INTEGRATED AI FRAMEWORKS FOR ADVANCED CYBERSECURITY AND THREAT INTELLIGENCE

As cyber threats become more sophisticated, scalable and rapid, traditional rule based defenses are at a loss to keep
up. This paper proposes an integrated AI-driven framework aimed at improving the cybersecurity and threat
intelligence capabilities by combining real-time data ingestion, multimodal and adaptive learning. The framework
aggregates myriad sources of data - network logs, endpoint telemetry, external threat feeds and unstructured
intelligence (aka OSINT, dark web feeds) - and uses machine learning, deep learning and natural language processing
to detect, classify and predict malicious behavior. A modular architecture supports both being supervised and
unsupervised learning, anomaly detection and continuous retraining to identify known and novel (zero day) threats.
Results from experimental evaluations show immense increase in detection accuracy, speed of response and decrease
in false positives as compared to conventional methods. The paper also addresses implementation challenges - such
as data quality, privacy and interpretability - and future choices for ongoing research on bounds, explainability and
ethics of AI empowered cyber defense systems.