Due to the development of advanced identity based attacks and even complex cyber threats, merely possessing defensive cyber security capabilities is not enough today. In this study, we investigate how predictive analytics based machine learning (ML) can be employed for pro-active identity management and threat detection. In this study, the authors assess some models of machine learning – Decision Trees, Random Forests, Support Vector Machines (SVM), and a new hybrid one – to determine which best allows for the detection of (...) both known and unknown threats. The results reveal that in metrics such as accuracy, precision, recall, and F1 score. The hybrid model incorporating both supervised and unsupervised learning approaches scored the highest among other models. As a consequence of its adaptability, the hybrid model is capable of real time dynamic threat detection and anomaly based identity management which makes it an appropriate model for the changing cyber security environment. This study provides the prospects to make proactive cybersecurity more efficient and therefore enhancing the technology for protection systems. (shrink)

Cyberspace relies on information technologies to mediate relations between different people, across different communication networks and is reliant on the supporting technology. These interactions typically occur without physical proximity and those working depending on cybersystems must be able to trust the overall human–technical systems that support cyberspace. As such, detailed discussion of cybersecurity policy would be improved by including trust as a key value to help guide policy discussions. Moreover, effective cybersystems must have resilience designed into them. This paper (...) argues that trustworthy cybersystems are a key element to resilient systems, and thus are core to cybersecurity policy. The paper highlights the importance of trustworthiness for resilient cybersystems. The importance of trustworthiness is shown through a discussion of three events where trustworthiness was the target or casualty of cyberattacks: Stuxnet, hacking of communications and the Edward Snowden revelations. The impact of losing trust is highlighted, to underpin the argument that a resilient cybersystem ought to design in trustworthiness. The paper closes off by presenting a general set of policy implications arising from recognition of the interplay between trust, trustworthiness and resilience for effective cybersecurity. (shrink)

The rapid evolution of cyber threats has made traditional cybersecurity methods increasingly inadequate. Artificial Intelligence (AI) has emerged as a transformative technology in the field of cybersecurity, offering enhanced capabilities for detecting and responding to cyber threats in real time. This paper explores the role of AI in revolutionizing cybersecurity, focusing on its applications in threat detection, anomaly detection, and automated response systems. Through the use of machine learning algorithms, AI can analyze vast amounts of data, identify (...) patterns, and predict potential threats, making it an essential tool in modern cybersecurity strategies. This paper also highlights the challenges and ethical considerations associated with implementing AI in cybersecurity. (shrink)

The ethical issues raised by cybersecurity practices and technologies are of critical importance. However, there is disagreement about what is the best ethical framework for understanding those issues. In this paper we seek to address this shortcoming through the introduction of a principlist ethical framework for cybersecurity that builds on existing work in adjacent fields of applied ethics, bioethics, and AI ethics. By redeploying the AI4People framework, we develop a domain-relevant specification of five ethical principles in cybersecurity: (...) beneficence, non-maleficence, autonomy, justice, and explicability. We then illustrate the advantages of this principlist framework by examining the ethical issues raised by four common cybersecurity contexts: penetration testing, distributed denial of service attacks (DDoS), ransomware, and system administration. These case analyses demonstrate the utility of this principlist framework as a basis for understanding cybersecurity ethics and for cultivating the ethical expertise and ethical sensitivity of cybersecurity professionals and other stakeholders. (shrink)

This article examines the rapidly evolving landscape of cybersecurity, focusing on emerging threats and innovative defense mechanisms. We analyze four key threat vectors: Advanced Persistent Threats (APTs), ransomware, Internet of Things (IoT) vulnerabilities, and social engineering attacks. These threats pose significant risks to organizations, including data breaches, financial losses, and operational disruptions. In response, we explore cutting-edge defense mechanisms such as Artificial Intelligence and Machine Learning for threat detection, Zero Trust Architecture for access control, blockchain for data integrity, behavioral (...) analytics for insider threat detection, and quantum cryptography for enhanced encryption. The article also outlines best practices for cybersecurity, including regular software updates, strong password policies, employee training, and security audits. By synthesizing current research and industry trends, this article provides a comprehensive overview of the cybersecurity landscape, emphasizing the need for continuous adaptation and vigilance in the face of evolving cyber threats. (shrink)

: The rapid proliferation of Internet of Things (IoT) devices in smart cities has introduced numerous benefits, enhancing urban efficiency, sustainability, and automation. However, these interconnected systems also pose significant cybersecurity challenges, including data breaches, unauthorized access, and cyberattacks that can compromise critical infrastructure. This paper explores various cybersecurity strategies tailored for IoT environments in smart cities, focusing on encryption techniques, secure authentication mechanisms, network security protocols, and blockchain-based security models. Additionally, it discusses machine learningbased anomaly detection systems (...) to identify potential cyber threats in real time. By analyzing existing security frameworks and emerging solutions, this study provides a comprehensive approach to safeguarding IoT devices in smart urban ecosystems. The findings emphasize the need for a multi-layered security model and proactive threat mitigation strategies to ensure the resilience and integrity of smart city infrastructures. (shrink)

In 2024, the intersection of artificial intelligence (AI) and cybersecurity presents both unprecedented challenges and significant opportunities. This article explores the evolving landscape of AI-driven cyber threats, the advancements in AI-enabled security measures, and the strategic responses required to navigate these new realities. Leveraging statistics, trends, and expert insights, we delve into how organizations can enhance their cybersecurity posture in the face of sophisticated AI threats.

