpretation of email evidence, highlighting their contributions to improving investigative outcomes.

Our review covers advanced techniques such as email metadata analysis, message thread reconstruction, and content decryption, which are essential for uncovering hidden information and establishing the context of email communications. We also examine cutting-edge tools that facilitate the automated collection and analysis of email artifacts, including email forensic software and machine learning algorithms that enhance pattern recognition and anomaly detection. The integration of these tools into forensic workflows provides a more comprehensive and efficient approach to email investigation.

The study emphasizes the importance of maintaining data integrity and adhering to legal and ethical standards throughout the forensic process. We discuss the challenges and limitations associated with current tools and techniques, such as handling encrypted messages and managing large volumes of data. Additionally, we explore emerging trends and future directions in email forensics, including the application of artificial intelligence and blockchain technology. By synthesizing the latest advancements in email forensic techniques and tools, this study aims to provide a valuable resource for forensic investigators, cybersecurity professionals, and legal practitioners. The findings highlight the critical role of innovation in adapting to the evolving landscape of digital communication and ensuring robust investigative practices in the realm of email forensics.

This study explores the implementation of a facial recognition-based access control system for the Al-Iman Workshop to enhance security and efficiency in managing access. The use of biometric systems, particularly facial recognition, has become a popular solution for secure access in various sectors. This paper assesses the design and effectiveness of integrating facial recognition technology in an industrial workshop setting, addressing concerns such as accuracy, security, and user privacy. Data was collected from the system’s performance, including its ability to identify workers, control access, and reduce human error. Results indicate that the facial recognition system significantly improved security and streamlined the access process, with a marked decrease in unauthorized entries. The paper concludes with recommendations for further improvements and the potential broader application of biometric access control systems in similar settings.

The manufacturing industry is undergoing a significant transformation, with digitalization, automation, and the Internet of Things (IoT) paving the way for more intelligent, connected, and efficient production systems. However, as manufacturing systems become increasingly digitalized, they are also becoming more vulnerable to cyber threats. This article examines the key challenges and solutions in ensuring that cybersecurity strategies are fit for the future of manufacturing, focusing on the integration of emerging technologies, proactive security measures, and the importance of collaboration across sectors. By addressing the evolving nature of cybersecurity risks in manufacturing, we highlight the need for robust, adaptive, and resilient security frameworks that can protect future manufacturing systems.

Zero Trust Architecture (ZTA) is a crucial process to adopt in the evolving cybersecurity framework because of the changing IT environment where the demands of cloud computing, remote working, and working with mobile devices drive a change in architecture. Based on this, the continuous verification principle is adopted on top of the principle of "never trust, always verify," which fundamentally departs from perimeter-based security. Within Zero Trust, the idea of trusting an internal network is eliminated and treated as all systems and users from within and outside the network must be authenticated, authorized, and continually monitored. This study discusses the recent situations around Zero Trust, such as blending artificial intelligence (AI) and machine learning (ML) to improve adaptive security and predictive threat detection via behavioral analytics. Furthermore, it considers the projected technological impacts, specifically the possibility of quantum computing frustrating classical encryption methods and calling for quantum resistance. The paper also mentions the developed regulatory landscape of new regulations like GDPR and CCPA, which fit quite well with the Zero Trust principles of least privilege access and data protection. The Zero Trust model encourages every organization to mitigate cybersecurity risks by continuously innovating and adapting to new use cases in technology. It discusses practical difficulties such as legacy system integration and how you become scalable with a Zero Trust model. It stresses that the successful transition to a zero-trust model can only be done with security and compliance through a strategic, phased implementation approach.

In today’s world, it’s really important to keep employee data safe in ERP systems. This study looks at how to protect data in Human Capital Management (HCM) systems, focusing on how Workday keeps information secure. The paper explains how Workday’s security features, like customizable settings and role-based permissions, help protect company data. It also looks at how Workday follows international rules to make sure data stays safe. The study talks about two main types of security policies: domain security and business process security. Domain security decides who can see or use certain data. Business process security controls how users interact with important tasks, like hiring and payroll. Both policies work together to keep data safe and make sure good security practices are followed. The paper also points out the importance of setting up security controls that match the company’s needs. It also stresses that companies need to regularly check and update their security systems to keep data safe. The study shows that Workday’s security system does a good job of reducing risks in ERP-based HCM systems. But there are still challenges, like dealing with complex data, managing security, and connecting different systems. The paper offers advice on how to handle these problems. It also says that training programs are needed to help employees understand how to use the system properly. The paper suggests using role-based access to improve security. Looking to the future, it recommends using AI and machine learning to spot threats early and manage who can access sensitive data. By using these tools and improving security rules, companies can make their ERP systems even safer. Lastly, the paper gives practical tips for organizations to improve their security using Workday’s features.

