Defense in Depth Strategies for Zero Trust Security Models
DOI:
https://doi.org/10.36676/jrps.v15.i3.1497Keywords:
Zero Trust Security, Defense in Depth, micro-segmentation, continuous authentication, layered securityAbstract
The groundbreaking Zero Trust Security Model challenges perimeter-based protections in cybersecurity. As cyber threats become more sophisticated, corporations are embracing the Zero Trust philosophy of "never trust, always verify." Whether from within or outside the network, this paradigm imposes rigorous access rules and continual authentication. Zero Trust is a strong security foundation, yet it has drawbacks. The Zero Trust paradigm is enhanced by Defense in Depth, which layers several security methods to safeguard assets. This article examines how the Zero Trust Security Model might include Defense in Depth methods for a complete, robust, and adaptable security architecture.
Zero Trust requires all users and devices to be verified, approved, and continually vetted before accessing resources, eliminating implicit trust. A typical method employed by attackers after breaching the perimeter is lateral movement inside the network, which this approach mitigates well. However, Defense in Depth—deploying numerous, redundant security measures throughout the IT environment—is a proven method. Defence in Depth and Zero Trust may be combined to strengthen access restrictions, detection, response, and recovery.
References
Bertino, E., & Sandhu, R. (2022). Advances in Zero Trust Security. Springer.
Bhattacharyya, A., & Nair, S. (2021). Combining Zero Trust and Defense in Depth. IEEE Security & Privacy, 19(5), 46-55.
Bertino, E., & Sandhu, R. (2022). Advances in Zero Trust Security. Springer.
Bhattacharyya, A., & Nair, S. (2021). Combining Zero Trust and Defense in Depth. IEEE Security & Privacy.
Fitzgerald, J., & Morris, T. (2023). Evaluating Security Models in Practice. Wiley.
Fowler, J., & Parsons, J. (2021). Implementing Zero Trust Security. O'Reilly Media.
Kindervag, J. (2010). No More Chewy Centers: Introducing Zero Trust. Forrester Research.
NIST. (2022). Guide to Protecting Information Technology Systems. National Institute of Standards and Technology.
Rose, S. (2020). Zero Trust Architecture. NIST Special Publication 800-207.
Shinder, D. (2019). The Security Imperative: Defense in Depth. Syngress.
Bertino, E., & Sandhu, R. (2022). Advances in Zero Trust Security. Springer.
Bhattacharyya, A., & Nair, S. (2021). Combining Zero Trust and Defense in Depth. IEEE Security & Privacy.
Fitzgerald, J., & Morris, T. (2023). Evaluating Security Models in Practice. Wiley.
Fowler, J., & Parsons, J. (2021). Implementing Zero Trust Security. O'Reilly Media.
Kindervag, J. (2010). No More Chewy Centers: Introducing Zero Trust. Forrester Research.
NIST. (2022). Guide to Protecting Information Technology Systems. National Institute of Standards and Technology.
Rose, S. (2020). Zero Trust Architecture. NIST Special Publication 800-207. DOI: https://doi.org/10.6028/NIST.SP.800-207-draft2
Shinder, D. (2019). The Security Imperative: Defense in Depth. Syngress.
Bertino, E., & Sandhu, R. (2022). Advances in Zero Trust Security. Springer.
Bhattacharyya, A., & Nair, S. (2021). Combining Zero Trust and Defense in Depth. IEEE Security & Privacy.
Key Technologies and Methods for Building Scalable Data Lakes", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 7, page no.1-21, July-2022, Available : http://www.ijnrd.org/papers/IJNRD2207179.pdf
"Exploring and Ensuring Data Quality in Consumer Electronics with Big Data Techniques"", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 8, page no.22-37, August-2022, Available : http://www.ijnrd.org/papers/IJNRD2208186.pdf
Jain, A., Singh, J., Kumar, S., Florin-Emilian, Ț., Traian Candin, M., & Chithaluru, P. (2022). Improved recurrent neural network schema for validating digital signatures in VANET. Mathematics, 10(20), 3895. DOI: https://doi.org/10.3390/math10203895
Kumar, S., Shailu, A., Jain, A., & Moparthi, N. R. (2022). Enhanced method of object tracing using extended Kalman filter via binary search algorithm. Journal of Information Technology Management, 14(Special Issue: Security and Resource Management challenges for Internet of Things), 180-199.