Due to the development of advanced identity based attacks and even complex cyber threats, merely possessing defensive cyber security capabilities is not enough today. In this study, we investigate how predictive analytics based machine learning (ML) can be employed for pro-active identity management and threat detection. In this study, the authors assess some models of machine learning – Decision Trees, Random Forests, Support Vector Machines (SVM), and a new hybrid one – to determine which best allows for the detection of (...) both known and unknown threats. The results reveal that in metrics such as accuracy, precision, recall, and F1 score. The hybrid model incorporating both supervised and unsupervised learning approaches scored the highest among other models. As a consequence of its adaptability, the hybrid model is capable of real time dynamic threat detection and anomaly based identity management which makes it an appropriate model for the changing cyber security environment. This study provides the prospects to make proactive cybersecurity more efficient and therefore enhancing the technology for protection systems. (shrink)

The Internet of Things (IoT) is revolutionizing industries by connecting everyday objects to the internet, enabling improved functionality and convenience. However, the rapid adoption of IoT devices has raised significant cybersecurity concerns, as the vast number of connected devices presents new vulnerabilities that can be exploited by cybercriminals. These vulnerabilities are often the result of insecure device configurations, weak authentication mechanisms, and inadequate data protection. This paper explores the cybersecurity challenges associated with IoT, such as device security, network (...) security, and data privacy, and discusses the solutions that can help mitigate these risks. The paper further evaluates the role of standardization, encryption techniques, and intrusion detection systems in enhancing the security of IoT ecosystems. Finally, it proposes a framework for IoT security based on best practices and emerging technologies. (shrink)

This article explores the critical intersection of adaptive cybersecurity strategies and cloud-based real-time data analytics platforms, addressing the unique security challenges posed by these dynamic environments. The article presents a comprehensive framework that leverages artificial intelligence, machine learning, and behavioral analytics to create a robust, responsive security infrastructure capable of evolving alongside emerging threats. By integrating zero-trust principles, automated incident response mechanisms, and continuous compliance monitoring, the proposed framework offers a holistic environment. -/- The article examines the multifaceted benefits (...) of cloud computing for real-time analytics, including scalability, cost-efficiency, and global reach, while emphasizing the importance of balancing these advantages with stringent security measures. Through an analysis of current cybersecurity limitations and the potential of adaptive strategies, this article provides valuable insights for organizations seeking to harness the power of cloud-based real-time analytics while maintaining a strong security posture in an increasingly complex threat landscape. The findings underscore the necessity of adopting dynamic, intelligent security frameworks to protect critical data assets and ensure the integrity of the real-time approaches to safeguarding sensitive data and analytics processes in distributed cloud analytics operations in the cloud era. (shrink)

The rapid proliferation of Internet of Things (IoT) devices in smart cities has introduced numerous benefits, enhancing urban efficiency, sustainability, and automation. However, these interconnected systems also pose significant cybersecurity challenges, including data breaches, unauthorized access, and cyberattacks that can compromise critical infrastructure. This paper explores various cybersecurity strategies tailored for IoT environments in smart cities, focusing on encryption techniques, secure authentication mechanisms, network security protocols, and blockchain-based security models. Additionally, it discusses machine learningbased anomaly detection systems to (...) identify potential cyber threats in real time. By analyzing existing security frameworks and emerging solutions, this study provides a comprehensive approach to safeguarding IoT devices in smart urban ecosystems. The findings emphasize the need for a multi-layered security model and proactive threat mitigation strategies to ensure the resilience and integrity of smart city infrastructures. (shrink)

Abstract: As the frequency and sophistication of cyberattacks continue to rise, organizations face increasing challenges in safeguarding their digital infrastructures. Traditional cybersecurity measures often struggle to keep pace with rapidly evolving threats, creating a pressing need for more adaptive and proactive solutions. Artificial Intelligence (AI) has emerged as a transformative force in this domain, offering enhanced capabilities for detecting, analyzing, and preventing cyberattacks in real- time. This paper explores the pivotal role of AI in strengthening cybersecurity defenses by (...) leveraging machine learning algorithms, predictive analytics, and automation to anticipate and mitigate potential threats before they manifest. Furthermore, it examines AI's ability to evolve with emerging attack vectors, providing a dynamic response to an ever-changing threat landscape. The paper also addresses the limitations and ethical considerations surrounding AI-driven cybersecurity, advocating for a balanced approach to its deployment. Through this exploration, the research underscores how AI is redefining the future of cyber defense by shifting the focus from reactive to proactive strategies. (shrink)

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps (...) and shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

This article explores the critical intersection of cloud computing, real-time data analytics, and cybersecurity in the context of digital transformation. It examines the rapid growth of cloud technologies and their impact on data management across various industries. The article highlights the benefits of real-time data analytics in cloud environments, particularly for sectors like finance, healthcare, and e-commerce. However, it also addresses the unique cybersecurity challenges that arise from this integration, including data breaches, unauthorized access, DDoS attacks, and advanced (...) persistent threats. -/- The article proposes a comprehensive, multi-faceted cybersecurity framework to address these challenges, incorporating zero-trust security models, advanced encryption techniques, machine learning for anomaly detection, micro-segmentation, and automated threat intelligence. Through analysis of industry reports, case studies, and statistical data, the article provides insights into the effectiveness of these security measures and their potential to safeguard sensitive information in dynamic cloud environments. (shrink)

In an era of unprecedented digital transformation, safeguarding business operations against cyber threats while adhering to stringent regulatory standards is of paramount importance. Pega, a leader in business process automation and customer engagement solutions, has significantly strengthened its platform to tackle the dual challenges of cybersecurity and regulatory compliance. This article explores Pega’s innovative approach to security and compliance, detailing how the platform has integrated cutting-edge security measures such as real-time threat detection, secure data handling, and compliance controls. These (...) features are designed to ensure robust protection for businesses in an increasingly interconnected and digital environment. By examining the platform’s built-in security infrastructure, compliance monitoring, and risk management capabilities, the paper sheds light on how Pega’s solutions align with evolving regulatory requirements, providing businesses with the tools they need to mitigate security risks, comply with industry standards, and secure sensitive data. Through detailed analysis, we assess the impact of Pega’s security innovations in strengthening the cybersecurity and compliance posture of enterprises and explore future trends that could shape the evolving digital frontier. (shrink)

