LLM Security Engineering Training Course

LLM Security Engineering Training Course

Introduction

LLM Security Engineering Training Course is designed to equip cybersecurity professionals, AI engineers, software developers, and security architects with advanced skills to secure Large Language Models (LLMs) throughout the AI development lifecycle. As organizations rapidly adopt Generative AI, AI Agents, Retrieval-Augmented Generation (RAG), Foundation Models, and Enterprise AI Applications, securing LLM-powered systems has become a critical priority. This course explores modern LLM security engineering practices, including prompt injection defense, adversarial machine learning, AI threat modeling, data protection, model hardening, secure AI architecture, AI governance, and responsible AI deployment.

Participants will gain practical expertise in identifying and mitigating emerging AI security risks such as model extraction, data leakage, jailbreak attacks, hallucination risks, malicious fine-tuning, insecure plugins, API vulnerabilities, and supply-chain threats. Through real-world case studies, hands-on exercises, and security engineering frameworks, learners will develop the capability to design, test, monitor, and protect enterprise-grade LLM applications against evolving cyber threats while aligning with AI security standards, zero trust principles, and secure-by-design methodologies.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

1. Understand LLM security architecture, AI threat landscapes, and emerging Generative AI risks. 
2. Apply AI security engineering principles for designing secure LLM applications. 
3. Identify and mitigate prompt injection, jailbreak, and adversarial AI attacks. 
4. Implement secure LLM application development practices. 
5. Perform LLM threat modeling using modern AI risk frameworks. 
6. Apply AI red teaming and adversarial testing techniques. 
7. Secure RAG pipelines, vector databases, and knowledge retrieval systems. 
8. Protect sensitive information through AI privacy engineering and data security controls. 
9. Implement LLM access control, authentication, and authorization mechanisms. 
10. Develop strategies for LLM monitoring, logging, and AI incident response. 
11. Apply secure fine-tuning and model customization techniques. 
12. Understand AI governance, compliance, and responsible AI security requirements. 
13. Build enterprise-ready LLM security operations and defense strategies. 

Target Audience

1. Cybersecurity professionals and security analysts 
2. AI engineers and machine learning engineers 
3. Software developers building LLM applications 
4. Cloud security architects 
5. DevSecOps and MLOps engineers 
6. Security architects and enterprise architects 
7. Data scientists working with Generative AI models 
8. Risk, compliance, and AI governance professionals 

Course Modules

Module 1: Foundations of LLM Security Engineering

• Introduction to Large Language Models and Generative AI security challenges 
• Understanding LLM architecture, transformers, embeddings, and inference processes 
• AI security lifecycle and secure-by-design principles 
• Common LLM vulnerabilities and attack surfaces 
• Overview of LLM security frameworks and standards 
• Case Study: Enterprise AI Assistant Exposure Case

Module 2: LLM Threat Modeling and Risk Assessment

• Building LLM-specific threat models 
• Identifying AI attack surfaces across applications and infrastructure 
• Applying STRIDE, MITRE ATLAS, and AI risk assessment approaches 
• Risk prioritization for LLM-powered systems 
• Designing AI security controls and mitigation strategies 
• Case Study: Banking Chatbot Security Assessment

Module 3: Prompt Injection and Jailbreak Defense

• Understanding direct and indirect prompt injection attacks 
• Jailbreak techniques targeting LLM behavior 
• Input validation and prompt security controls 
• Context isolation and instruction hierarchy protection 
• Defensive prompt engineering techniques 
• Case Study: Corporate RAG Assistant Attack Simulation

Module 4: Secure LLM Application Development

• Secure coding practices for LLM applications 
• API security for AI services 
• Authentication and authorization for AI systems 
• Secure prompt management and secrets protection 
• Preventing insecure AI application vulnerabilities 
• Case Study: Healthcare AI Application Security Review

Module 5: Securing RAG Systems and AI Data Pipelines

• Retrieval-Augmented Generation security architecture 
• Vector database security considerations 
• Data poisoning and retrieval manipulation attacks 
• Document security and access controls 
• Protecting enterprise knowledge repositories 
• Case Study: Enterprise Knowledge Bot Data Leakage Incident

Module 6: LLM Adversarial Testing and AI Red Teaming

• AI penetration testing methodologies 
• Adversarial attacks against LLM systems 
• Model behavior testing and vulnerability discovery 
• Automated AI security testing tools 
• Building LLM red team programs 
• Case Study: AI Application Red Team Exercise

Module 7: LLM Monitoring, Governance, and Incident Response

• AI security monitoring and observability 
• Detecting abnormal model behavior 
• LLM logging and audit strategies 
• AI incident response frameworks 
• Governance controls for production AI systems 
• Case Study: Generative AI Security Incident Response

Module 8: Advanced LLM Security Architecture and Future Trends

• Secure AI architecture patterns 
• Zero Trust approaches for AI systems 
• Protecting AI agents and autonomous workflows 
• LLM supply chain security 
• Future developments in AI security engineering 
• Case Study: Autonomous AI Agent Security Deployment

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.