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Research Paper | Information Security | Volume 15 Issue 3, March 2026 | Pages: 1057 - 1066 | India
Governance and Strategic Integration of AI-Driven Autonomous Cyber Defence Systems in Large Enterprises
Abstract: The rapid proliferation of artificial intelligence (AI), reinforcement learning (RL), and self-adaptive control loops-offer real-time detection, containment, and response with minimal human intervention across enterprise technology stacks has fundamentally reconfigured the cybersecurity threat landscape and the organisational responses to it. These systems promise reduced breach costs (e.g., average savings of $2.22 million) and operational resilience, yet their deployment introduces novel risks including model drift, adversarial attacks, bias, and accountability gaps. Drawing on empirical data from industry surveys, peer-reviewed research, and case studies from sectors including financial services, healthcare, critical infrastructure, and telecommunications, the paper develops a multi-dimensional framework for responsible ACDS deployment. Central findings indicate that organisations achieving mature AI governance in cybersecurity realise up to 76% faster mean time to detect (MTTD) and 84% faster mean time to respond (MTTR) compared to traditional approaches. However, ungoverned or poorly integrated ACDS introduce systemic risks including algorithmic bias, liability ambiguity, over-automation failure modes, and regulatory non-compliance. The paper proposes an integrated Governance-Strategy-Operations (GSO) model and provides empirical benchmarks across governance maturity dimensions. It concludes with actionable recommendations for CISOs, boards, and policy-makers seeking to harness AI-driven defence while preserving accountability, transparency, and human oversight.
Keywords: AI governance, autonomous cyber defence, agentic AI, NIST AI RMF, EU AI Act, ISO 42001, enterprise cybersecurity strategy
How to Cite?: Badri S., "Governance and Strategic Integration of AI-Driven Autonomous Cyber Defence Systems in Large Enterprises", Volume 15 Issue 3, March 2026, International Journal of Science and Research (IJSR), Pages: 1057-1066, https://www.ijsr.net/getabstract.php?paperid=SR26318172747, DOI: https://dx.dx.doi.org/10.21275/SR26318172747