The advent of quantum computing represents a revolutionary leap in computing power, offering solutions to problems previously thought intractable. However, it also introduces new challenges and risks, particularly in the realm of cybersecurity. Quantum computers, with their ability to solve certain computational problems exponentially faster than classical computers, pose a significant threat to current cryptographic systems, which are foundational to modern cybersecurity. Algorithms that currently secure everything from banking transactions to personal communications may be rendered obsolete by quantum (...) computing's ability to break their encryption in a fraction of the time. This paper explores the potential risks that quantum computing presents to cybersecurity and examines emerging mitigation strategies. Specifically, it focuses on the vulnerabilities of public-key cryptography systems such as RSA, ECC (Elliptic Curve Cryptography), and AES (Advanced Encryption Standard), all of which rely on the computational difficulty of certain mathematical problems, such as factoring large numbers or solving discrete logarithms—tasks that quantum computers can perform efficiently through Shor’s algorithm. Additionally, it discusses potential post-quantum cryptography solutions, which involve the development of cryptographic algorithms that are resistant to quantum attacks. Furthermore, the paper evaluates the timeline for quantum computing advancements, the readiness of quantum-safe algorithms, and the actions organizations and governments must take to prepare for this new era. The work emphasizes the need for early adoption of quantum-resistant encryption methods and cross-disciplinary collaboration to develop secure frameworks capable of safeguarding sensitive data in a quantum future. (shrink)

The rapid transition to remote learning during the early 2020 pandemic has catalyzed unprecedented changes in higher education cybersecurity infrastructure. This white paper presents a comprehensive framework addressing the multifaceted challenges of securing academic environments during this transformation. Our research examines not only traditional security concerns but also emerging challenges in student privacy, mental health considerations, geographical access disparities, and cross-institutional collaboration. Through empirical analysis of implementation across multiple institutions, we demonstrate how universities can adapt their security architectures to (...) support widespread remote access while maintaining data protection, academic integrity, and student wellbeing. Our findings show significant improvements across key metrics, including an 85% reduction in unauthorized access attempts, 99.9% availability of critical academic services, and a 40% reduction in student-reported technical anxiety during the transition period. This paper provides technical specifications, implementation guidelines, and empirical validation of our approach, offering a blueprint for institutions facing similar challenges in the rapidly evolving landscape of higher education. (shrink)

The human factor in information systems is a large vulnerability when implementing cybersecurity, and many approaches, including technical and policy driven solutions, seek to mitigate this vulnerability. Decisions to apply technical or policy solutions must consider how an individual’s values and moral stance influence their responses to these implementations. Our research aims to evaluate how individuals prioritise different ethical principles when making cybersecurity sensitive decisions and how much perceived choice they have when doing so. Further, we sought to (...) use participants’ responses to cybersecurity scenarios to create profiles that describe their values and individual factors including personality. Participants (n = 193) in our study responded to five different ethically sensitive cybersecurity scenarios in random order, selecting their action in that scenario and rating and ranking of the ethical principles (i.e., Beneficence, Non-Maleficence, Justice, Autonomy, Explicability) behind that action. Using participants’ demographics, personality, values, and cyber hygiene practices, we created profiles using machine learning to predict participants’ choices and the principle of most importance to them across scenarios. Further, we found that, generalising, for our participants Autonomy was the most important ethical principle in our scenarios, followed by Justice. Our study also suggests that participants felt they had some agency in their decision making and they were able to weigh up different ethical principles. (shrink)

This book aims to provide a comprehensive analysis of Advanced Persistent Threats (APTs), including their characteristics, origins, methods, consequences, and defense strategies, with a focus on detecting these threats. It explores the concept of advanced persistent threats in the context of cyber security and cyber warfare. APTs represent one of the most insidious and challenging forms of cyber threats, characterized by their sophistication, persistence, and targeted nature. The paper examines the origins, characteristics and methods used by APT actors. It also (...) explores the complexities associated with APT detection, analyzing the evolving tactics used by threat actors and the corresponding advances in detection methodologies. It highlights the importance of a multi-faceted approach that integrates technological innovations with proactive defense strategies to effectively identify and mitigate APT. (shrink)

Where organizations used to rely on employees tenured with their company, the Great Resignation has presented new problems to organizational structure and fortification. Such a process usually leads to disrupted employee productivity and most importantly, increased vulnerability to cyber threats. Loyal workers, intentionally or unintentionally disloyal workers, and employees who leave the organization can compromise organizational confidential information, such as innovation, customer data, and other data that the organization considers to be highly valuable. Research has also revealed that workforce transition (...) time is also the highest-risk activity period for insiders, wherein activities like the unauthorized download of data or accidental data leaks when offboarding an employee are likely to occur. Moreover, there are difficulties, particularly for organizations that cannot apply adequate access controls and monitoring during the notice periods, making them much more sensitive to data leaks. Further, this paper examines these paramount cybersecurity threats and offers an organized framework for their mitigation. Explores how the risks can be reduced under this through policies like strong access controls, opaque data monitoring systems, and comprehensive offboarding. To get practical recommendations for managing the problem with data sharing, the example of using machine learning to realize the mechanisms for identifying anomalies and graph-theory-based mathematical models is given. Thus, this research provides a comprehensive set of procedures to address the consequences of the Great Resignation for organizations and safeguard their assets during worker turnovers. (shrink)

The increasing integration of digital technologies into every aspect of modern life has led to a growing concern over cybersecurity, particularly in the context of privacy. As organizations collect vast amounts of personal and sensitive data, the tension between securing this information and preserving individual privacy has become a critical ethical dilemma. This paper explores the ethical challenges faced by cybersecurity professionals, policymakers, and organizations when balancing the need for protection against the potential risks to personal privacy. It (...) discusses the implications of surveillance, data collection, and the ethical boundaries of cybersecurity practices. Additionally, it evaluates existing frameworks, legal regulations, and technological solutions that attempt to balance these concerns. The paper also proposes a set of ethical guidelines for cybersecurity practices, focusing on how organizations can protect data while respecting privacy rights. (shrink)

The increasing sophistication of cyber threats necessitates innovative and proactive cybersecurity measures. This paper explores the latest trends in cybersecurity, focusing on the role of Artificial Intelligence (AI), Zero Trust security, and blockchain technology. A review of the literature highlights significant advancements and persistent challenges, including the security of Internet of Things (IoT) ecosystems and human-centric vulnerabilities. Experiments were conducted to evaluate the efficacy of machine learning-based intrusion detection systems and Zero Trust implementation in a simulated environment. Results (...) demonstrate improved detection rates and reduced insider threat risks. This paper concludes by emphasizing future directions, such as quantum-resistant cryptography and collaborative cybersecurity frameworks, to secure the digital future. (shrink)

