https://www.youtube.com/watch?v=vMRpNaavElg Artificial Intelligence (AI) is transforming security in software applications by facilitating smarter bug discovery, test automation, and even self-directed attack surface scanning. This article delivers an in-depth overview on how generative and predictive AI function in the application security domain, crafted for cybersecurity experts and decision-makers in tandem. We’ll explore the growth of AI-driven application defense, its current features, limitations, the rise of autonomous AI agents, and future developments. Let’s commence our exploration through the past, current landscape, and coming era of artificially intelligent application security. Origin and Growth of AI-Enhanced AppSec Foundations of Automated Vulnerability Discovery Long before AI became a trendy topic, infosec experts sought to streamline vulnerability discovery. In the late 1980s, Professor Barton Miller’s trailblazing work on fuzz testing demonstrated the impact of automation. His 1988 class project randomly generated inputs to crash UNIX programs — “fuzzing” exposed that 25–33% of utility programs could be crashed with random data. This straightforward black-box approach paved the groundwork for future security testing strategies. By the 1990s and early 2000s, developers employed automation scripts and scanning applications to find widespread flaws. Early static scanning tools behaved like advanced grep, scanning code for insecure functions or fixed login data. Though these pattern-matching approaches were useful, they often yielded many spurious alerts, because any code matching a pattern was reported regardless of context. Progression of AI-Based AppSec During the following years, university studies and commercial platforms advanced, transitioning from static rules to context-aware analysis. ML slowly entered into AppSec. Early examples included deep learning models for anomaly detection in network traffic, and Bayesian filters for spam or phishi