Evaluating AI-Driven Cybersecurity Systems: Effectiveness, Adversarial Risks, and Ethical Considerations
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I1P142Keywords:
AI Cybersecurity, Intrusion Detection, Adversarial Machine Learning, Explainable AI, Ethical AI, PrivacyAbstract
Artificial Intelligence (AI) has emerged as a transformative force in cybersecurity, enabling advanced threat detection, real-time response, and automated decision-making across complex digital environments such as cloud systems, Internet of Things (IoT), and critical infrastructure. By leveraging machine learning and deep learning techniques, AI-driven cybersecurity systems can analyze large volumes of data to identify anomalies and evolving attack patterns more efficiently than traditional rule-based approaches. Despite these advancements, significant challenges remain. AI-based systems are increasingly vulnerable to adversarial threats, including evasion attacks, data poisoning, and model extraction, which can undermine detection accuracy and system reliability. Additionally, ethical concerns such as data privacy, algorithmic bias, and lack of transparency raise critical questions about trust, accountability, and responsible deployment in security-sensitive domains. This study adopts a systematic evaluation framework to assess AI-driven cybersecurity systems across three key dimensions: effectiveness, adversarial robustness, and ethical compliance. Through a structured analysis of existing literature and comparative assessment metrics, the research examines how these systems perform under both standard and adversarial conditions while addressing governance and ethical requirements. The findings indicate that AI significantly enhances detection accuracy and operational efficiency but remains susceptible to sophisticated adversarial manipulation and ethical limitations. The study contributes by proposing an integrated evaluation perspective that combines technical performance with security resilience and ethical considerations, providing a foundation for developing more robust and trustworthy AI-driven cybersecurity solutions.
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