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Cursusaanbod
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
Vereisten
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
7 Uren
Aangepaste bedrijfsopleiding
Opleidingsoplossingen ontworpen exclusief voor bedrijven.
- Aangepaste inhoud: We passen de syllabus en praktijkopdrachten aan naar de echte doelen en behoeften van uw project.
- Voor flexibel schema: Datums en tijden aangepast aan het rooster van uw team.
- Formaat: Online (live), In-company (bij uw kantoren) of Hybride.
Prijs per privégroep, online live training, startend vanaf 1600 € + BTW*
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