The relentless demand for faster data rates and reduced end-to-end delays has driven the evolution of communication networks towards technologies like 5G and beyond [3, 5, 6, 11, 18, 19]. High-speed, low-latency applications, ranging from real-time gaming and augmented reality to autonomous systems and industrial automation, are critically dependent on efficient and predictable network performance. Traditional congestion control mechanisms, primarily embodied in variants of the Transmission Control Protocol (TCP), often react to congestion signals (like packet loss or delay) rather than predicting them [1, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 20, 21]. While various TCP variants have been developed for different network conditions [7, 8, 9, 10, 12, 13, 16, 17, 19, 20, 21] and machine learning has been explored for congestion control [5, 6, 18], a proactive approach through accurate prediction is highly desirable for meeting stringent low-latency requirements. Rough Set Theory (RST) [24, 25] is a mathematical framework for dealing with vagueness and uncertainty in data, offering powerful tools for attribute reduction and rule extraction [24, 25]. Although RST has been applied successfully in various prediction and classification tasks in other domains [24, 25], its application to predicting network congestion, particularly from the perspective of network towers supporting high-speed, low-latency traffic, is a novel area. This article proposes a design concept for a congestion prediction model leveraging RST, outlining its methodology, expected benefits in terms of interpretability and efficiency, and potential for deployment on network infrastructure to enable proactive congestion management and enhance high-speed, low-latency communication.

Due to the ever-increasing adoption of AI systems in the financial space, it is necessary to assess these regulatory frameworks, such as IEC 62304 and PCI DSS. As AI technologies within the finance sector process huge quantities of data that are sensitive, like transaction and personal information, these must be handled securely so that these are not breached or involve fraud—meeting the strict data security standards, privacy, and operation standards for a medical device software compliance with IEC 62304 and PCI DSS for payment card data security results. This article investigates how these compliance frameworks create the responsibility for designing, structuring, and building AI systems in financial institutions. It describes the technical problems in implementing real-time financial data processing and the issues addressed with cloud-native platforms, encryption, and data management applications. It discusses how, with technological advancements like large language models, Apache Kafka, and Apache Spark, the resulting financial AI systems can be compliance-driven and perform well. The article also delves into the ethical options of AI in finance and, in particular, data privacy, bias, and transparency. The conclusions include insights into the future of AI compliance with new technologies such as quantum computing and blockchain that will change the face of science. This study offers an actionable roadmap for companies to address the difficulties of regulatory compliance in the vein of AI’s potential fulfillment.

The paper looks at the utilization of Natural Language Processing (NLP) technologies in customer support and case management systems with a discussion about their role in operational efficiency and customer satisfaction. NLP is a branch of artificial intelligence (AI) that enables machines to understand, interpret, and generate human language to enable businesses to automate conversations that human agents otherwise handle. Using NLP, organizations can handle a high load of the customer’s requests and queries while offering quicker, more accurate, and customized support. NLP components, namely tokenization, sentiment analysis, and named entity recognition, are used within case routing, issue tracking, and status updates to remove the manual effort and resulting costs. The paper analyzes the NLP, AI, and Customer Relationship Management (CRM) systems synergy and the synergy between AI qualities and decision-making based on predictive analytics that further improves the case management processes. By the use of the NLP, businesses can accelerate resolving cases, prioritizing urgent cases, and provide better customer experience. Such as data privacy, model bias, and the need for human oversight, especially where customer interactions are complicated. Finally, the paper discusses future trends in the area of NLP models, chatbots, and virtual assistants based on their use of deep learning, as well as the possible development of fully automated customer service operations. These innovations will revolutionize ways the customer support functions can operate cost-effectively, efficiently, and on a scale that allows businesses to adapt to this new landscape of AI-powered service delivery.

Azure Entra Conditional Access is a first-class security product that enforces identity and access management policies in multi-tenant environments to implement secure access to the most important resources. Azure Entra lets businesses manage user IDs, enhance the protections, and reduce risks on a hybrid cloud infrastructure through integration with Azure Active Directory. This article discusses the main features, practices, and benefits of Azure Entra Conditional Access that enable the application of granular security policies based on criteria, including user role, device compliance, location, and risk assessment. It describes Conditional Access as a means to increase regulatory compliance across various industries, including finance, healthcare, and government, to name a few, so that organizations can follow each of these standards, such as GDPR, HIPAA, and PCI-DSS. The article also brings up real-time monitoring, incident response workflows, and AI-based adaptive access policies in securing Enterprise environments. The article illustrates how to ensure operational efficiency by safeguarding resources with Azure Entra through case studies and practical recommendations. With the growing popularity of digital transformation, Azure Entra Conditional Access will be a leading force in securing access to cloud and on-premise resources to ensure that businesses can continue to meet the requirements of modern IT security while reducing risk.

