AI & Web Application Security: A Practical Guide to Risks & Responses (TTAI2835)

About This Course:

AI Secure Programming for Web Applications / Technical Overview is built for security professionals, technical leaders, developers, and stakeholders who need a strong starting point to understand how AI is reshaping risks in modern web applications. As AI-powered features like chatbots, language models, and generative content become more common across systems, they bring new vulnerabilities that many teams are not yet prepared to address. This course helps you get up to speed with the key concepts, attack types, coding considerations, and design decisions that impact web security when AI is involved.

Through expert instruction, real-world demos, and focused discussion, you will explore how threats like prompt injection, model manipulation, and unsafe output can emerge in real applications, and what it looks like to mitigate them effectively. The course covers essential secure programming patterns for AI-enabled features, practical guidance for working with APIs and AI-generated content, and team-ready advice for managing risk from tools like ChatGPT or Copilot. This is a valuable first step for anyone looking to take on AI-related security more confidently, whether leading development projects, evaluating vendor tools, or beginning to build internal policies and protections. You will leave with a clearer understanding of where to start, what to look for, and how to support safer adoption of AI in your web environment.

Course Objectives:

• Explain the core risks AI introduces to web applications, including how models behave differently than traditional code and why that matters for security.

• Identify common attack methods used against AI-powered systems, such as prompt injection, model manipulation, and unsafe AI-generated output.

• Understand where AI shows up in modern web apps, and begin recognizing how features like chatbots, AI-based search, and LLMs affect system behavior and risk.

• Describe practical guardrails and coding patterns that help reduce the risk of using or connecting AI in a web application, even if you are not writing code directly.

• Know what to look for when evaluating AI tools and services, and how to ask the right questions about privacy, input handling, and model behavior.

• Use OWASP AI and LLM guidance as a starting point to frame risk areas, support internal conversations, and align your organization with emerging AI security standards.

Prerequisites:

• Basic understanding of how web applications are structured and delivered

• Familiarity with common application security concerns, such as input validation and API access

• Comfort reviewing technical diagrams, workflows, or simple code examples from a security perspective