Kanchi, P., Jain, S., & Tyagi, P. (2022). Integration of SAP PS with Finance and Controlling Modules: Challenges and Solutions. Journal of Next-Generation Research in Information and Data, 2(2). https://tijer.org/jnrid/papers/JNRID2402001.pdf
Rao, P. R., Goel, P., & Jain, A. (2022). Data management in the cloud: An in-depth look at Azure Cosmos DB. International Journal of Research and Analytical Reviews, 9(2), 656-671. http://www.ijrar.org/viewfull.php?&p_id=IJRAR22B3931
"Continuous Integration and Deployment: Utilizing Azure DevOps for Enhanced Efficiency". (2022). International Journal of Emerging Technologies and Innovative Research (www.jetir.org), 9(4), i497-i517. http://www.jetir.org/papers/JETIR2204862.pdf
• Shreyas Mahimkar, Dr. Priya Pandey, Om Goel, "Utilizing Machine Learning for Predictive Modelling of TV Viewership Trends", International Journal of Creative Research Thoughts (IJCRT), Vol.10, Issue 7, pp.f407-f420, July 2022. Available: http://www.ijcrt.org/papers/IJCRT2207721.pdf
"Exploring and Ensuring Data Quality in Consumer Electronics with Big Data Techniques", International Journal of Novel Research and Development (www.ijnrd.org), Vol.7, Issue 8, pp.22-37, August 2022. Available: http://www.ijnrd.org/papers/IJNRD2208186.pdf
Sumit Shekhar, Prof. (Dr.) Punit Goel, Prof. (Dr.) Arpit Jain, "Comparative Analysis of Optimizing Hybrid Cloud Environments Using AWS, Azure, and GCP", International Journal of Creative Research Thoughts (IJCRT), Vol.10, Issue 8, pp.e791-e806, August 2022. Available: http://www.ijcrt.org/papers/IJCRT2208594.pdf
FNU Antara, Om Goel, Dr. Prerna Gupta, "Enhancing Data Quality and Efficiency in Cloud Environments: Best Practices", International Journal of Research and Analytical Reviews (IJRAR), Vol.9, Issue 3, pp.210-223, August 2022. Available: http://www.ijrar.org/IJRAR22C3154.pdf
Pronoy Chopra, Akshun Chhapola, Dr. Sanjouli Kaushik, "Comparative Analysis of Optimizing AWS Inferentia with FastAPI and PyTorch Models", International Journal of Creative Research Thoughts (IJCRT), Vol.10, Issue 2, pp.e449-e463, February 2022. Available: http://www.ijcrt.org/papers/IJCRT2202528.pdf
Fnu Antara, Dr. Sarita Gupta, Prof. (Dr.) Sangeet Vashishtha, "A Comparative Analysis of Innovative Cloud Data Pipeline Architectures: Snowflake vs. Azure Data Factory", International Journal of Creative Research Thoughts (IJCRT), Vol.11, Issue 4, pp.j380-j391, April 2023. Available: http://www.ijcrt.org/papers/IJCRT23A4210.pdf
"Strategies for Product Roadmap Execution in Financial Services Data Analytics", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 1, page no.d750-d758, January-2023, Available : http://www.ijnrd.org/papers/IJNRD2301389.pdf
"Shanmukha Eeti, Er. Priyanshi, Prof.(Dr.) Sangeet Vashishtha", "Optimizing Data Pipelines in AWS: Best Practices and Techniques", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 3, pp.i351-i365, March 2023, Available at : http://www.ijcrt.org/papers/IJCRT2303992.pdf
(IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.10, Issue 1, Page No pp.35-47, March 2023, Available at : http://www.ijrar.org/IJRAR23A3238.pdf
Pakanati, D., Goel, E. L., & Kushwaha, D. G. S. (2023). Implementing cloud-based data migration: Solutions with Oracle Fusion. Journal of Emerging Trends in Network and Research, 1(3), a1-a11. https://rjpn.org/jetnr/viewpaperforall.php?paper=JETNR2303001
Swamy, H. (2020). Unsupervised machine learning for feedback loop processing in cognitive DevOps settings. Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering, 17(1), 168-183. https://www.researchgate.net/publication/382654014
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal for Research Publication and Seminar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Re-users must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. This license allows for redistribution, commercial and non-commercial, as long as the original work is properly credited.
