Training outline: Secure and Efficient AI Development
We focus on practical delivery. Expect active participation, hands‑on exercises, real‑world examples and case studies. The agenda can be adjusted or tailored to your organisation.
Why application and AI security matter
- Laws, regulations, compliance and developer responsibilities
- Why secure and trustworthy AI‑driven applications matter for your users (case studies and real‑world examples)
- How attackers think vs. how developers and data scientists think
Finding and fixing vulnerabilities in real systems
Real applications and AI‑augmented workflows, dozens of examples and case studies.
Authorization
- Most common issues and how to spot them (BOLA, BFLA, BOPLA...)
- Correct authorization patterns in API and AI‑driven systems
- ABAC, RBAC – authorization as a regulatory requirement
- Automated tools for application security testing (DAST, vulnerability scanners)
- Case studies and analysis of real incidents
Logging & monitoring
- Typical pitfalls (log injection, over‑logging...)
- How to log correctly and in line with compliance
- Types of logs – audit logs, application logs…
- Log management, SIEM, monitoring and alerting
- Case studies and analysis of real incidents
Dependencies & supply chain
- Most common vulnerabilities and how to detect them
- Package manager and model registry integrity
- Automated SCA tools for dependencies and licenses
- Virtual patching, WAF and RASP
- Vulnerabilities related to technical debt
- Case studies and analysis of real incidents
Resource consumption & abuse
- Typical issues and how to uncover them
- Correct rate‑limit design for APIs and AI endpoints
- Cloud service consumption and overuse of 3rd‑party services
- Case studies and analysis of real incidents
Injection vulnerabilities
- Most common flaws and how to detect them (NoSQL, command, code, SQL...)
- Prompt injection and data exfiltration in LLM systems
- Correct query parametrization and ORM usage
- SAST and other tools for code‑level analysis
- Case studies and analysis of real incidents
Secure Software & AI Development Lifecycle
- How to embed security and AI guardrails into your SDLC
- Where to find trustworthy information about vulnerabilities and incidents
- Backups, incident response and disaster recovery
Security tooling
- SAST
- DAST
- SCA
- WAF, RASP
- SIEM
Secure vibe coding & AI usage
- How to use AI tools safely and configure them properly
- Risks of vibe‑coding tools and unsafe patterns
- Case studies and analysis of real incidents
Developers see us as peers because we have an engineering background ourselves – we're not just "trainers".
We are practitioners who have both built and secured real applications. Our trainings are practical and go deep.