Year: 2025
Type: article
Abstract: This article presents a comprehensive overview of AI-driven security architectures and innovations in autonomous threat response. As cybersecurity landscapes evolve with increasingly sophisticated threats, traditional security approaches relying on signature-based detection and human intervention prove inadequate against modern attack methodologies. The paradigm shift toward autonomous security systems leverages machine learning and artificial intelligence to enable continuous adaptation and proactive defense mechanisms. The article examines foundational components of AI-driven security architectures, key innovations including reinforcement learning, generative adversarial networks, security orchestration platforms, and implementation strategies and best practices. While highlighting transformative potential, the article also addresses significant challenges, including model interpretability, adversarial vulnerabilities, computational constraints, and ethical considerations that security practitioners must navigate when deploying these advanced systems.
Author: Gowtham Kukkadapu
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411441183