OWASP GenAI Security Training is a focused two-day program designed to help professionals understand and mitigate security risks associated with Generative AI systems using the OWASP GenAI Security framework. This course covers GenAI threat landscapes, common vulnerabilities, attack vectors, and defensive strategies, enabling participants to design, deploy, and govern secure GenAI applications aligned with industry best practices and OWASP recommendations.

• Understand GenAI security fundamentals
• Learn OWASP GenAI security concepts
• Understand AI application vulnerabilities
• Gain knowledge of prompt injection risks
• Learn secure AI deployment practices
• Understand data privacy and compliance basics
• Explore AI threat detection concepts
• Identify enterprise AI security use cases

• Application and cloud security professionals
• AI and machine learning engineers
• DevSecOps and platform security teams
• Security architects and risk managers
• Technology leaders responsible for AI governance

• Basic understanding of cybersecurity or application security concepts
• Familiarity with AI, machine learning, or generative AI systems
• Awareness of APIs, web applications, or cloud-based architectures
• Interest in secure and responsible AI adoption

• Understanding OWASP GenAI Security principles and risks
• Identifying vulnerabilities in generative AI systems
• Applying secure design and deployment practices for GenAI
• Mitigating prompt injection, data leakage, and model abuse
• Evaluating governance, compliance, and AI security controls

• Understand GenAI security fundamentals
• Learn OWASP GenAI security concepts
• Understand AI application vulnerabilities
• Gain knowledge of prompt injection risks
• Learn secure AI deployment practices
• Understand data privacy and compliance basics
• Explore AI threat detection concepts
• Identify enterprise AI security use cases

Introduction to OWASP GenAI Security
• Overview of OWASP GenAI Security initiative
• GenAI threat landscape and risk categories
• Difference between traditional app security and GenAI security
• Security responsibilities across the AI lifecycle

GenAI Architecture and Attack Surface
• GenAI system components and data flows
• APIs, plugins, tools, and external integrations
• Model inputs, outputs, and execution contexts
• Identifying GenAI-specific attack surfaces

Common GenAI Vulnerabilities
• Prompt injection and indirect prompt attacks
• Sensitive data exposure and leakage risks
• Insecure output handling and hallucination risks
• Model abuse, misuse, and denial-of-service threats

Mitigation Strategies and Secure Design
• Input validation and prompt hardening techniques
• Output filtering and response controls
• Access control, authentication, and rate limiting
• Secure system and tool integration practices

Governance, Compliance, and Risk Management
• Logging, monitoring, and auditability for GenAI
• AI governance and policy enforcement
• Risk assessment and threat modeling for GenAI
• Alignment with OWASP, security, and compliance standards

Secure GenAI Use Cases and Best Practices
• Securing GenAI-powered applications
• DevSecOps integration for GenAI systems
• Incident response and vulnerability handling
• Future trends in GenAI security and defense

• OWASP GenAI security concepts
• AI application vulnerabilities
• Prompt injection techniques
• Data leakage and privacy risks
• Secure AI deployment practices
• Access control and authentication basics
• AI threat detection concepts
• Compliance and governance considerations
• Enterprise AI security use cases
• Practical GenAI security scenarios

• GenAI security scenario analysis
• Vulnerability identification and mitigation exercises
• Secure design and governance assessment
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in OWASP GenAI Security Training, validating their expertise in identifying, mitigating, and governing security risks in generative AI systems using OWASP-aligned best practices.