Vol. 5 No. 01 (2025)

Network security would be incomplete without firewalls that control traffic flow through rule-based policies. The manual way to configure and manage firewall rules, however, is prone to various pitfalls; rules tend to become overly complex, human error occurs, and cyber threats continue to evolve. This work investigates the reinforcement learning (RL) - driven method for firewall policy generation, utilizing RL as an automated means for policy generation to increase adaptability and reduce administrative overhead. The proposed system utilizes RL agents that learn an optimal policy from real-time network traffic and dynamically update firewall rules to maximize security while minimizing false positives and latency. The key contributions of this work include a novel system architecture that integrates reinforcement learning (RL) with existing firewall frameworks, as well as methodologies for data collection, feature engineering, and reward function design. Additionally, the system is evaluated using simulated network environments and benchmark datasets. It is demonstrated that the RL-based system achieves better accuracy in threat detection compared to traditional static or heuristic approaches, as well as improved policy effectiveness and network performance. Computational cost, explainability, exploration risks, and model generalization are discussed, and future research directions in transfer learning, multi-agent coordination, and integration with broader security frameworks are addressed. This work moves the field closer to realizing real-time, intelligent, and adaptive firewalls that can handle today's cybersecurity challenges. It motivates further exploration of more secure, interpretable, and production-ready RL-driven security solutions.

Researchers track the creation of feedback-based report optimization systems that emerge during the development period for workplace investment platforms. Users' changing preferences now require profiled encounters with rich data to force investment endpoints to generate dynamic custom insights from old static reports. The user needs to activate an immediate report reorganization process through feedback-assisted systems that combine behavioral analytics and telemetry statistical data. The main feedback entry point serves as the system base, while report distribution automation results from persona-based data processing structures. The study proves that combining direct and indirect feedback approaches through thumbs-up/down and content interaction patterns and session telemetry amounts to enhanced user intention understanding. System-generated report appearance optimization solutions accompany content scheduling methods that improve user satisfaction by processing collected data. This article uses practical engineering team scalability-expression examples together with case studies to display customization approaches. The optimization process requires cooperative work between members from both data science and product development alongside engineering teams to trace business objectives with performance objectives. The solution updates static platforms into user-adjustable learning systems that react to user activities. Real-time reporting on workplace investment platforms provides better decision support through the feedback-driven optimization model because it includes customizable elements. The new benefits provided by the result will continuously improve for all users. User engagement metrics, together with job retention and operational effects, become measurable using responsive design methods that help various user groups.

This paper focuses on the cold start latency problem in serverless Java applications, seen mainly when deploying with Google Cloud Run and Spring Boot. Auto-scaling, reduced infrastructure use, and cost savings are why serverless computing is becoming more popular. Still, Java applications generally face delays when starting up, referred to as cold starts, because the JVM must boot up, and Spring Boot comprises complex settings. Because of this latency, users' experience can become very poor, especially when using real-time APIs, chatbots, and e-commerce systems. The study explains the processes behind cold starts, their impact, and several ways to address them. Cloud Run's usage of containers and Spring Boot being a good choice for creating microservices is covered, as well as their difficulties in initializing quickly. Optimizations include adjusting container images, native compilation using GraalVM, setting "minimum instances" and concurrency in Cloud Run, using lazy initialization in Spring Boot, and routinely pinging during warm-up. These approaches have been observed to improve latency from several seconds to almost nothing whenever they are used. This research uses case analysis, benchmarking, and performance monitoring to examine the effectiveness of mitigation strategies. The paper concludes that although Java encounters some cold start difficulties, recent advancements in tools such as GraalVM, virtual machine design, and better cloud support are promising. If Java is configured correctly and sufficient time is spent planning, it can be an excellent fit for serverless environments on Google Cloud Run.

With the proliferation of digital financial services and digital transactional documents, data volumes are vastly increasing, including invoices, receipts, bank statements, and balance sheets. The document has garnered massive interest and a keen interest in handling Information extraction from these documents. For such documents, manual data extraction is time-consuming and prone to human error as the documents come in many formats. This paper covers techniques, tools, and technology in the case of extracting tables from financial and transactional documents, specifically in the case of vertical tables and in the presence of mixed-type data representations. Table extraction means extracting tabular data from a readable image schema document and transforming it into a structured format (CSV / JSON). The paper discusses other extraction methods, such as rule-based extraction, optical character recognition (OCR), and machine learning models. The book also covers some use cases from industry banking, e-commerce, or accounting, amongst other industries. The paper then discusses ethical and legal implications such as GDPR, HIPAA, compliance with data privacy laws, and how it should be transparent and fair for AI systems. Last but not least, the future trends of table extraction, including integration of generative AI and large language models (LLMs) and robotic process automation (RPA), as well as real-time data extraction, are discussed. This paper presents the growing demand for advanced extraction technologies to increase financial document processing accuracy, efficiency, and scalability.