Abstract—The purpose of this study is to highlight and prove the positive impact in which blockchain could have on today’s IoT environment in terms of providing Cybersecurity for not just organizations, but other individuals who share data via the internet. The current IoT environs operates on a centralized cloud based server, meanwhile block chain operates on a decentralized server. The differentiation between the both plays a major role in the level of security they both provide; whereby, decentralized systems are (...) less vulnerable to cyber-attacks and centralized ones are not. In this report, real life illustrations are used to justify the knowledge that the block chain security could serve as a saving grace for internet users. Also, this form of security seems to be the only way to eradicate all forms of insecurity in supply chain and IoT breaches for businesses. An in depth understanding on how the block chain system operates would show why it’s held with such high hopes and how it can be beneficial for a wide range of industries. However, this research also highlights some difficulties that might arise in implementing the block chain system. It also gives ways in which the system can be gradually implemented. Firstly, the government needs to make it mandatory for businesses that deal with national based confidential data to implement block chain in their supply chain system to boost security. Secondly, top company officials like C.E. O’s need to take genuine interest in the block chain technology by investing in its development and making it a part of employee training, which in the long run would benefit the economy and business in return. Thirdly, a merge of more public and private businesses would help push the use of block chain further. Lastly, the government should make the block chain technology easily accessible, making the official permission to implement -easy and cost effective. (shrink)

Organizations are now facing more complex cyber threats, prompting them to implement forward-thinking security testing methods to protect their digital systems. The Red Team and Blue Team simulations offer a practical method for evaluating an organization's ability to withstand cyber threats by mimicking actual attacks and the corresponding defensive measures taken in response. These exercises aid in uncovering system weaknesses, boosting threat recognition, and refining incident response capacities. Integrating artificial intelligence-driven threat intelligence, automated security checks, and Zero Trust frameworks enhances (...) overall cybersecurity protection measures. Continuous cyber training exercises enable organizations to stay prepared for evolving threats despite facing obstacles such as resource constraints and adapting attacker strategies, incorporating drills on a regular basis. Simultaneous cooperation between teams employing offensive (Red Team) and defensive (Blue Team) tactics, facilitated by Purple Teaming, strengthens adaptive security systems. Consistent evaluations and security instruction enable companies to remain in front of cyber threats. Structured simulations can be used by businesses to improve and fine-tune their security policies and emergency response procedures. As cyber threats continue to evolve, organizations must give priority to ongoing enhancement and real-time security surveillance. A well-organized cybersecurity strategy guarantees lasting stability in an ever more digital environment. (shrink)

COVID-19 is the new standard for forced changes in the workforce, forcing the adoption of remote and hybrid work environments globally. It was in the period of this rapid adoption of distributed work environments that new opportunities for flexibility and productivity were opened up, but fundamental weaknesses of conventional notions of cybersecurity were unveiled. As employees connect to company networks from a plethora of different places and different types of devices, situations were complex at the corporate level to ensure (...) that security measures remained adequately stringent. New threats came to the fore, such as endpoint devices that have added more risks since they were unsecured home networks and the use of personal devices (BYOD). All these changes raised the concern of a new security model for the enterprise whereby new, fresh methods of dealing with the issue emphasize strategies which accommodate the change, and we affirm the need to put in place security architecture that will help protect the valuable enterprise assets. This paper looks at the new challenges of defending the widely dispersed employee base and offers an integrated solution for protecting the modern enterprise. To address risks inherent in the current working environments, the framework seeks to address concerns of identity-centric security and zero-trust and apply emerging technologies in threat intelligence. Using knowledge acquired by current cyber assaults and actual programs, the research provides numerous substantial approaches to countering current and potential cyber threats. The research also identifies the main recommendations for enterprises operating in this environment, including the need for active management of risks and constant adaptation to threats. (shrink)

Through a combination of Guidewire platforms, cloud computing and cybersecurity frameworks, the insurance industry in miniature is being transformed into a digitally transformed, ever resilient ecosystem. This ecosystem enables insurers to modernize core operations of policy management, claims processing and billing while continuing to provide secure, scalable and efficient service delivery. Insurers using cloud infrastructure have elastic resources capable of scaling to meet dynamic workloads and can provide high availability and fast disaster recovery. They have liberated data analytics to (...) run on the cloud with advanced capabilities to churn out actionable insights to optimize operations and create personalized offerings for their customers. Sensitive customer data and important systems are securely protected from cyber security threats by a robust cybersecurity layer, which includes data encryption, controls access, and detects threats proactively. Integration and cloud adoption challenges like costly implementation, cybersecurity risk and legacy system migration will be handled by deft and effective strategies such as phased adoption, workforce upskilling and adoption of a zero-trust security model. Within operational efficiency, stronger resilience and innovative business models enabled, i.e. behavior-based insurance pricing through IoT and real time analytics, are the opportunities. However, moisture and regulatory complexity can be barriers to change, and investments in modern technology, workforce development, and partnerships with insurtech and cybersecurity experts can enable insurers to survive and thrive in a more competitive, digital-first landscape. This convergence engenders customer trust, unlocks its operational agility and ensures compliance with changing requirements, thereby enabling insurers to sustain their growth, build resilience and rival digital transformation. (shrink)

This comprehensive article examines the transformative impact of cloud infrastructure and real-time data analytics on the travel industry, with a particular focus on advancing sustainability and enhancing cybersecurity. The article explores how these technologies are revolutionizing travel operations, from personalized customer experiences to optimized resource allocation and reduced environmental footprints. We delve into the implementation of cloud-based solutions that enable real-time processing of vast data streams, facilitating dynamic adjustments to travel plans and fostering more efficient, eco-friendly practices. The article (...) also addresses the critical role of advanced cybersecurity frameworks in protecting sensitive traveler data within this increasingly digitized landscape, discussing the adoption of Zero Trust models, AI-driven threat detection, and end-to-end encryption. Furthermore, we investigate emerging trends in travel analytics, evolving sustainability standards, and anticipated developments in travel cybersecurity, providing insights into the future trajectory of the industry. By synthesizing current practices with future projections, this article offers a roadmap for travel companies seeking to innovate responsibly, balancing technological advancement with environmental stewardship and data protection. Ultimately, this article underscores the pivotal role of cloud infrastructure and real-time analytics in shaping a more sustainable, secure, and efficient travel ecosystem for the digital age. (shrink)

