OWASP Global AppSec EU 2026 Vienna

With the rise of AI, especially large language models, it seems every security workflow will soon be automated or heavily supported by automation - from LLM-powered threat-intelligence enrichment or compliance mappings to AI-written threat models, codefixes and complete CISO roadmaps. But which processes will truly benefit, and in which cases will AI just increase the risk of adding cost and complexity? As security managers or leaders, how can we determine where to focus our efforts and investments upfront?

This talk presents a practical framework for evaluating the effectiveness of AI-driven automation in application security and related fields. First, we explore how to identify processes that are strong candidates for automation based on criteria such as repeatability, return on investment, and risk tolerance. Then, we map typical security processes to AI approaches, including large language models (LLMs), traditional machine learning, retrieval-augmented generation (RAG), and hybrid systems.

We will learn how these solutions are applied to critical security areas, such as vulnerability management, secure software development, threat detection, and compliance. We will explore an AI Capability Map, industry benchmarks, and real-world examples, such as the use of RAG-powered chatbots for security guidance and LLMs for compliance analysis. Our goal is to help you determine where AI would be a good fit for your organization and where you would likely see measurable value when applying it, so that you can make informed decisions. Also, we will examine the available data: In which areas of the industry is value already being recognized? We explore potential pitfalls, from fragile LLM implementations to poor risk modeling, and discuss how to avoid wasting resources.

Using industry data, real-world experience, and structured criteria, this talk provides security leaders and practitioners with more guidance in this rapidly evolving field.