Adaptive Security Model for Cloud Platforms Based on Information Security and Cryptographic Protocols

The integration of cloud computing into healthcare systems has revolutionized data management by enabling seamless data sharing and enhanced accessibility, thereby improving patient outcomes and operational efficiency. However, the sensitive nature of healthcare data necessitates robust privacy-preserving mechanisms to protect patient confidentiality. This paper explores state-of-the-art techniques such as cryptography, data anonymization, and secure access control, which are tailored to address privacy challenges in healthcare environments. It also critically evaluates existing frameworks, identifying limitations, and proposes a scalable architecture designed to ensure secure and efficient data sharing. Healthcare systems increasingly rely on cloud environments for storing and sharing sensitive patient data. However, these systems face significant challenges, including the risk of data breaches, unauthorized access, and the need for strict compliance with regulatory frameworks such as HIPAA and GDPR. To address these challenges, this paper presents a privacy-preserving framework that leverages advanced cryptographic methods, blockchain for secure transaction logging, and machine learning-based anomaly detection to safeguard patient data from collection to sharing. Experimental results demonstrate that the proposed system outperforms traditional methods in terms of privacy, efficiency, and scalability, showcasing its potential for real-world healthcare applications. The integration of cloud computing into healthcare systems has significantly transformed how patient data is managed and shared across multiple stakeholders, including healthcare providers, insurers, and researchers. This digital transformation has enabled real-time access to medical records, improving clinical decision-making and patient outcomes. However, the sensitive nature of healthcare data demands the adoption of advanced privacy-preserving techniques to mitigate risks such as unauthorized access, data breaches, and regulatory non-compliance. In this paper, we explore state-of-the-art technologies like cryptographic techniques (homomorphic encryption, differential privacy), data anonymization (k-anonymity, l-diversity), and access control mechanisms (RBAC, ABAC) that have been proposed to enhance data privacy and security. Additionally, we delve into the use of blockchain technology for secure, transparent transaction logging and machine learning-based anomaly detection systems to safeguard against unauthorized access. Through experimental analysis and performance testing, we demonstrate that the proposed privacy-preserving framework outperforms conventional methods in terms of scalability, efficiency, and data protection, thereby offering a promising solution for real-world healthcare applications.

The Government and industry are currently using information and communication technology a lot. The Government and companies in Indonesia have regulations on operational standards and management procedures for information and communication systems security at Government regarding implementing information system security. Information security is essential because there are many threats to information security. Information security in this era of Information and Communication Technology (ICT) is fundamental. Information Exchange Environment (IEE) vulnerabilities have increased as threats become more widespread and complex; therefore, information security has become a fundamental problem for businesses, organizations, and governments. So, in this paper, we will explain the review of the impact of Information Security For Government and companies in terms of threats and types of information security. Some security system methods are analyzed, among others—application Security, cloud security, Cryptography, security infrastructure, Incident response, and vulnerability management. The results of this review analysis, it known threat has several solutions, each depending on the type of threat for both the Government and the Company. Furthermore, Information Security is considered very important, especially for data stored in Government and companies.

Abstract: The accelerating advancement of quantum computing poses an imminent threat to classical cryptographic schemes, rendering traditional cloud security frameworks increasingly vulnerable. To address this challenge, this study proposes a post-quantum secure cloud architecture that integrates number-theoretic cryptography, artificial intelligence, and blockchain protocols within a scalable deployment model. The framework is built upon lattice-, hash-, and code-based cryptographic primitives whose security is grounded in number-theoretic hardness assumptions, while AI-driven reinforcement learning and deep learning mechanisms provide adaptive resilience through anomaly detection and optimized key management. Blockchain is employed to establish decentralized trust and immutability, supported by post-quantum signature schemes and quantum-safe ledgers, thereby ensuring verifiability across distributed environments. The methodology combines theoretical modeling, simulation environments, and multi-cloud validation to evaluate security, performance, and scalability. Anticipated results include measurable improvements in quantum resistance, reduced latency through AI-optimized cryptographic parameters, and sustained blockchain throughput under post-quantum constraints. Comparative analysis against classical and quantum-ready baselines highlights the superiority of the proposed system in maintaining privacy, efficiency, and interoperability. The findings suggest significant implications for both academia and industry by providing a unified framework capable of safeguarding critical infrastructures in finance, healthcare, and governance. This research contributes to the broader discourse on quantum-safe cybersecurity by demonstrating how interdisciplinary integration—spanning number theory, artificial intelligence, blockchain, and cloud computing—can establish resilient, future-proof digital ecosystems. Keywords Post-quantum cryptography, cloud security, AI-driven cryptography, blockchain protocols, number theory, quantum computing, scalable privacy, decentralized trust, adaptive security, multi-cloud architecture

