Enhancing Enterprise Security with Zero Trust Architecture
- URL: http://arxiv.org/abs/2410.18291v1
- Date: Wed, 23 Oct 2024 21:53:16 GMT
- Title: Enhancing Enterprise Security with Zero Trust Architecture
- Authors: Mahmud Hasan,
- Abstract summary: Zero Trust Architecture (ZTA) represents a transformative approach to modern cybersecurity.
ZTA shifts the security paradigm by assuming that no user, device, or system can be trusted by default.
This paper explores the key components of ZTA, such as identity and access management (IAM), micro-segmentation, continuous monitoring, and behavioral analytics.
- Score: 0.0
- License:
- Abstract: Zero Trust Architecture (ZTA) represents a transformative approach to modern cybersecurity, directly addressing the shortcomings of traditional perimeter-based security models. With the rise of cloud computing, remote work, and increasingly sophisticated cyber threats, perimeter defenses have proven ineffective at mitigating risks, particularly those involving insider threats and lateral movement within networks. ZTA shifts the security paradigm by assuming that no user, device, or system can be trusted by default, requiring continuous verification and the enforcement of least privilege access for all entities. This paper explores the key components of ZTA, such as identity and access management (IAM), micro-segmentation, continuous monitoring, and behavioral analytics, and evaluates their effectiveness in reducing vulnerabilities across diverse sectors, including finance, healthcare, and technology. Through case studies and industry reports, the advantages of ZTA in mitigating insider threats and minimizing attack surfaces are discussed. Additionally, the paper addresses the challenges faced during ZTA implementation, such as scalability, integration complexity, and costs, while providing best practices for overcoming these obstacles. Lastly, future research directions focusing on emerging technologies like AI, machine learning, blockchain, and their integration into ZTA are examined to enhance its capabilities further.
Related papers
- Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security [1.2369895513397127]
Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated.
To efficiently secure IoT devices, real-time detection of intrusion systems is critical.
This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security.
arXiv Detail & Related papers (2024-10-01T19:24:34Z) - Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
generative AI, particularly large language models (LLMs), become increasingly integrated into production applications.
New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems.
Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks.
This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
arXiv Detail & Related papers (2024-09-23T10:18:10Z) - Counter Denial of Service for Next-Generation Networks within the Artificial Intelligence and Post-Quantum Era [2.156208381257605]
DoS attacks are becoming increasingly sophisticated and easily executable.
State-of-the-art systematization efforts have limitations such as isolated DoS countermeasures.
The emergence of quantum computers is a game changer for DoS from attack and defense perspectives.
arXiv Detail & Related papers (2024-08-08T18:47:31Z) - Cross-Modality Safety Alignment [73.8765529028288]
We introduce a novel safety alignment challenge called Safe Inputs but Unsafe Output (SIUO) to evaluate cross-modality safety alignment.
To empirically investigate this problem, we developed the SIUO, a cross-modality benchmark encompassing 9 critical safety domains, such as self-harm, illegal activities, and privacy violations.
Our findings reveal substantial safety vulnerabilities in both closed- and open-source LVLMs, underscoring the inadequacy of current models to reliably interpret and respond to complex, real-world scenarios.
arXiv Detail & Related papers (2024-06-21T16:14:15Z) - Security in IS and social engineering -- an overview and state of the art [0.6345523830122166]
The digitization of all processes and the opening to IoT devices has fostered the emergence of a new formof crime, i.e. cybercrime.
The maliciousness of such attacks lies in the fact that they turn users into facilitators of cyber-attacks, to the point of being perceived as the weak link'' of cybersecurity.
Knowing how to anticipate, identifying weak signals and outliers, detect early and react quickly to computer crime are therefore priority issues requiring a prevention and cooperation approach.
arXiv Detail & Related papers (2024-06-17T13:25:27Z) - Generative AI in Cybersecurity [0.0]
Generative Artificial Intelligence (GAI) has been pivotal in reshaping the field of data analysis, pattern recognition, and decision-making processes.
As GAI rapidly progresses, it outstrips the current pace of cybersecurity protocols and regulatory frameworks.
The study highlights the critical need for organizations to proactively identify and develop more complex defensive strategies to counter the sophisticated employment of GAI in malware creation.
arXiv Detail & Related papers (2024-05-02T19:03:11Z) - Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content.
In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks.
We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
arXiv Detail & Related papers (2024-02-21T06:22:41Z) - A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure.
This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus.
We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
arXiv Detail & Related papers (2024-01-19T14:52:04Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - Zero Trust: Applications, Challenges, and Opportunities [0.0]
This survey comprehensively explores the theoretical foundations, practical implementations, applications, challenges, and future trends of Zero Trust.
We highlight the relevance of Zero Trust in securing cloud environments, facilitating remote work, and protecting the Internet of Things (IoT) ecosystem.
Integrating Zero Trust with emerging technologies like AI and machine learning augments its efficacy, promising a dynamic and responsive security landscape.
arXiv Detail & Related papers (2023-09-07T09:23:13Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.