Blackwall: An integrated AI-driven Framework for proactive Cybersecurity Defense

Abstract— Modern cybersecurity threats such as AI-driven attacks and zero-day exploits are overwhelming traditional defense systems that rely on static signatures or predefined rules. This paper introduces BlackWall, an integrated AI-driven framework for proactive cybersecurity defense. BlackWall consists of three core components: the Reverse Zero-day Algorithm (RZA) for autonomous vulnerability discovery, the Zero-Trust Verification Module (TVM) for continuous trust evaluation, and the False Positive Protocol (FPP), a deception-based system designed to mislead attackers and reduce false alerts. These modules operate independently but coordinate through a secure signal bus, enabling rapid response, resilience, and adaptive defense. In simulated attack scenarios, BlackWall improved detection accuracy by 37%, reduced response time by 82%, and cut false positives by 74% compared to traditional intrusion detection systems. These results demonstrate that BlackWall can serve as a robust foundation for next-generation, autonomous cybersecurity architectures.