Legacy systems are still critical business operations in many industries – but they are becoming roadblocks to innovation, agility, and scalability. As enterprises increasingly pressure themselves to modernize their aging infrastructures, strategic implementation of a transition from monolithic to microservices is gaining ground. Transforming this type of complex monolith into microservices is not a trivial task. It presents technical and organizational challenges, including bureaucratic service boundaries embedded in legacy codebases that tightly couple the service's functionality. The topic of this article is how artificial intelligence (AI) can help automate the decomposition of monolithic systems into decomposed, scalable microservices. By using machine learning, natural language processing, and clustering algorithms, AI tools can analyze source code, runtime data, and interactions between system components to determine intelligent service boundaries. A detailed methodology for AI-assisted decomposition is presented, along with real-world tools such as IBM Mono2Micro and AWS Microservice Extractor. A practical case study involving a global e-commerce company is included to illustrate applied outcomes. Additionally, the article addresses key challenges such as data inconsistency, domain misalignment, and organizational resistance. How it works outlines best practices to support successful implementation, including incremental migration patterns, domain-driven design, and DevOps integration. The article concludes with strategic recommendations and a forward-looking perspective on how AI will further change the modernization process. When done right, AI improves organizations’ ability to create agile, future-prepared software ecosystems.

As businesses increasingly adopt multi-cloud strategies to improve cost, performance, and availability, managing dispersed infrastructure across many providers becomes a crucial challenge. Infrastructure as Code (IaC) emerges as a key paradigm, allowing for automation, version control, and consistency in infrastructure provisioning and administration. This article provides a complete examination of IaC best practices for multi-cloud settings, focusing on modular architecture, tool standardization, governance, security integration, and automation via CI/CD pipelines. Terraform, AWS CloudFormation, and policy-as-code frameworks like OPA are all appraised for their use in cross-cloud orchestration. The paper uses case studies and practical examples to demonstrate how firms can streamline deployments, decrease operational risk, and assure regulatory compliance in complex enterprise systems. These insights are intended to assist DevOps and cloud engineering teams in creating durable, scalable, and secure multi-cloud infrastructures.

In pharmaceutical manufacturing, compliance with 21 CFR Part 11 is critical for ensuring the integrity of electronic records and signatures within Manufacturing Execution Systems (MES). This paper proposes a comprehensive framework for implementing electronic signatures and data integrity controls in MES, aligning with 21 CFR Parts 11, 210, 211, ICH Q7, and EudraLex Volume 4 Annex 11. The methodology includes system design, user access controls, audit trails, and data lifecycle management, validated through risk-based assessments. Key findings demonstrate that tailored electronic signature configurations (none, single, or double) based on process criticality reduce compliance risks while enhancing operational efficiency. Automated data capture and true-copy transmission further ensure data integrity. Challenges such as manual data entry and generic account usage are addressed through procedural and technical controls. This study underscores the importance of data integrity by design, offering practical guidance for pharmaceutical manufacturers to achieve regulatory compliance and safeguard patient safety.

Securing inter-service communication and data access has become crucial as microservices become the architectural standard in enterprise software development. In dynamic, cloud-native systems, traditional perimeter-based security solutions are no longer adequate. The Zero-Trust Architecture (ZTA) in Java-based microservices is thoroughly examined in this study. We go over the fundamentals of ZTA, look at how it applies to microservices, and offer thorough methods for implementing zero-trust policies with industry-standard frameworks and tools like OAuth 2.0, Istio, and Spring Security. Additionally, a case study showing how ZTA is implemented in a distributed Java microservices application is provided.

In today’s rapidly evolving software development landscape, DevOps has emerged as a vital practice to ensure faster delivery and improved collabora- tion between development and operations teams. Jenkins, a widely adopted open-source automation server, is central to Continuous Integration and Con- tinuous Deployment (CI/CD) pipelines. However, as systems scale, tradi- tional methods of pipeline optimization often fall short in maintaining per- formance and resource efficiency. This paper explores the integration of Ar- tificial Intelligence (AI) to dynamically enhance the performance of Jenkins pipelines in scalable DevOps environments.

We propose an AI-driven framework that leverages machine learning mod- els to analyze historical build data, identify pipeline bottlenecks, predict build failures, and automatically tune performance parameters. Techniques such as anomaly detection, reinforcement learning, and predictive analytics are employed to provide actionable insights and automation in decision-making. The framework monitors pipeline execution metrics—such as queue times, ex- ecutor utilization, and test runtimes—and learns optimal configurations that minimize latency and resource overhead. The AI models continuously adapt to evolving workloads, ensuring that pipelines remain efficient as project de- mands change.

To validate the proposed framework, we conducted experiments using real-world Jenkins job data from enterprise-scale DevOps environments. Re- sults show a 30–45 This study demonstrates that AI can play a transformative role in op- timizing CI/CD workflows by introducing intelligence, adaptability, and re- silience into pipeline management. Our solution provides a generalized ap- proach that can be extended to other orchestration tools and aligns with the broader goals of DevOps automation and intelligent software engineer- ing. Ultimately, the research contributes to the growing field of AIOps by showcasing how AI-enhanced automation in DevOps environments can lead to higher software delivery velocity, improved developer productivity, and re- duced operational costs. Future work includes expanding the framework to support cross-platform integration, real-time observability dashboards, and federated learning for multi-tenant DevOps ecosystems.