https://thyssen-basse-2.blogbright.net/application-security-faq-1741911686 https://telegra.ph/Generative-and-Predictive-AI-in-Application-Security-A-Comprehensive-Guide-03-14 Artificial Intelligence (AI) is transforming security in software applications by allowing heightened weakness identification, automated testing, and even semi-autonomous threat hunting. This article provides an in-depth discussion on how machine learning and AI-driven solutions are being applied in the application security domain, written for cybersecurity experts and stakeholders as well. We’ll delve into the growth of AI-driven application defense, its current features, limitations, the rise of “agentic” AI, and future directions. Let’s start our journey through the history, current landscape, and prospects of artificially intelligent AppSec defenses. Evolution and Roots of AI for Application Security Early Automated Security Testing Long before AI became a buzzword, security teams sought to mechanize vulnerability discovery. In the late 1980s, the academic Barton Miller’s trailblazing work on fuzz testing showed the effectiveness of automation. His 1988 university effort randomly generated inputs to crash UNIX programs — “fuzzing” uncovered that 25–33% of utility programs could be crashed with random data. This straightforward black-box approach paved the foundation for subsequent security testing strategies. By the 1990s and early 2000s, engineers employed automation scripts and tools to find common flaws. Early static scanning tools operated like advanced grep, inspecting code for insecure functions or hard-coded credentials. While these pattern-matching tactics were helpful, they often yielded many spurious alerts, because any code resembling a pattern was labeled regardless of context. Progression of AI-Based AppSec Over the next decade, academic research and industry tools grew, moving from rigid rules to intelligent reasoning. ML slowly entered into the application security realm. Ear