By integration with cloud computing, cybersecurity and generative AI, the insurance industry is being transformed from high efficiency, low cost, and better customer service. However, these advanced technologies can also be used by insurers to automate and streamline processes like claims handling, underwriting, and policy generation, which are majorly time consuming and error prone. In predictive analytics, fraud detection, and personalized customer experience, generative AI makes it possible for insurers to mitigate risks and, at the same time, provide more (...) personalized services to customers. With cloud platforms, data management is scalable, flexible and secure, offering real time access to data that drives value by accelerating sound decisions in the insurance value chain. Sensitive data is protected against cybersecurity threats, staying compliant with regulations and marginally reducing risk. Guidewire’s unified technology stack allows insurers to leverage the integrated nature of these technologies through end-to-end integration to improve operational resilience and customer satisfaction. It is these synergies that are the future of insurance which is why transparency, ethical practices in AI as well as proactive cybersecurity are vital. It will become an arms race for insurers to keep pace with these changes and move away from being rigid and slow to adapt; they will need to stay agile and quick, especially with the landscape changing so rapidly. (shrink)

Behavioral biometrics is an emerging field in cybersecurity that leverages unique patterns of human behavior to continuously authenticate individuals in real-time. Unlike traditional biometrics, such as fingerprints or facial recognition, which are typically used at the point of access, behavioral biometrics continuously monitor and analyze how users interact with devices, systems, and applications. This dynamic approach offers enhanced security by detecting anomalous behaviors that may indicate unauthorized access or suspicious activity, without disrupting the user experience. This paper explores the (...) role of behavioral biometrics in continuous authentication systems, discussing various modalities such as keystroke dynamics, mouse movement, touch gestures, and gait analysis. It examines the advantages and limitations of these methods, with a focus on their effectiveness in preventing identity theft, session hijacking, and insider threats. The paper also delves into the integration of machine learning algorithms in behavioral biometrics, which enable systems to adapt to evolving user behaviors and enhance the accuracy of detection. We also address the challenges associated with implementing continuous authentication systems, including privacy concerns, data security, and the computational overhead of processing behavioral data in real-time. Furthermore, we explore future research directions, including multi-modal authentication, cross-platform integration, and the potential impact of artificial intelligence in refining behavioral biometric models. In conclusion, behavioral biometrics holds great promise in enhancing cybersecurity by providing a seamless, non-intrusive layer of continuous authentication. The integration of behavioral patterns into cybersecurity systems could significantly improve defense mechanisms against unauthorized access and improve user experience by reducing reliance on traditional authentication methods. (shrink)

The contribution of this paper is a comprehensive cybersecurity framework to secure cloud hosted Guidewire implementations by addressing critical security challenges such as threat detection, incident response, compliance, and system performance. Based on advanced technologies like machine learning, behavioral analytics and auto patching, the framework detects and mitigates known and unknown threats, incidentally zero-day exploit. The system does this through micro segmenting, behavioral anomaly detection, and automated patch orchestration in a way that does not render the system unperforming. Key (...) performance metrics of threat detection time (less than 3 seconds), incident response (patching within 5 minutes) and system availability (99.95% uptime) are all tested as the framework outperforms in all of these. Additionally, the framework ascertains industry standards such as GDPR and HIPAA via automated audit trails and ongoing monitoring. Compared to the currently deployed cloud security, the proposed approach, with a special focus on more secure the Guidewire application deployed in the cloud, offers a multi layered security approach to the modules specific to the Guidewire. Though powerful, the framework also suffers from complexity in its deployment, overhead in terms of performance and a reliance on cloud provider-specific features. The scalability will need to improve, and the product will need to support multiple clouds and integrate advanced technology such as quantum computing and AI-powered threat intelligence. This work sets a solid basis for the protection of cloud-based Guidewire systems against future cyber threats while maintaining operational continuity and system compliance. (shrink)

As digital transformation accelerates, organizations are increasingly reliant on cloud-based services for flexibility, scalability, and cost-efficiency. However, the proliferation of cloud technologies has introduced new, sophisticated cybersecurity challenges. The traditional perimeter-based security model is no longer sufficient in safeguarding critical data and applications. This paper explores the evolving landscape of cybersecurity in the context of cloud computing and presents strategies for mastering security in the digital age. We discuss the importance of securing cloud-native applications, the role of advanced (...) technologies such as artificial intelligence (AI), machine learning (ML), blockchain, and quantum computing in securing cloud environments, and how organizations can implement a proactive security posture. By focusing on a multi-layered security framework, this paper provides insights into securing data, ensuring privacy, managing identity and access, and maintaining compliance in increasingly complex and dynamic cloud environments. (shrink)

The increasingly prominent role of digital technologies during the coronavirus pandemic has been accompanied by concerning trends in privacy and digital ethics. But more robust protection of our rights in the digital realm is possible in the future. -/- After surveying some of the challenges we face, I argue for the importance of diplomacy. Democratic countries must try to come together and reach agreements on minimum standards and rules regarding cybersecurity, privacy and the governance of AI.

The 2024 CrowdStrike Cybersecurity incident resulted in a worldwide IT disruption impacting millions of Microsoft Windows systems. In July 2024, a faulty update of CrowdStrike’s Falcon Endpoint Detection and Response (EDR) software caused widespread system crashes known as the “Blue Screen of Death” (BSOD). The event caused severe disruptions to major industries such as aviation, financial services, healthcare and emergency response systems resulting in operational shutdown, financial setbacks and global safety concerns. This study presents a detailed examination of the (...) CrowdStrike incident, focusing on the technical issues, global effects and legal consequences. The research delves into the weakness of centralized cybersecurity systems, emphasizing the dangers of putting too much trust in single-point security services. The research highlights the significance of strict testing protocols, comprehensive cybersecurity frameworks, and risk assessment strategies enhancing future cybersecurity preparedness. The CrowdStrike incident presents a compelling case study in global cybersecurity risk management, prompting organizations to reassess redundancy measures, failover mechanisms, and more decentralized security architectures in order to protect critical IT systems. (shrink)

