What You'll Learn
- Understand the fundamentals of Large Language Models (LLMs) and their security landscape
- Explore the OWASP Top 10 for LLMs (2025) and why it matters for developers, architects, and security professionals
- Identify common vulnerabilities unique to LLMs, such as prompt injection and data leakage
- Learn practical techniques for defending against adversarial prompt manipulation
- Recognize risks of unbounded resource consumption and denial-of-wallet attacks
- Detect and mitigate model extraction and replication attempts
- Understand embedding inversion attacks and their impact on data privacy
- Explore cross-tenant risks in multi-user vector databases and retrieval-augmented generation (RAG)
- Implement safe input validation, sanitization, and filtering strategies
- Apply Role-Based Access Control (RBAC) and least-privilege design principles to LLM systems
- Build robust monitoring, logging, and anomaly detection pipelines for AI workloads
- Learn secure deployment practices for APIs and LLM-driven applications
- Apply adversarial robustness training and continuous red-teaming practices
- Explore strategies for preventing sensitive information disclosure from training data
- Balance usability with security when designing LLM-enabled user interfaces
- Learn about legal, ethical, and compliance considerations for AI security
- Gain hands-on experience with real-world case studies and attack simulations
- Develop a security mindset for building and auditing AI-powered systems
- Learn best practices for MLOps governance and secure lifecycle management
- Walk away with actionable checklists and frameworks to protect LLMs in production
Requirements
- Familiarity with web application concepts (APIs, databases, authentication)
- General awareness of cybersecurity principles (helpful but not required)
- Curiosity about Artificial Intelligence, Machine Learning, or LLMs
- No advanced math or deep AI background is needed - we focus on practical security
- Willingness to experiment with AI tools, prompts, and security testing scenarios
Who This Course is For
- Software developers who integrate LLMs into applications and want to avoid common pitfalls
- Security engineers and penetration testers interested in the newest category of AI threats
- AI/ML engineers who need to secure LLM-powered pipelines, APIs, and RAG systems
- Solution architects designing enterprise systems that include AI components
- Product managers and tech leads who want to understand risks before deploying LLMs in production
- DevOps and MLOps professionals responsible for monitoring and governance of AI systems
- Cybersecurity students and researchers exploring adversarial AI and AI ethics
- Compliance and risk management professionals looking to align AI use with security standards
- Business leaders and decision-makers seeking to make informed choices about adopting LLMs securely
- Anyone curious about the OWASP Top 10 for LLMs (2025) and eager to learn practical defense strategies
Your Instructor
Andrii Piatakha
Founder and CEO in IT-Bulls, Founder of Learn-IT University
4.4 Instructor Rating
48,730 Reviews
1,166,455 Students
73 Courses
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