AI & Web Application Security A Practical Guide to Risks & Responses (TTAI2835)
Understand how AI impacts web app security and learn practical ways to spot risks, guide safe use, and reduce exposure.
FormatsVirtual Classroom LiveTopicsApplication Development · AI & machine learning · Cybersecurity
What you’ll learn
- Foundations of AI and Secure Coding for Web Applications
- The evolving AI threat landscape: Risks and opportunities
- Why AI awareness matters for secure coding and enterprise security
- Core AI concepts: Machine learning, deep learning, LLMs, and generative AI
- Common ways AI intersects with software development and security
- Demo: How AI models can be embedded in modern applications
- Secure Coding Principles in the Age of AI
- AI-specific coding vulnerabilities
- Threats introduced by integrating AI/ML into apps
- Key differences between traditional secure coding and AI/ML secure development
- Case study: Attack scenarios involving poor secure coding in AI models
- OWASP guidance
- Secure vs. insecure AI-infused code
- How AI Attacks Your Code, Systems, and Teams
- Real-world AI-driven attack techniques: prompt injection, data poisoning, evasion
- AI-generated code: new risks and review challenges
- Model manipulation and AI backdoors
- Common AI-related vulnerabilities in web apps and APIs
- Human-in-the-loop risks: trust, overreliance, and social engineering
- Demo: Adversarial Attacks on AI
- Defending Against AI-Powered Attacks
- Building an enterprise AI defense strategy
- Threat modeling with AI/ML in mind
- Establishing governance, model monitoring, and audit trails
- How to assess and verify AI components in your stack
- Best practices for mitigating model poisoning, backdoors, and misuse
- Tools and frameworks for secure AI development
- Securing the software supply chain for AI-integrated apps
- Policies to reduce exposure to AI-generated vulnerabilities
- Reviewing code with AI threat awareness
- Secure AI Integration in Web Applications
- Integrating AI responsibly into production web systems
- Validating input/output of models and preventing injection
- Secure API design for AI services
- Handling user data securely in AI workflows
- Demo: Using a Python AI Model from a Web Application
- Natural Language Processing (NLP) and AI Security Risks
- NLP systems and their security challenges (e.g., prompt injection, data leakage)
- How attackers use NLP to trick AI-powered systems
- Using NLP for vulnerability detection and monitoring
- Review prompt injection and mitigation techniques
- AI Risk Management and Security Leadership
- Governance frameworks for AI (NIST AI RMF, ISO/IEC standards)
- Managing AI risk across the SDLC
- Setting up enterprise-wide guardrails for secure AI use
- Secure AI deployment checklists
- Evaluating tools like GitHub Copilot, ChatGPT, and internal LLMs
- Guiding development teams in secure AI usage
- Staying Safe with AI Tools at Work
- Where AI tools are commonly used across roles and departments
- Safe data sharing practices for employees using AI (what's OK vs. what's risky)
- How to create and share clear internal guidelines and review processes
- Role of security leaders in managing workplace AI usage and reducing shadow AI
- AI Playbook / Addendum
Upcoming training
No upcoming dates are listed yet. You can still enquire with flexible dates, and we’ll check options for your team.