In light of Taiwan's extensive exposure to the Chinese Communist Party's "cognitive domain infiltration warfare," this paper proposes new response mechanisms and strategies for cybersecurity and national defense. The focus is primarily on assessing the CCP's cognitive infiltration tactics to develop policy recommendations in cybersecurity linguistics. These recommendations are intended to serve as a reference for future national defense and information security policies. Within the constraints of limited resources, this study attempts to provide an integrated analysis method combining (...) qualitative and quantitative tools. This method involves text mining in cybersecurity linguistics, focusing on "textual content" and incorporating discourse deconstruction. The aim is to enhance the success rate in identifying and defining contentious information in cognitive warfare, thereby offering policy suggestions. -/- Keywords: Cognitive Infiltration, Big Data Corpus, Cybersecurity Linguistics, Text Mining Analysis, Narrative Language Deconstruction. (shrink)

The use of the internet in the current government environment continues to grow. The increasingly complex use of the internet can cause vulnerabilities to cyber attacks in information security, which include aspects of confidentiality, integrity, and service availability, so that it can disrupt the performance of public service delivery. The systematic literature review research method was carried out because of the large amount of information and data regarding cyber security strategies. This can be traced through various information in books, scientific (...) journals, newspapers, magazines, as well as sources of information from web pages/websites via the internet. This method is important in analyzing the concept of cyber security strategy in Indonesia. The obstacles that the government has are expired devices that are still used on local government networks, which may not have the latest updates, security devices such as antiviruses have expired, human resources have not fully supported this cybersecurity. Indonesia needs policies that regulate all elements related to cyber security. This infrastructure standard must comply with international standards for dealing with cyber warfare. (shrink)

A contemporary cybersecurity strategy known as the Zero Trust Security Model aims to eradicate implicit trust and ensure uninterrupted authentication for every user, device, and application. Zero Trust security models diverge from conventional perimeter-based security by anticipating threats within and beyond the network, necessitating rigorous authentication and limitations on user access privileges. Components like Multi-Factor Authentication (MFA), Zero Trust Network Access (ZTNA), micro-segmentation, and AI-driven threat detection strengthen security by reducing vulnerabilities and stopping unauthorized access. The integration of Artificial (...) Intelligence and Machine Learning enhances Zero Trust by facilitating real-time risk assessment and automated reaction to security incidents. Implementation of Zero Trust security comes with its own set of difficulties, such as the intricacies of deployment, problems with integration, and worries about the user experience. As cyber threats become increasingly complex, organizations must place a high priority on implementing Zero Trust to guarantee robust security in cloud-based and hybrid work environments. This study examines the core concepts of Zero Trust, its fundamental technologies, deployment methods, and practical examples to demonstrate its efficacy in safeguarding digital resources. By implementing a Zero Trust model, businesses can substantially lower their cybersecurity vulnerabilities and enhance adherence to regulatory requirements. The research indicates that Zero Trust is a fundamental security strategy for contemporary businesses, providing sustained defense against progressively sophisticated threats. (shrink)

The heightened digitization of the healthcare industry has led to an exponential increase in sensitive patient data, which requires robust security models to prevent breaches, unauthorized access, and cyber attacks. Traditional security protocols are inadequate, and this has made it imperative to explore Artificial Intelligence (AI) and Blockchain as novel solutions. AI enhances healthcare cybersecurity by facilitating real-time anomaly detection, predictive analysis, and automated threat response, while blockchain offers decentralization, immutability, and secure data sharing. However, blockchain technology faces major (...) challenges for scalability and performance, as represented by lengthy transaction processing durations and high storage demands, elements that could deter its widespread adoption across the healthcare industry. To help counter these challenges, researchers are exploring Layer 2 scaling solutions, hybrid blockchain architectures, and off-chain storage strategies. In addition, cleanroom technology provides a controlled and secure environment for handling sensitive healthcare data, protecting privacy while also supporting AI-driven analytics and research collaboration. This article critically examines the intersection of blockchain and AI for healthcare security, in terms of challenges, use cases, and direction. Leveraging these technologies, health organizations can set up future-proof models of security that address data integrity, regulatory requirements, and resistance to future threats more effectively. (shrink)

In an era of increasing cyber threats, ethical hacking has emerged as a pivotal practice in strengthening network security. Ethical hacking, also known as penetration testing, involves authorized attempts to breach a network or system to uncover vulnerabilities before malicious actors can exploit them. This research paper delves into the role of ethical hacking in assessing and mitigating network vulnerabilities to fortify defenses against cyberattacks. It emphasizes the strategic importance of ethical hacking in the context of evolving cybersecurity challenges (...) and explores its practical application in organizational security frameworks. The study begins by defining ethical hacking and its ethical and legal boundaries, distinguishing it from malicious hacking activities. It examines the methodologies and tools used by ethical hackers to identify vulnerabilities, simulate attacks, and provide actionable recommendations for remediation. By mimicking the tactics of cybercriminals, ethical hackers play a critical role in uncovering weaknesses in network configurations, software applications, and hardware systems. A core focus of the research is on the importance of integrating ethical hacking into a proactive cybersecurity strategy. The paper highlights how ethical hacking helps organizations stay ahead of potential threats by identifying vulnerabilities in real time and enabling swift mitigation. Case studies from various industries, such as healthcare, finance, and technology, are analyzed to demonstrate the tangible benefits of ethical hacking in preventing data breaches, minimizing downtime, and safeguarding sensitive information. The paper also explores the challenges associated with ethical hacking, including the need for skilled professionals, the potential for system disruptions during testing, and the importance of maintaining strict compliance with legal and regulatory standards. It discusses the ethical implications of penetration testing, particularly in balancing the need for security with respect for privacy and data protection. Advancements in tools and techniques used by ethical hackers are another significant aspect of this research. The paper investigates the use of artificial intelligence (AI) and machine learning (ML) to enhance vulnerability detection and streamline testing processes. It also addresses the rise of automated penetration testing tools and their role in increasing the efficiency and accuracy of ethical hacking efforts. The findings of this research underscore the necessity of ethical hacking as an essential component of modern cybersecurity frameworks. Organizations that adopt ethical hacking as part of their security strategy gain valuable insights into their vulnerabilities and can build resilient defenses against an everevolving threat landscape. By fostering a culture of continuous testing and improvement, ethical hacking contributes to the creation of a robust and secure network environment. This paper aims to serve as a comprehensive resource for cybersecurity professionals, IT managers, and policymakers, offering insights into the best practices, tools, and techniques of ethical hacking. By leveraging the principles of ethical hacking, organizations can effectively assess vulnerabilities, enhance their security posture, and protect critical assets in the face of mounting cyber threats. (shrink)