The growing share of information technologies in the daily lives of citizens of the Republic of Kazakhstan during the pandemic leads to the fact that various institutions use an increasingly wide range of information elements and mechanisms. The expectations of society are not only to improve the functioning of electronic administration, but also to ensure that all stored data is properly protected from unauthorized access, so that ensuring the security of information processing becomes one of the most important tasks of the State and the public. The purpose of the study is to consider aspects of the functioning of information security in the Republic of Kazakhstan during the pandemic and to identify the factors of reliable information security by state policy in order to distinguish hoaxes from real malicious actions. The methodological approach of the research is institutional, structural–functional, and systemic. Improper management of information security can lead to leakage, loss, or falsification of stored data, paralyzing completely relevant activities. Kazakhstan has made notable advancements in establishing a comprehensive legal framework for cybersecurity, positioning itself ahead of certain Central Asian neighbors. Government agencies develop, install, implement, operate, monitor, and analyze an information security management system, ensuring confidentiality, accessibility, and integrity of information. Information security requires the establishment of comprehensive procedures for all ongoing processes, taking into account the use of personal data. Information is an integral part of society, acting as a strategic resource for creating national security. Kazakhstan has been particularly active in forging collaborations and alliances to bolster their cybersecurity postures. The practical significance lies in the improvement of state measures aimed at protecting information, as well as preventing material, physical, moral, or other damage to the state and society as a result of information activities.

Cloud storage is a popular and convenient way to store and access data, but it also poses security risks, as the data is stored remotely and accessible over the internet.Cryptographic protocols can be used to secure cloud storage and protect the confidentiality, integrity, and availability of the data.To address these security risks, cryptographic protocols can be used to secure cloud storage.These protocols employ various cryptographic techniques such as encryption, digital signatures, and access control to protect the data from unauthorized access and ensure its confidentiality, integrity, and availability.By utilizing cryptographic protocols, users can have greater confidence in the security of their cloud-stored data, and organizations can meet compliance requirements for data protection.This paper is a valuable resource for individuals and organizations who are looking for ways to secure their cloud-stored data.By providing a comprehensive overview of cryptographic protocols for securing cloud storage, the paper offers insights into various techniques and best practices that can be employed to enhance the security of cloud storage.By providing a comprehensive overview of cryptographic protocols for securing cloud storage, the paper offers insights into various techniques and best practices that can be employed to enhance the security of cloud storage.The paper analyzes the strengths and weaknesses of different cryptographic protocols and provides recommendations on how to select the appropriate protocol for a specific use case.

: Data processing, management, and storage have all been changed by the internet of things, which offers companies scalable and reasonably priced solutions. There is serious cybersecurity concerns connected to using cloud services, such as service outages, unauthorized access, and data breaches. Investigating these security threats and developing defenses to enhance cloud security are the goals of this study. This study reviews current security mechanisms including encryption, authentication, and access control and highlights critical vulnerabilities in cloud systems through methodical literature analysis and risk assessment. To assist enterprises in evaluating and prioritizing cloud security concerns, the study suggests a risk assessment methodology. To strengthen cloud computing's overall security posture, best practices and security solutions are also advised. The results of this study will help create cloud infrastructures that are more secure, guaranteeing data availability, confidentiality, and integrity in the rapidly changing digital environment.

