Abstract
[1]. Artificial Intelligence (AI) is revolutionizing cybersecurity by improving threat identification, mitigation, and protection strategies. As cyber threats become more complex and sophisticated, AI-driven solutions play a key role in strengthening the security architecture and ensuring proactive protection, AIdriven solutions offer proactive defense strategies, real-time network monitoring, and automated security protocols. This research employs the The Gray Correlation Analysis (GRA) method is used to evaluate the performance of AI in five important domains: communication protocols (C1), node security (C2), network monitoring (C3), cryptography (C4), and security policy (C5). study highlights how AI optimizes security frameworks, mitigates cyber risks, and strengthens overall defense mechanisms. The results indicate that AI significantly improves cybersecurity resilience by addressing vulnerabilities across multiple layers of security. Research Significance: Cyber threats are evolving, requiring intelligent and adaptive security measures AI enhances conventional cybersecurity approaches by automating threat detection and mitigation. Assessing the impact of AI on the security architecture helps improve security mechanisms and response effectiveness. impact on key security components provides insights into its effectiveness. Methodology: Grey Relational Analysis (GRA) Grey Relational Analysis (GRA) is used to assess the relationship between multiple security factors in AI-driven cybersecurity. GRA helps in ranking and determining the most effective AI applications in cybersecurity by analyzing Communication Protocols (C1), Node Security (C2), Network Monitoring (C3), Cryptography (C4), and Security Policy (C5). Alternative Approaches: Communication Protocol (C1): AI-based secure communication frameworks and anomaly detection in data exchange. Node Security (C2): AI-driven authentication, endpoint protection, and intrusion detection at the node level. Network Monitoring (C3): AI-powered network traffic analysis, anomaly detection, and automated threat response. Cryptography (C4): AI-assisted encryption, quantumresistant algorithms, and secure key management. Security Policy (C5): AI-enhanced policy enforcement, adaptive security frameworks, and compliance monitoring. Evaluation Parameters: Threat Intelligence AI analyzes vast datasets to predict, dentify, analyze, and neutralize cyber threats by recognizing attack patterns and vulnerabilities before exploitation. Intrusion Detection and Prevention AI enhances Intrusion Detection Systems (IDS) and Through Intrusion Prevention Methods (IPM) identifying malicious activities and blocking attacks proactively. Malware Detection and Analysis AI-powered cybersecurity solutions detect, classify, and neutralize malware using machine learning algorithms and behavioral analysis. User and Entity Behavior Analytics (UEBA) AI monitors Monitor user behavior to identify abuse, prevent unauthorized access, and detect insider threats enhancing cybersecurity posture. Automated Incident Response AI accelerates cybersecurity responses by automating threat mitigation, reducing human intervention, and minimizing damage from cyberattacks. Results: The study findings indicate that AI significantly improves cybersecurity across all parameters. AI-driven network monitoring (C3) and cryptography (C4) exhibit the highest impact in mitigating threats. Node security (C2) and communication protocols (C1) demonstrate enhanced efficiency in securing endpoints and data exchange. Security policies (C5) benefit from AI-driven automation, ensuring compliance and real-time adaptation to threats. The GRA analysis highlights AI plays a key role in improving cybersecurity resilience and reducing the likelihood of cyber threats attacks.