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus (...) on developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (shrink)

Due to the development of advanced identity based attacks and even complex cyber threats, merely possessing defensive cyber security capabilities is not enough today. In this study, we investigate how predictive analytics based machine learning (ML) can be employed for pro-active identity management and threat detection. In this study, the authors assess some models of machine learning – Decision Trees, Random Forests, Support Vector Machines (SVM), and a new hybrid one – to determine which best allows for the detection of (...) both known and unknown threats. The results reveal that in metrics such as accuracy, precision, recall, and F1 score. The hybrid model incorporating both supervised and unsupervised learning approaches scored the highest among other models. As a consequence of its adaptability, the hybrid model is capable of real time dynamic threat detection and anomaly based identity management which makes it an appropriate model for the changing cyber security environment. This study provides the prospects to make proactive cybersecurity more efficient and therefore enhancing the technology for protection systems. (shrink)

The time window between the disclosure of a new cyber vulnerability and its use by cybercriminals has been getting smaller and smaller over time. Recent episodes, such as the Log4j vulnerability, exemplify this well. Within hours after the exploit being released, attackers started scanning the internet looking for vulnerable hosts to deploy threats like crypto currency miners and ransom ware on vulnerable systems. Thus, it becomes imperative for the cybersecurity defense strategy to detect threats and their capabilities as early (...) as possible to maximize the success of prevention actions. Although crucial, discovering new threats is a challenging activity for security analysts due to the immense volume of data and information sources to be analyzed for signs that a threat is emerging. In this sense, we present a framework for automatic identification and profiling of emerging threats using Twitter messages as a source of events and MITRE ATT&CK as a source of knowledge for threat characterization. The framework comprises three main parts: identification of cyber threats and their names; profiling the identified threat in terms of its intentions or goals by employing two machine learning layers to filter and classify tweets; and alarm generation based on the threat’s risk . The main contribution of our work is the approach to characterize or profile the identified threats in terms of their intentions or goals, providing additional context on the threat and avenues for mitigation. In our experiments, the profiling stage reached an F1 score of 77% in correctly profiling discovered threats. (shrink)

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data (...) set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used. (shrink)

In the evolving landscape of data security, traditional access control mechanisms often fall short in addressing dynamic and context-specific requirements. As data breaches become more sophisticated, organizations require more adaptive and intelligent access control strategies. This paper explores the integration of Artificial Intelligence (AI) into Just-in-Time (JIT) access control models to enhance database security. By leveraging AI, we aim to create adaptive, context-aware permission systems that grant access precisely when needed, reducing the attack surface and mitigating unauthorized access risks. We (...) begin by analyzing traditional access control methods such as Role-Based Access Control (RBAC), Mandatory Access Control (MAC), and Discretionary Access Control (DAC), highlighting their limitations in handling real-time access control scenarios. The paper then explores the benefits of JIT access control, emphasizing how it minimizes over-privileged accounts and reduces security vulnerabilities. Furthermore, we investigate the role of AI in cybersecurity, particularly in real-time monitoring, anomaly detection, and decision-making processes for access control. To achieve a more intelligent access control framework, we propose an AI-driven JIT access model that incorporates machine learning algorithms, user behavior analytics, and contextual evaluation to determine access permissions dynamically. The proposed model is evaluated through simulations and case studies to measure its effectiveness in preventing unauthorized access, reducing the attack surface, and enhancing overall database security. The results demonstrate that our AI-driven approach significantly improves access accuracy, minimizes false positives and negatives, and optimizes response times compared to traditional methods. Through comprehensive analysis, this research provides a roadmap for organizations looking to implement AIenhanced JIT access control mechanisms. By dynamically granting access based on real-time behavioral and contextual assessments, organizations can significantly improve database security, reduce administrative overhead, and mitigate insider threats. Future research directions include further refinement of AI models, integration with multi-factor authentication mechanisms, and testing across diverse real-world scenarios to enhance security effectiveness. (shrink)

Older adults’ growing use of the internet and related technologies, further accelerated by the COVID-19 pandemic, has prompted not only a critical examination of their behaviors and attitudes about online threats but also a greater understanding of the roles of specific characteristics within this population group. Based on survey data and using descriptive and inferential statistics, this empirical study delves into this matter. The behaviors and attitudes of a group of older adults aged 60 years and older (n=275) regarding different (...) dimensions of online safety and cybersecurity are investigated. The results show that older adults report a discernible degree of concern about the security of their personal information. Despite the varied precautions taken, most of them do not know where to report online threats. What is more, regarding key demographics, the study found some significant differences in terms of gender and age group, but not disability status. This implies that older adults do not seem to constitute a homogeneous group when it comes to attitudes and behaviors regarding safety and security online. The study concludes that support systems should include older adults in the development of protective measures and acknowledge their diversity. The implications of the results are discussed and some directions for future research are proposed. (shrink)

The increasing complexity and volume of cyber threats necessitate a more advanced and proactive approach to cybersecurity. Traditional Security Operations Centers (SOCs) rely on rule-based systems and manual analysis, which are often insufficient to counter evolving attack vectors. Artificial Intelligence (AI), particularly Machine Learning (ML) and Blockchain technology, has emerged as a game-changer in enhancing SOC operations, improving threat detection, response, and mitigation capabilities. Machine Learning algorithms can analyze vast amounts of security data in real time, identifying anomalies and (...) predicting potential threats with higher accuracy than conventional methods. ML-driven threat intelligence enables SOCs to detect zero-day attacks, automate incident response, and reduce false positives, thereby increasing operational efficiency. Additionally, AI-powered automation allows security analysts to focus on strategic threat mitigation rather than being overwhelmed by repetitive tasks. Blockchain technology further strengthens cybersecurity by providing an immutable, decentralized, and transparent framework for securing digital transactions and communications. Its application in SOC operations includes secure identity management, tamper-proof logging of security events, and decentralized threat intelligence sharing. By integrating blockchain with AI, SOCs can ensure data integrity, enhance collaboration across organizations, and reduce insider threats. This paper explores the synergy between AI, ML, and Blockchain in modern SOC environments. It discusses their combined potential to transform cybersecurity operations by enabling intelligent automation, realtime threat analysis, and secure data management. Furthermore, it highlights challenges such as computational overhead, integration complexity, and privacy concerns that must be addressed to maximize the benefits of these technologies. (shrink)