Cybersecurity in SAP systems technical support is a fundamental concern for enterprises due to the increasing complexity of cyber threats and the crucial role SAP applications play in business operations. Workstations utilized in technical support represent significant attack vectors, particularly during installation, updates, and maintenance procedures. These vulnerabilities, if not properly managed, can lead to unauthorized access, data breaches, and operational disruptions. Ensuring robust security in SAP technical support environments requires a proactive approach that integrates access control, patch management, endpoint protection, and data security measures. This study explores best cybersecurity practices for SAP system technical support, focusing on mitigating workstation vulnerabilities. Key recommendations include implementing multi-factor authentication (MFA) and role-based access control (RBAC) to restrict unauthorized access. The study highlights the necessity of automated patch management systems to reduce exposure to exploit-based attacks and the importance of endpoint protection strategies such as next-generation antivirus solutions and endpoint detection and response (EDR) tools. Furthermore, encryption techniques and data loss prevention (DLP) technologies are identified as crucial measures for safeguarding sensitive business data. A review of recent literature supports these findings, demonstrating how organizations adopting AI-driven security monitoring and zero-trust architectures achieve enhanced cybersecurity resilience. Studies emphasize that traditional security solutions are inadequate in addressing modern cyber threats, advocating for the integration of behavioral-based threat detection and continuous security assessments. In conclusion, enterprises must adopt a multi-layered security approach, combining advanced technical solutions with continuous employee training and proactive threat monitoring. By fostering a security-first culture and refining security frameworks in response to emerging threats, organizations can effectively protect their SAP environments from cyber risks and maintain operational integrity.

The broadcast encryption with dealership (BED) scheme allows a dealer, instead of a broadcaster, to manage a recipient. Unlike prior broadcast encryption schemes, BED reduces the burden placed on the broadcaster to manage recipient, which makes it suitable for a broadcasting service targeting a large number of recipients. Subscribing and unsubscribing from the broadcast service occur frequently at the request of the user, however, early versions of BED schemes do not support recipient revocation. In this paper, we propose a recipient revocable broadcast encryption with dealership and show that it is secure in the adaptive security model without random oracles.

Cloud security in India's retail sector has become a significant concern as the industry adopts cloud-based solutions for data management and customer engagement. This paper examines how deep learning techniques such as Convolutional Neural Networks (CNN) and Autoencoders can be applied to detect anomalies and mitigate cyber threats in cloud-based retail environments. The study focuses on identifying and addressing security vulnerabilities such as data breaches, insider threats, and account takeovers. By presenting case studies from leading Indian retail companies, the paper highlights the potential of deep learning to enhance security and operational efficiency in cloud infrastructures.

Cloud computing has become an essential driver of digital transformation, enabling businesses to op- timize IT infrastructure, e-commerce operations, artificial intelligence (AI) applications, and Internet of Things (IoT) networks [1, 2]. Service models such as Infrastructure-as-a-Service (IaaS), Platform- as-a-Service (PaaS), and Software-as-a-Service (SaaS) offer scalability, cost efficiency, and operational flexibility [3, 4]. However, the transition from traditional data centers to cloud environments has introduced security vulnerabilities that conventional perimeter-based defenses fail to address [5, 6]. The increasing sophistication of cyber threats, including data breaches, unauthorized access, and ran- somware attacks, necessitates the adoption of advanced security strategies [7, 8]. This paper explores AI-driven security automation, blockchain-based identity management, and zero-trust security archi- tectures as key solutions for mitigating modern cloud security risks [9, 10]. Additionally, this study identifies existing research gaps, such as real-time adaptability, interoperability challenges, scalability concerns, and quantum computing risks [11, 12]. Future research directions in- clude quantum-resistant encryption, AI-enhanced security automation, and AI-blockchain convergence for self-adaptive cloud security solutions [13, 14]. By addressing these areas, this research aims to contribute to the development of next-generation cloud security frameworks that enhance cyber threat mitigation, regulatory compliance, and secure cloud integration [15, 16].

Cloud computing is a framework for delivering services through a shared network. It should follow certain procedures or mechanisms to protect and make the cloud data more reliable. In secure cloud computing, the implementation and service delivery model are dealt with. Cloud security could be viewed as a provider and customer responsibility depending on their design and level. There are cases where multiple cloud users can use the same information to create service abnormalities, which is called ‘multitenancy.' In a multi-tenant model, the resources are isolated between different users and it is done by segmentation to achieve the goal of data security between many users. In the user access area, user access to common resources is also limited. The software is aware of the actions of the client and acts accordingly if unauthorized access is detected. The authors propose a clear idea of the data storage security of cloud information through honeypot in cloud monitoring applications and various cloud security scheme key models used and also identifies the audit time and invalid response time.

With the rapid development of digital technologies, responsible AI has become a critical focus for ensuring ethical and socially conscious advancements in business and operations management. The integration of responsible AI practices in business groups’ digital transformations is essential to mitigate potential risks and maximize the positive impact on operational efficiency, supply chain performance, and subsidiary performance. This study aims to examine the consequences and mechanisms through which responsible group digitalization influences business group’s operation management, as manifested in subsidiary performance within the context of the digital economy. Analyzing data from 202 affiliated subsidiaries, we examine the role of HRM collaboration and technological turbulence in facilitating group digitalization. This study enriches the operations management literature and expands the application of ethical and responsible AI practices in digitalization by investigating the relationship between business group digitalization and business operations. Furthermore, this study provides practical implications pertaining to how ethical and responsible practices can guide group digital transformations, business operations and enhance the performance of subsidiaries.

