http://decoyrental.com/members/dreamstone92/activity/841054/ https://posteezy.com/code-security-faqs-4 AI is redefining application security (AppSec) by enabling smarter bug discovery, automated assessments, and even semi-autonomous attack surface scanning. This article provides an in-depth discussion on how AI-based generative and predictive approaches are being applied in AppSec, designed for security professionals and decision-makers in tandem. We’ll delve into the development of AI for security testing, its current capabilities, limitations, the rise of agent-based AI systems, and forthcoming directions. Let’s begin our exploration through the past, present, and coming era of artificially intelligent application security. Evolution and Roots of AI for Application Security Early Automated Security Testing Long before machine learning became a hot subject, security teams sought to streamline bug detection. In the late 1980s, Dr. Barton Miller’s trailblazing work on fuzz testing demonstrated the power of automation. His 1988 class project randomly generated inputs to crash UNIX programs — “fuzzing” revealed that roughly a quarter to a third of utility programs could be crashed with random data. This straightforward black-box approach paved the groundwork for subsequent security testing methods. By the 1990s and early 2000s, developers employed basic programs and tools to find common flaws. Early source code review tools operated like advanced grep, inspecting code for risky functions or embedded secrets. Though these pattern-matching methods were helpful, they often yielded many incorrect flags, because any code resembling a pattern was labeled irrespective of context. Evolution of AI-Driven Security Models During the following years, scholarly endeavors and commercial platforms improved, shifting from rigid rules to context-aware interpretation. Data-driven algorithms slowly entered into AppSec. Early adoptions included neural networks for anomaly detection in sys