Generative AI Security and Risk Management Training Course
Generative AI Security and Risk Management Training Course is designed to equip professionals with advanced knowledge and practical capabilities to secure Generative AI ecosystems, Large Language Models (LLMs), AI applications, AI infrastructure, and enterprise AI workflows.
Course Overview
Generative AI Security and Risk Management Training Course
Introduction
Generative AI Security and Risk Management Training Course is designed to equip professionals with advanced knowledge and practical capabilities to secure Generative AI ecosystems, Large Language Models (LLMs), AI applications, AI infrastructure, and enterprise AI workflows. As organizations rapidly adopt AI-powered automation, intelligent assistants, Retrieval-Augmented Generation (RAG), autonomous AI agents, and machine learning platforms, the need for robust AI cybersecurity, governance, compliance, privacy protection, threat modeling, and responsible AI practices has become critical. This course explores emerging Generative AI security risks, including prompt injection attacks, data leakage, model manipulation, adversarial AI threats, AI supply chain vulnerabilities, shadow AI usage, and regulatory challenges.
Participants will gain hands-on expertise in building secure AI environments through AI risk assessment frameworks, LLM security controls, AI governance models, secure prompt engineering, identity and access management, data protection strategies, model monitoring, and incident response planning. The course integrates industry best practices, real-world enterprise case studies, and modern security frameworks to help organizations achieve trusted AI adoption, resilient AI operations, and secure digital transformation while balancing innovation, compliance, and cybersecurity requirements.
Course Duration
5 days
Course Objectives
By the end of this course, participants will be able to:
- Understand Generative AI security principles, AI threat landscapes, and emerging cybersecurity challenges.
- Implement LLM security frameworks and enterprise AI protection strategies.
- Identify and mitigate prompt injection, jailbreak, and adversarial AI attacks.
- Apply AI risk management frameworks and governance methodologies.
- Develop secure Generative AI architectures and deployment models.
- Protect sensitive information through AI data privacy and confidentiality controls.
- Perform AI security assessments, vulnerability analysis, and risk evaluations.
- Design Responsible AI governance and compliance programs.
- Implement secure prompt engineering and AI application defense mechanisms.
- Manage AI identity, access control, authentication, and authorization security.
- Establish AI monitoring, auditing, and continuous security improvement processes.
- Develop Generative AI incident response and recovery strategies.
- Build organizational capabilities for secure AI transformation and cyber resilience.
Target Audience
- Chief Information Security Officers (CISOs)
- Cybersecurity Engineers and Analysts
- AI Engineers and Machine Learning Professionals
- Cloud Security Architects
- Data Scientists and Data Engineers
- IT Risk and Compliance Professionals
- Enterprise Architects and Technology Leaders
- AI Governance and Security Managers
Course Modules
Module 1: Foundations of Generative AI Security
- Introduction to Generative AI security concepts and cybersecurity fundamentals
- Understanding LLMs, foundation models, AI agents, and AI applications
- AI attack surfaces and emerging security challenges
- Security principles for enterprise AI adoption
- Generative AI risk management lifecycle
- Case Study: Enterprise AI Assistant Security Assessment
Module 2: Generative AI Threat Landscape and Attack Techniques
- Understanding AI-specific cyber threats and vulnerabilities
- Prompt injection and indirect prompt injection attacks
- Jailbreaking and model manipulation techniques
- Data poisoning and adversarial machine learning attacks
- AI supply chain security risks
- Case Study: Financial Services LLM Attack Simulation
Module 3: Secure Large Language Model Architecture
- Designing secure LLM-powered enterprise architectures
- Secure AI infrastructure and deployment models
- Cloud AI security considerations
- Model isolation and sandboxing techniques
- Secure API integration for AI services
- Case Study: Healthcare AI Platform Protection
Module 4: AI Data Security, Privacy, and Compliance
- AI data protection strategies
- Data classification and privacy controls
- Preventing sensitive data leakage through AI systems
- Encryption and secure data pipelines
- AI compliance requirements and regulatory frameworks
- Case Study: Enterprise Data Leakage Prevention Program
Module 5: Prompt Engineering Security and AI Application Defense
- Secure prompt engineering principles
- Prompt validation and filtering techniques
- Guardrails for AI applications
- Output validation and content security
- Preventing malicious AI interactions
- Case Study: Customer Service AI Security Enhancement
Module 6: AI Governance, Risk, and Compliance Management
- Developing AI governance frameworks
- AI risk identification and assessment methodologies
- Responsible AI principles
- AI audit and accountability processes
- Security policies for enterprise AI adoption
- Case Study: Global Enterprise AI Governance Framework
Module 7: AI Security Operations and Incident Response
- AI security monitoring and threat detection
- Logging and auditing AI activities
- AI incident response processes
- Security automation for AI environments
- Continuous AI risk management
- Case Study: AI Security Incident Investigation
Module 8: Future of Generative AI Security and Emerging Risks
- Autonomous AI agents and security challenges
- AI-powered cyber threats
- Future AI regulations and standards
- Zero Trust approaches for AI environments
- Building AI cyber resilience strategies
- Case Study: Enterprise AI Transformation Security Roadmap
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.