Securing Generative AI Applications Training Course
Securing Generative AI Applications Training Course is designed to equip professionals with advanced knowledge and practical skills to protect Generative AI systems, Large Language Models (LLMs), AI-powered applications, and enterprise AI ecosystems against emerging cyber threats.
Course Overview
Securing Generative AI Applications Training Course
Introduction
Securing Generative AI Applications Training Course is designed to equip professionals with advanced knowledge and practical skills to protect Generative AI systems, Large Language Models (LLMs), AI-powered applications, and enterprise AI ecosystems against emerging cyber threats. As organizations rapidly adopt AI copilots, intelligent agents, retrieval-augmented generation (RAG), and automated decision systems, securing these technologies has become a critical priority. This course explores AI security architecture, prompt injection defense, data protection, model risk management, adversarial machine learning, AI application security, secure AI development lifecycle (Secure AI SDLC), and responsible AI governance.
Participants will learn how to identify, assess, and mitigate vulnerabilities affecting Generative AI applications, including LLM attacks, data poisoning, model extraction, insecure APIs, AI supply chain risks, privacy leakage, and unauthorized AI access. Through hands-on exercises, industry scenarios, and real-world case studies, learners will develop strategies for implementing AI threat detection, zero-trust AI security frameworks, AI compliance controls, secure deployment practices, and continuous AI risk monitoring to build trustworthy and resilient AI solutions.
Course Duration
5 days
Course Objectives
By the end of this course, participants will be able to:
- Understand Generative AI security fundamentals and emerging AI cyber risk landscapes.
- Apply Secure AI Development Lifecycle (Secure AI SDLC) practices for AI applications.
- Identify and mitigate LLM vulnerabilities and AI application attack vectors.
- Implement prompt injection prevention and jailbreak defense strategies.
- Design secure architectures for AI copilots, AI agents, and RAG systems.
- Apply AI data privacy, protection, and governance frameworks.
- Perform AI threat modeling and adversarial risk assessments.
- Secure Generative AI APIs, integrations, and cloud AI platforms.
- Implement zero-trust security principles for AI environments.
- Understand AI supply chain security and third-party model risks.
- Deploy AI monitoring, logging, and incident response capabilities.
- Apply responsible AI security, compliance, and regulatory practices.
- Develop organizational strategies for enterprise-grade Generative AI security transformation.
Target Audience
- Cybersecurity professionals and security analysts
- AI engineers and machine learning engineers
- Software developers building AI applications
- Cloud security architects and DevOps teams
- SOC analysts and threat intelligence professionals
- Data scientists and AI researchers
- Risk, compliance, and governance professionals
- IT leaders implementing enterprise AI solutions
Course Modules
Module 1: Fundamentals of Generative AI Security
- Introduction to Generative AI security concepts and challenges
- Understanding LLMs, AI agents, and AI application ecosystems
- AI attack surfaces and emerging cyber threats
- Security principles for trustworthy AI adoption
- AI risk management frameworks and best practices
- Case Study: Enterprise AI Assistant Security Assessment
Module 2: Generative AI Threat Landscape and Attack Techniques
- LLM vulnerabilities and exploitation methods
- Prompt injection and indirect prompt attacks
- AI jailbreak techniques and defense mechanisms
- Model poisoning and adversarial attacks
- AI-powered cyber attack scenarios
- Case Study: RAG System Prompt Injection Incident
Module 3: Secure AI Application Architecture
- Designing secure Generative AI architectures
- AI application security patterns
- Secure API integration strategies
- Identity management and authentication controls
- Zero-trust architecture for AI applications
- Case Study: Secure Enterprise Copilot Deployment
Module 4: Protecting Large Language Models (LLMs)
- LLM security testing methodologies
- Model access protection and authorization
- Preventing model leakage and extraction attacks
- Secure model deployment practices
- LLM vulnerability assessment frameworks
- Case Study: LLM Model Protection Program
Module 5: Data Security and Privacy for Generative AI
- AI data lifecycle security
- Protecting training and operational datasets
- Preventing sensitive information disclosure
- Data encryption and access governance
- Privacy-enhancing technologies for AI
- Case Study: Healthcare AI Data Protection Scenario
Module 6: AI Application Security Testing and Monitoring
- AI penetration testing approaches
- Generative AI vulnerability scanning
- Security testing automation
- AI behavior monitoring and anomaly detection
- Continuous AI security improvement
- Case Study: Financial Services AI Security Testing
Module 7: AI Governance, Compliance, and Risk Management
- AI governance frameworks and policies
- Responsible AI security principles
- AI regulatory requirements
- Risk assessment methodologies
- Security controls for enterprise AI governance
- Case Study: Global Enterprise AI Governance Framework
Module 8: Incident Response and Future AI Security Strategies
- AI security incident response planning
- Detecting AI misuse and abuse
- Threat intelligence for Generative AI
- Building AI security operations capabilities
- Future trends in AI cyber defense
- Case Study: AI Security Breach Response Simulation
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.