The emergence of video streams as a primary medium for communication and the demand for high-quality video sharing over the internet have given rise to several security and privacy issues, such as unauthorized access and data breaches. To address these limitations, various Selective Video Encryption (SVE) schemes have been proposed, which encrypt specific portions of a video while leaving others unencrypted. The SVE approach balances security and usability, granting unauthorized users access to certain parts while encrypting sensitive content. However, existing SVE schemes adopt an all-or-nothing coarse-grain encryption approach, where a user with a decryption key can access all the contents of a given video stream. This paper proposes and designs a fine-grained access control-based selective video encryption scheme, ABSVE, and a use-case protocol called Blind Brother. Our scheme encrypts different identified Regions of Interest (ROI) with a unique symmetric key and applies a Ciphertext Policy Attribute Based Encryption (CP-ABE) scheme to tie these keys to specific access policies. This method provides multiple access levels for a single encrypted video stream. Crucially, we provide a formal syntax and security definitions for ABSVE, allowing for rigorous security analysis of this and similar schemes - which is absent in prior works. Finally, we provide an implementation and evaluation of our protocol in the Kvazaar HEVC encoder. Overall, our constructions enhance security and privacy while allowing controlled access to video content and achieve comparable efficiency to compression without encryption.

The increasing migration of enterprise operations to Amazon Web Services (AWS) has amplified the need for robust, scalable, and intelligent cybersecurity solutions. Security Information and Event Management (SIEM) systems have become vital for detecting threats, correlating logs, and maintaining compliance in cloud environments. This systematic review explores the integration of SIEM tools within AWS-based infrastructures, focusing on their effectiveness in threat detection and log correlation. It examines leading SIEM solutions such as Splunk, IBM QRadar, Sumo Logic, and AWS-native services like Amazon GuardDuty, AWS CloudTrail, and AWS Security Hub. Emphasis is placed on key integration approaches, including API-based ingestion, agentless data capture, and real-time event streaming through AWS services like Kinesis and S3. The review critically analyzes studies published between 2018 and 2023, highlighting trends in the automation of log management, enrichment of security alerts through machine learning, and orchestration via Security Orchestration, Automation, and Response (SOAR) platforms. Challenges such as data normalization, scalability limitations, cross-service visibility, and compliance adherence in multi-account AWS architectures are discussed. The findings indicate that SIEM integration enhances threat detection efficiency by enabling proactive anomaly detection, facilitating rapid incident response, and improving forensic investigation capabilities. However, the review identifies gaps, particularly in cost optimization, handling high-velocity log streams, and adapting traditional SIEM models to dynamic, serverless AWS architectures. Best practices for successful SIEM deployment include leveraging AWS-native integrations, prioritizing event prioritization algorithms, applying continuous tuning, and aligning with security frameworks like NIST and CIS AWS Foundations Benchmark. Future research directions propose the development of AI-driven adaptive SIEM systems tailored for cloud-native environments, advanced correlation engines for serverless and containerized workloads, and strategies to optimize licensing and resource utilization. This systematic review provides cybersecurity practitioners, cloud architects, and researchers with a comprehensive understanding of SIEM integration complexities and evolving practices in AWS infrastructures, ultimately contributing to improved cloud security postures and operational resilience in the face of sophisticated cyber threats.

Information security risks are a major threat to South Africa's bid to build a broad-based information society. The integration of information security in e-government is no longer an option, but an imperative given the resulting “information overload” and the need to filter “good” from “bad” information. Unless South Africa integrates information security in its e-government development policy and practices, the acclaimed benefits of e-government will not be realized. The moral hazard problems arising from bad information behavior such as human manipulation, withholding information, unauthorized access, and violation of individual privacy and confidentiality heightens the need to combat info-security risks and vulnerabilities. South Africa's readiness to deal with the information security risks has come under scrutiny. The information security infrastructure in South Africa is also not clearly understood. This chapter examines South Africa's information security landscape and describes how institutional and agency coordination could help improve information security in e-government.