Published Paper Details:

The proliferation of autonomous security solutions based on artificial intelligence (AI) and machine learning (ML) is a response to the growing complexity of cyber security threats. These automated systems are designed to detect, prevent, and mitigate cyber risks without the need for human actions. However, this automation has to be thoroughly validated using robust security testing approaches. Vulnerability Assessment and Penetration Testing (VAPT) is particularly useful for these autonomous systems because it allows for the efficient identification, analysis, and mitigation of security vulnerabilities. With AI-based automation, VAPT improves threat detection, refines penetration testing processes, and allows for perpetual security oversight. This paper investigates how VAPT can be improved with the assistance of AI technologies, the tools and techniques employed, and the various hurdles faced when securing autonomous security systems. In addition, it examines how AI-enabled security testing frameworks work with modern security architectures like Zero Trust and SOAR to bolster an organization's defense against cyber threats. Finally, this paper discusses future possibilities such as evolution of AI and blockchain incorporation, and self-directed threat hunting technologies that are expected to revolutionize the next generation of autonomous security testing methodologies.