Abstract :

The Rapid Expansion Of Cloud Computing, Internet Of Things Devices, Mobile Platforms, Remote Work Infrastructures, Software-defined Networks, Digital Services, And Interconnected Enterprise Ecosystems Has Significantly Increased The Complexity And Scale Of Modern Cybersecurity Threats. Conventional Security Systems Frequently Depend On Static Rules, Known Attack Signatures, Isolated Monitoring Tools, And Manually Configured Response Mechanisms, Making Them Less Effective Against Zero-day Attacks, Advanced Persistent Threats, Ransomware, Polymorphic Malware, Phishing, Insider Misuse, Credential Compromise, Lateral Movement, And Rapidly Evolving Adversarial Behavior. This Research Proposes An Adaptive Threat Intelligence: AIIntegrated Holistic Framework For Modern Cybersecurity That Combines Heterogeneous Security-data Acquisition, Contextual Preprocessing, Machine Learning, Deep Learning, Anomaly Detection, Natural Language Processing, Behavioral Analytics, Threat Intelligence Fusion, Dynamic Risk Assessment, And Adaptive Response Within A Unified Architecture. The Proposed Framework Continuously Collects Network Traffic, Endpoint Telemetry, Authentication Events, Cloud Audit Records, Application Logs, Vulnerability Information, Email Characteristics, Threat Indicators, User Behavior, Device Context, And External Intelligence Feeds. Random Forest, Support Vector Machine, XGBoost, Isolation Forest, Deep Neural Networks, And NLP-based Intelligence Analysis Are Integrated To Identify Known Attacks, Previously Unseen Anomalies, Malicious Communication, Compromised Identities, Suspicious Behavior, And Emerging Threat Patterns. A Hybrid Threat-fusion Engine Correlates Model Predictions With Indicators Of Compromise, Tactics, Techniques And Procedures, Asset Criticality, Vulnerability Exposure, Identity Confidence, And Behavioral Deviation To Classify Events As Normal, Suspicious, High Risk, Or Critical Threat. The Framework Dynamically Initiates Actions Such As Enhanced Monitoring, Step-up Authentication, Traffic Restriction, Endpoint Isolation, Maliciousdomain Blocking, Session Termination, Account Suspension, Incident Escalation, And Security Operations Center Notification. The Architecture Consists Of Five Interconnected Layers: Cybersecurity Data And Threat Intelligence Acquisition, Security Preprocessing And Contextual Intelligence, AI-Integrated Threat Detection And Analytics, Adaptive Risk Assessment And Automated Response, And Security Operations, Governance And Continuous Learning. Illustrative Conceptual Evaluation Demonstrates Improved Detection Accuracy, Precision, Recall, F1-score, Threat-intelligence Efficiency, And Reduced Analytical Response Time Compared With Traditional Rule-based Security, Signature-based Detection, And Conventional Machine Learning Approaches. The Framework Provides A Scalable Foundation For Adaptive Cybersecurity Across Enterprise Networks, Cloud Environments, IoT Ecosystems, Critical Infrastructure, Financial Services, Healthcare Platforms, And Distributed Digital Systems.

Published:

09-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

367-377

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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