The internet has made the world more linked than ever before. While taking advantage of this online transition, cybercriminals target flaws in online systems, networks, and infrastructure. Businesses, government organizations, people, and communities all across the world, particularly in African countries, are all severely impacted on an economic and social level. Many African countries focused more on developing secure electricity and internet networks; yet, cybersecurity usually receives less attention than it should. One of Africa's major issues is the lack (...) of adequate digital security infrastructure, which has harmed businesses, governmental institutions, and individual communities more than it has helped. The majority of African countries operate without cybersecurity measures in place to combat cyberattacks. Only a few examples of today's cyber risks include digital extortion, business email intrusion, data breaches, online fraud, ransomware, and phishing, and new types of cybercrime are always developing. Due to the advent of new technology, cybercriminals have become more organized and quicker in their attacks and alliance creation. To maintain a secure digital environment for all internet users, this study focused on the challenges, solutions, and need for African countries to improve their online safety by tackling cybercrime, online fraud, and cybersecurity concerns. The objective of this study is to offer practical and long-term answers to the problems posed by cybercrime, with a continuing emphasis on enhancing online safety. It will assess the effectiveness of cutting-edge cybersecurity measures, legislative frameworks, and cross-border cooperative efforts, as well as potential areas for improvement. Additionally, the study will examine cutting-edge methods like blockchain technology, machine learning, and other cutting-edge methods that could improve our using digital defences to stop cybercrime. (shrink)

With the rise of remote work and cloud-first strategies in 2020, the traditional perimeter-based security model became increasingly obsolete. Zero Trust Architecture (ZTA), which emphasizes “never trust, always verify,” emerged as a strategic framework to mitigate evolving cybersecurity threats, especially those exacerbated by the COVID-19 pandemic. Despite its theoretical robustness, implementing Zero Trust in enterprise environments revealed several technical and organizational roadblocks, including identity-centric access control, network segmentation, legacy system compatibility, and the scalability of real-time monitoring. This paper evaluates (...) the underlying principles of Zero Trust from both cybersecurity and organizational perspectives and identifies implementation challenges through a mixed-methods approach combining quantitative threat metrics and qualitative enterprise case studies. By synthesizing insights from cybersecurity engineering, organizational behavior, and risk management, this research proposes a phased framework to streamline Zero Trust adoption in complex network environments. Findings show that tailored ZTA strategies significantly reduced insider threat surfaces and lateral movement during the rapid digital transformations of 2020, but required extensive investment in identity governance and cross-functional coordination. (shrink)

The rapid adoption of cloud computing by businesses has led to substantial cybersecurity concerns, necessitating comprehensive security systems to safeguard cloud infrastructure. Cloud environments' multi-tenant nature renders systems vulnerable to threats including unauthorized access, data theft, and distributed denial-of-service (DDoS) attacks. Protecting data confidentiality, privacy, and regulatory adherence requires implementing a combination of encryption, authentication protocols, and intrusion detection measures. These risks have been addressed through the use of sophisticated methods, such as dynamic remote data auditing, decentralized access control, (...) and privacy-preserving query execution. Economic denial-of-sustainability (EDoS) attacks present a significant challenge that necessitates the implementation of effective mitigation strategies to prevent resource depletion. A comprehensive security strategy combines Zero Trust Architecture (ZTA), blockchain-based security frameworks, and AI-powered threat identification to improve cloud security for businesses. Enterprise operations must be safeguarded by implementing secure service delivery models that include strict access control, authentication, and encryption, such as IaaS, PaaS, and SaaS. Advances in trusted computing frameworks and cryptography enhance cloud security by rigorously enforcing data protection policies. This paper provides a comprehensive review of cybersecurity threats, assesses current methods of mitigation, and examines the latest technologies, providing expert guidance on optimal procedures and future research avenues for strengthening enterprise cloud security. (shrink)

In today’s digital landscape, organizations leveraging Pega Business Process Management (BPM) face significant security challenges as they strive to protect sensitive business processes from evolving cyber threats. This article explores best practices for implementing robust security measures within Pega BPM environments, focusing on secure configuration settings, identity and access management (IAM), encryption standards, and real-time monitoring. By integrating Pega BPM with broader cybersecurity frameworks, organizations can proactively detect and mitigate vulnerabilities, ensuring the integrity and confidentiality of critical business operations. (...) This research highlights the importance of a holistic approach to security, combining technical measures with organizational policies to create a resilient BPM environment. Through a detailed examination of methodologies, implementation strategies, and performance evaluations, this article provides actionable insights for organizations aiming to strengthen their security posture. The findings underscore the necessity of continuous monitoring, adaptive security configurations, and alignment with industry standards to address emerging threats effectively. This study contributes to the field by identifying research gaps and offering practical recommendations for securing Pega BPM systems in an increasingly complex threat landscape. (shrink)

The Home Depot data breach, which occurred in 2014, marked a significant turning point in the cybersecurity landscape, drawing attention to the increasing prevalence of ransomware and cyberattacks targeting large-scale retail organizations. This breach exposed the personal and financial information of over 56 million customers and highlighted the vulnerabilities in the systems used by major corporations. The attack was attributed to a sophisticated ransomware operation that gained access through a third-party vendor’s compromised credentials, ultimately enabling cybercriminals to infiltrate Home (...) Depot’s network. This paper explores the Home Depot breach, focusing on its relation to the rise of ransomware as a common cyber threat. It delves into the tactics, techniques, and procedures (TTPs) employed by the attackers, the vulnerabilities exploited, and the broader implications for the retail industry and cybersecurity at large. Additionally, the study examines how ransomware has evolved from a relatively niche threat to a significant weapon in the cybercriminal arsenal, affecting organizations across all sectors. By analyzing the breach and its aftermath, this paper offers insights into best practices for cybersecurity resilience and incident response strategies that can help mitigate future threats of similar magnitude. (shrink)