Identity and Access Management (IAM) is essential for cybersecurity architecture because of the increasing complexity of the digital enterprise. The research investigates how Artificial Intelligence (AI) transforms Identity and Access Management (IAM) by establishing context-aware systems that function adaptively through automated identity governance capabilities. Concepts from traditional IAM infrastructure face challenges when implementing dynamic access models because they base their function on manual processes and static policies in their design. Machine learning combined with behavioral analytics and orchestration capabilities installed across the entire IAM lifecycle by AI can solve these issues, from authentication procedures to authorization functions and continuing through entitlement governance until policy execution. AI integration establishes continuous authentication with behavioral biometrics and conducts real-time anomaly detection through unsupervised learning models to enable proactive threat mitigation through risk-adaptive access controls. Through AI, the discovery and automation of access rights become possible because systems use actual user activities and organizational settings to refine and certify proper access definitions. The automation systems help organizations comply with GDPR and HIPAA by delivering immediate policy changes while providing auditable access decision logs. The research document evaluates how AI contributes to creating IAM infrastructure that can adapt because it uses predictive load-balancing techniques, self-healing orchestration mechanisms, and autonomous incident response capabilities. The document shows how IAM unites with Security Operations Centers (SOCs) by correlating identity signals with wider security monitoring data to enhance security detection visibility and coordinated response. This report reveals through technical precision and industry examples that AI-driven IAM functions as a security defense system and a business-enabling power for operational speed, compliance adherence, and digital safety in organizational networks. The research highlights AI's critical position in creating security for contemporary identity perimeters.

Artificial Intelligence provides exact forecast models that adapt to changes in the business environment to benefit sales and revenue operations. The current business setting demands sophisticated predictive methods that exceed traditional ones based on human interpretation and historical data processing. AI forecasting models featuring machine learning technologies, predictive analytics, and automation yield improved sales and revenue operations by offering precise forecasts, flexible systems, and real-time tracking capabilities. Companies achieve time-sensitive decisions through these models by evaluating various information sources that combine structured and unstructured elements, such as market signals and customer data, with sales data statistics. CRM platform-linking AI systems can view complete customer data to create accurate sales pipeline understanding, thus leading to improved forecasting results. A partnership between AI systems and GTM functions with DevOps enables businesses to distribute resources while effectively offering enhanced partner empowerment. Business operations enhanced by AI generate improved sales forecasting capabilities, allowing continuous educational systems to monitor market shifts and organizational requirement adjustments. AI forecasting models generate multiple advantages, although data quality issues prevent them from effectively operating and obtaining stakeholder agreement when integrating data sources. To maximize the exploitation of AI forecasting methods, companies must develop advanced data management systems, implement AI tools, and deliver employee training to reach the best potential outcomes. AI will drive organizational sales and revenue operations into the future to improve operational productivity and strategic decision-making abilities alongside revenue expansion.

The contact the dealer faces has been transformed digitally in the automotive industry. Traditional communication methods, such as manual processes, phone calls, and emails, are becoming less and less satisfactory in meeting today’s demands of speed and accuracy for their customers. Artificial intelligence (AI) and cloud-based technologies enforce real-time digital communication solutions, which are helping to bridge the communication gap between automotive manufacturers and the customers / deal room. These advancements enable real-time information such as inventory, pricing, promotions, operations, and smoother customer experience. With the help of AI-driven tools like chatbots and virtual assistants, customers are replied to instantly, engaged, and personalized the way they want it.

The cloud platforms help the automotive value chain work seamlessly together without missing the need to share data on product launches, recalls, and regulatory changes between dealers and manufacturers. These combined technologies of AI and cloud solutions permit agents to supply customized and reactive services and optimize the internal system of dealerships. Autonomous vehicles complemented by the Internet of Things (IoT) will change how dealers communicate with customers by allowing engaging with them proactively based on real-time vehicle data. Through the convergence of these technologies, they see preparing the scene for a new era of automotive communication based on the drivers of efficiency, personalization, and customer satisfaction. In the age of digital shift, dealers also had to change, and maintaining data security, privacy, and regulatory compliance will also be crucial for them.

The widespread adoption of Internet of Things (IoT) devices across homes and enterprises has introduced significant security risks, especially when unauthorized or compromised devices gain access to sensitive networks. This paper proposes a machine learning-based framework to detect unauthorized IoT devices in real-time using features extracted from TCP/IP traffic. We utilize a Random Forest classifier trained on labeled network traffic from authorized devices. The proposed approach detects device types not on a pre-established whitelist, achieving an average of 96% accuracy in identifying unauthorized devices based on a 20-session window classification. The framework generalizes across different vendors, supports real-time alerting, and is resilient against adversarial attacks.