AI Security Fundamentals for Developers Training Course
Hands-On Practice
The course includes a minimum of 30 minutes of guided, hands-on practice using prepared sandbox exercises. All labs use publicly available tools and sample applications — no delegate credentials or production data are used.
On completion of the course, delegates will be able to:
- Describe the AI threat landscape relevant to government engineering teams, including the distinction between traditional application security and AI-specific risks
- Identify and mitigate the ten vulnerabilities in the OWASP Top 10 for Large Language Model Applications (2025), including prompt injection, sensitive information disclosure, supply chain risks, and excessive agency
- Apply secure-by-design principles when integrating LLMs into services, including input/output handling, authentication, and authorisation patterns
- Recognise AI-specific supply chain and model provenance risks and apply appropriate controls
- Produce a lightweight threat model for an AI feature they are currently working on
- Identify the first three quick wins they can implement in their own codebase the following week
Assessment and Certification
- NobleProg Certificate of Completion issued to every delegate
- Informal knowledge checks throughout the day
- No formal examination
Follow-on Courses
Delegates completing this course are strong candidates for the two-day Advanced AI Security for Developers — Agentic Applications, Red-Teaming & MCP course, which builds directly on this foundation
This course is available as onsite live training in Morocco or online live training.Course Outline
Session 1 — 09:30 to 10:50 · The AI Security Landscape for Developers
- What's different about AI security: the gap between traditional AppSec and AI-era threats
- A quick tour of the AI stack: foundation models, fine-tuning, RAG, agents, and where risk sits in each
- Why government services face unique exposure: data sensitivity, public accountability, and regulatory context (UK AI Principles, DPA 2018, GDPR)
- The OWASP Top 10 for LLM Applications (2025) at a glance
- Interactive Slido poll: "What's the single AI feature your team is most worried about securing?"
Break — 10:50 to 11:10
Session 2 — 11:10 to 12:30 · OWASP Top 10 for LLM Applications (2025) — Part 1
- LLM01: Prompt Injection — direct and indirect attacks, real-world examples
- LLM02: Sensitive Information Disclosure — what models leak and why
- LLM03: Supply Chain — model provenance, third-party plugins, Hugging Face risks
- LLM04: Data and Model Poisoning — training data integrity basics
- LLM05: Improper Output Handling — why treating LLM output as user input matters
- Short live demo: Prompt injection against a sample chatbot
Lunch — 12:30 to 13:20
Session 3 — 13:20 to 14:40 · OWASP Top 10 for LLM Applications (2025) — Part 2+ Hands-On Lab
- LLM06: Excessive Agency — tool use, permissions, and the principle of least privilege
- LLM07: System Prompt Leakage — what happens when your system prompt escapes
- LLM08: Vector and Embedding Weaknesses — RAG-specific pitfalls
- LLM09: Misinformation and Over-reliance — hallucinations as a security issue
- LLM10: Unbounded Consumption — denial-of-wallet and resource exhaustion
- Hands-on lab (~30 minutes): Delegates attack and then harden a small LLM-backed service. Each delegate tests at least one prompt-injection pattern and implements one control. Group debrief at the end.
Break — 14:40 to 15:00
Session 4 — 15:00 to 16:30 · Building Secure AI Services for Government + Close
- Secure-by-design patterns: input validation, output filtering, guardrails
- Authentication, authorisation, and session management for AI-exposing endpoints
- Logging, monitoring, and incident response considerations for AI features
- Data protection touchpoints: DPIA triggers for AI features, UK GDPR Article 22 in practice
- Putting it together: a lightweight threat-modelling walkthrough for an AI feature
- Implementation planning: each delegate drafts their three quick wins for the week ahead
- Q&A, resources, and next steps
Requirements
Prerequisites
- Working knowledge of at least one modern programming language (Python, JavaScript/TypeScript, Go, Java, or C#)
- Basic familiarity with web applications and APIs
- No prior AI or security background is required — the course is designed as a levelling-up foundation
Audience
- Software developers and engineers building or integrating AI features
- Platform engineers and DevOps engineers supporting AI workloads
- Technical leads and architects responsible for AI-powered services
- Security champions embedded in engineering teams
- QA and test engineers testing AI-enabled systems
Need help picking the right course?
morocco@nobleprog.com or +971 4871 6715
AI Security Fundamentals for Developers Training Course - Enquiry
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