Responsible AI Governance for Cybersecurity: Insights from Chanakya Neeti
DOI:
https://doi.org/10.31305/rrjiks.2025.v2.n2.014Keywords:
Indian Knowledge System, Chanakya Neeti, Accountability, Proactive Monitoring, Cybersecurity, Responsible AI, Ethical AI, CNIRAI Framework, AI GovernanceAbstract
The ancient Indian treatise Chanakya Neeti, which was written by the scholar Acharya Chanakya, is a treatise on the principles of administration, morality, and strategy. It highlights the significance of being intelligent, attentive, and acting in accordance with moral principles. Artificial Intelligence (AI) is transforming cybersecurity by enabling advanced anomaly detection, predictive analytics, and continuous compliance checks. However, these systems raise pressing challenges around accountability, fairness, and responsible use. This paper introduces the Chanakya Neeti-Inspired Responsible AI (CNIRAI) framework, which combines five ethical pillars Accountability (Dandniti), Proactive Monitoring (Jagriti), Fairness (Nyay), Strategic Oversight (Rajniti), and Trust (Vishwas) with state-of-the-art AI tools. Following a systematic review of over 340 records (2018–2025), 40 were selected for detailed analysis to ground the framework. The ideas of intelligence collection, layered protection, insider awareness, and ethical governance are all in line with modern AI-driven cybersecurity frameworks and Responsible AI practices. The European Union Agency for Cybersecurity (ENISA) and the National Institute of Standards and Technology (NIST) have both done research that can be used to make the Chanakya Neeti-inspired framework. These include the three-layer artificial intelligence (AI) cybersecurity framework and the AI Risk Management Framework. CNIRAI is represented as a three-layered architecture (Governance–Ethical–Technical) and compared with major frameworks such as the EU AI Act, IEEE guidelines, and NIST RMF. Results highlight CNIRAI’s stronger cultural inclusivity and cybersecurity specificity. The paper concludes with implementation guidelines, limitations, and directions for empirical validation to operationalize responsible AI-driven cybersecurity.
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