https://posteezy.com/agentic-artificial-intelligence-faqs-43 Machine intelligence is revolutionizing application security (AppSec) by allowing smarter bug discovery, automated assessments, and even semi-autonomous threat hunting. This write-up delivers an thorough discussion on how machine learning and AI-driven solutions operate in AppSec, designed for security professionals and decision-makers as well. We’ll explore the evolution of AI in AppSec, its modern features, limitations, the rise of “agentic” AI, and prospective trends. Let’s begin our journey through the foundations, present, and prospects of ML-enabled AppSec defenses. and Roots of AI for Application Security Foundations of Automated Vulnerability Discovery 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 showed the impact of automation. His 1988 research experiment 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 way for later security testing techniques. By the 1990s and early 2000s, developers employed basic programs and scanners to find typical flaws. Early source code review tools functioned like advanced grep, searching code for risky functions or embedded secrets. Even though these pattern-matching approaches were useful, they often yielded many incorrect flags, because any code mirroring a pattern was labeled without considering context. Progression of AI-Based AppSec From the mid-2000s to the 2010s, scholarly endeavors and industry tools advanced, shifting from hard-coded rules to intelligent analysis. Data-driven algorithms slowly made its way into AppSec. Early adoptions included neural networks for anomaly detection in network flows, and probabilistic models for spam or phishing — not strictly AppSec, but demon