Artificial Intelligence in Offensive and Defensive Cybersecurity: Opportunities, Risks, and Ethical Boundaries
Abstract
Artificial Intelligence (AI) has become a transformative force in cybersecurity, offering powerful capabilities for threat detection, anomaly recognition, and predictive defense, while also exhibiting a dual-use nature that makes it equally capable of being leveraged for offensive cyber operations. It can greatly enhance digital resilience through advanced defense mechanisms. At the same time, the very same technology can be exploited for offensive purposes, such as adversarial attacks, deepfake-enabled fraud, and automated intrusions. This paper critically examines the opportunities, risks, and ethical dilemmas posed by AI in cybersecurity, drawing on both academic literature and recent case studies, including deepfake fraud incidents and empirical insights. The discussion highlights key risks and ethical challenges, including algorithmic bias, transparency gaps in explainability, the dual-use dilemma of AI in penetration testing, and governance voids stemming from the absence of harmonized global standards. The case studies illustrate both offensive and defensive deployments, emphasizing the urgency for governance and ethical frameworks that operationalize fairness, accountability, and transparency within AI systems. The analysis integrates policy insights from compliance frameworks such as NIST, ISO, and GDPR, positioning them as anchors for building trustworthy AI ecosystems. The paper concludes that while AI should not be regarded as a panacea for cybersecurity, it is an indispensable evolving tool that requires responsible deployment, human-in-the-loop oversight, and collaborative governance to ensure resilience. The proposed research roadmap identifies explainable AI, AI forensics, and cross-sector collaborations as priority areas for advancing both academic and industry understanding. The paper positions AI as both an asset and a liability, providing a balanced foundation for future governance models that safeguard innovation while mitigating systemic risks.
Keywords
Artificial Intelligence, Cybersecurity, Dual-Use Dilemma, Algorithmic Bias, Governance, Ethical Frameworks, Explainable AI, AI Forensics, Compliance (NIST, ISO, GDPR)
Citations
IRE Journals:
Zechariah Oluleke Akinpelu
"Artificial Intelligence in Offensive and Defensive Cybersecurity: Opportunities, Risks, and Ethical Boundaries" Iconic Research And Engineering Journals Volume 9 Issue 2 2025 Page 1024-1042
IEEE:
Zechariah Oluleke Akinpelu
"Artificial Intelligence in Offensive and Defensive Cybersecurity: Opportunities, Risks, and Ethical Boundaries" Iconic Research And Engineering Journals, 9(2)
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