Autonomous GenAI-Driven Cyber-Posture Optimization in Hybrid Enterprises
Keywords:
Generative AI, cyber-posture, hybrid enterprise, telemetry, reinforcement learning, identity policyAbstract
The purpose of this article is to explore the hybrid enterprise cyber-posture optimisation, using autonomous Generative AI (GenAI) systems. GenAI engine design uses aggregate data from on-premises and cloud infrastructures and automatically adjust adaptive security measures to fit the organization's risk tolerance to improve network segmentation, enforce identity-based policies, and reset encryption baselines via reinforcement learning.
Downloads
References
S. M. Bridges and R. B. Vaughn, “Fusing multiple detection methods to improve intrusion detection,” Computers & Security, vol. 29, no. 1, pp. 1–14, Feb. 2010.
N. McLaughlin, J. Butts, and R. Beyah, “Active learning for cybersecurity: Current progress and future directions,” IEEE Security & Privacy, vol. 17, no. 4, pp. 54–62, Jul.–Aug. 2019.
C. S. Caldeira and J. M. Ferreira, “Security policy management in hybrid cloud environments: Challenges and approaches,” IEEE Cloud Computing, vol. 6, no. 2, pp. 52–59, Mar.–Apr. 2019.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
P. Y. Simard et al., “Recent advances in reinforcement learning for cybersecurity,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 2413–2428, Jul. 2020.
T. M. Mitchell, Machine Learning. McGraw-Hill, 1997.
A. Shokri, V. Lenders, and J. Hubaux, “Machine learning in cyber security: A review,” ACM Computing Surveys, vol. 51, no. 4, pp. 1–36, Jul. 2018.
A. Wald et al., “Generative adversarial networks for network intrusion detection,” in Proc. IEEE Int. Conf. Cyber Security and Cloud Computing, New York, USA, 2018, pp. 152–157.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. MIT Press, 2018.
M. T. Goodrich and R. Tamassia, Algorithm Design and Applications. Wiley, 2019.
D. E. Denning, “An intrusion-detection model,” IEEE Transactions on Software Engineering, vol. SE-13, no. 2, pp. 222–232, Feb. 1987.
F. Zhang et al., “Risk-aware security policy management using probabilistic models,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 1, pp. 200–213, Jan. 2019.
J. D. Ullman et al., “Network telemetry and its role in cybersecurity,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2089–2110, Thirdquarter 2020.
D. M. Kaber and S. J. Endsley, “The effects of levels of automation and adaptive automation on human performance, situation awareness and workload in dynamic control tasks,” Theoretical Issues in Ergonomics Science, vol. 8, no. 4, pp. 353–373, Jul. 2007.
M. Salehie and L. Tahvildari, “Self-adaptive software: Landscape and research challenges,” ACM Transactions on Autonomous and Adaptive Systems, vol. 4, no. 2, pp. 14:1–14:42, May 2009.
J. Yan, X. Yu, and R. Zhang, “A survey on risk assessment and decision making in cybersecurity,” Computers & Security, vol. 81, pp. 1–19, May 2019.
A. Singla et al., “Hybrid cloud security: Challenges and solutions,” in Proc. IEEE Int. Conf. Cloud Computing Technology and Science (CloudCom), Sydney, Australia, 2017, pp. 123–130.
S. T. King et al., “Hybrid enterprise architectures: Patterns and practices for cloud adoption,” IEEE Software, vol. 35, no. 2, pp. 45–53, Mar.–Apr. 2018.
B. Biggio and F. Roli, “Wild patterns: Ten years after the rise of adversarial machine learning,” Pattern Recognition, vol. 84, pp. 317–331, Oct. 2018.
T. Fawcett and F. Provost, “Adaptive fraud detection,” Data Mining and Knowledge Discovery, vol. 1, no. 3, pp. 291–316, Sep. 1997.