https://lovely-bear-z93jzp.mystrikingly.com/blog/frequently-asked-questions-about-agentic-artificial-intelligence-ffeac609-9d14-4dca-9781-89a89aa10c6a https://notes.io/wKrcc Computational Intelligence is transforming the field of application security by allowing more sophisticated weakness identification, automated testing, and even autonomous threat hunting. This guide offers an thorough discussion on how AI-based generative and predictive approaches operate in AppSec, designed for security professionals and stakeholders in tandem. We’ll examine the growth of AI-driven application defense, its current strengths, limitations, the rise of “agentic” AI, and forthcoming directions. Let’s commence our exploration through the past, present, and future of artificially intelligent AppSec defenses. Origin and Growth of AI-Enhanced AppSec Foundations of Automated Vulnerability Discovery Long before machine learning became a hot subject, infosec experts sought to streamline security flaw identification. In , Dr. Barton Miller’s pioneering work on fuzz testing proved the effectiveness of automation. His 1988 class project randomly generated inputs to crash UNIX programs — “fuzzing” revealed that a significant portion of utility programs could be crashed with random data. This straightforward black-box approach paved the foundation for later security testing strategies. By the 1990s and early 2000s, developers employed basic programs and scanners to find typical flaws. Early static scanning tools behaved like advanced grep, scanning code for dangerous functions or hard-coded credentials. Even though these pattern-matching tactics were useful, they often yielded many spurious alerts, because any code matching a pattern was reported regardless of context. Progression of AI-Based AppSec Over the next decade, academic research and corporate solutions advanced, transitioning from hard-coded rules to intelligent reasoning. Machine learning slowly entered into the application security