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Frontier Analysis of Machine Intelligence: Models, Architectures, and Next-Generation AI Systems

DOI https://doi.org/10.55640/corr-v04i05-01
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Arjun Mehta 1 , Prof. Kavya Sharma 1 ,
4 Department of Computer Science and Research, Institute of Open Research Studies, New Delhi, India
4 Department of Information Systems, Institute of Open Research Studies, New Delhi, India

Abstract

The rapid evolution of artificial intelligence has transformed machine intelligence from narrowly programmed computational functionality into increasingly adaptive, data-driven, and autonomous systems. Contemporary AI systems integrate learning models, distributed architectures, organizational knowledge, and automated decision processes, creating substantial opportunities as well as new requirements for governance, security, accountability, and human oversight. This research and review paper develops a conceptual framework for analyzing next-generation machine intelligence through the combined perspectives of computational models, system architectures, organizational knowledge, information security, and behavioral governance. The methodology synthesizes the supplied literature on engineering research artefacts, information security standards, employee compliance, security awareness, governance alignment, knowledge sharing, and knowledge-to-action processes. The analysis indicates that technical advancement alone is insufficient for sustainable machine intelligence. Effective AI architectures require alignment between intelligent computational components and organizational governance mechanisms. The proposed framework therefore emphasizes four interacting dimensions: intelligence capability, architectural integration, security and governance, and knowledge-to-action alignment. The findings suggest that future machine intelligence systems should be evaluated not only by predictive or computational performance but also by their ability to operate securely, translate knowledge into reliable action, and remain compatible with organizational controls. The paper concludes by identifying governance-aware architectures, adaptive security mechanisms, explainable decision processes, and human-machine collaboration as important directions for future research.

Keywords

Artificial Intelligence, Machine Intelligence, AI Architecture, Intelligent Systems

References

Y Bobbert (2017) Defining a research method for engineering a Business Information Security artefact. Proceedings of the Enterprise Engineering Working Conference (EEWC) Forum, Antwerp, Belgium
(2005) ITGI, Information Risks Who’s Business, are they?. IT Governance Institute, United States
M Siponen, R Willison (2009) Information Security management standards: problems and solutions. Information & Management 46(5): 267-270.
D Kluge, S Sambasivam, (2008) Formal Information Security Standards in German Medium Enterprises. Conisar, Phoenix.
Puhakainen P, SM (2010) Improving employees’ compliance through information systems security training; an action research study MIS Quarterly 34(4): 757-78.
M Workman, W Bommer, D (2008) Straub Security lapses and the omission of information security measures: A threat control model and empirical test. Computers in Human Behavior 24(6): 2799-2816.
B Lebek, J Uffen, M Neumann, B Hohler, M Breitner (2014) Information security awareness and behavior: a theory-based literature review. Management Research Review 12(37): 1049-1092.
W Yaokumah, S Brown (2014) An Empirical Examination of the relationship between Information Security / Business strategic alignment and Information Security Governance Journal of Business Systems. Governance and Ethics 2(9): 50-65.
W Flores, E Antonsen, M Ekstedt (2014) Information security knowledge sharing in organizations: Investigating the effect of behavioral information security governance and national culture. Computers & security 43: 90-110.
J Pfeffer, R Sutton (2001) The Knowing‐Doing Gap: How Smart Companies Turn Knowledge into Action no. Har

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