Generative AI Security: Principles and Practices Training by Tonex
This course offers a comprehensive overview of security concerns specific to generative artificial intelligence (AI), including both deep learning models like GPT (Generative Pre-trained Transformer) and DALL-E, and their applications. With a blend of theoretical knowledge and practical exercises, participants will learn about the vulnerabilities, ethical considerations, and mitigation strategies essential for deploying secure and responsible AI systems. The curriculum is designed to equip engineers and managers with the skills needed to identify potential security threats, implement robust security measures, and oversee the development of generative AI technologies with a strong emphasis on security and ethical integrity.
Target Audience:
- Engineers: Software engineers, AI/ML engineers, and cybersecurity professionals looking to deepen their understanding of AI security.
- Managers: Project managers, product managers, and decision-makers overseeing AI projects who need to understand the security and ethical implications of generative AI technologies.
Learning Objectives:
Upon completion of this course, participants will be able to:
- Understand Generative AI Security Risks: Identify the unique security vulnerabilities associated with generative AI technologies.
- Implement Security Best Practices: Apply best practices and strategies to mitigate risks in the development and deployment of generative AI systems.
- Navigate Ethical Considerations: Navigate the ethical landscape of generative AI to ensure the responsible use of technology.
- Develop Security Policies: Create and enforce policies and frameworks that ensure the ethical and secure use of generative AI in organizational contexts.
- Respond to Security Incidents: Prepare and respond effectively to security incidents involving generative AI systems.
Course Outline:
Module 1: Introduction to Generative AI and Security
- Overview of Generative AI Technologies
- Understanding the Security Landscape of AI
- Case Studies of AI Security Breaches
Module 2: Vulnerabilities Specific to Generative AI
- Exploration of Common Attack Vectors
- Deepfakes and Synthetic Media Risks
- Data Poisoning and Model Inversion Attacks
Module 3: Ethical Considerations in Generative AI
- Bias and Fairness
- Privacy Concerns with Generative Models
- Regulatory and Compliance Issues
Module 4: Security Best Practices for Generative AI
- Secure Data Handling and Model Training
- Robustness and Resilience in AI Models
- Anomaly Detection and Threat Monitoring in AI Systems
Module 5: Implementing Security Measures
- Encryption and Secure Model Serving
- Access Control and Authentication for AI Systems
- Audit Trails and Incident Response Planning
Module 6: Developing and Enforcing AI Security Policies
- Creating a Security-First Culture in AI Development
- Policy Development for AI Ethics and Security
- Case Study: Implementing an AI Security Framework
Module 7: Future of Generative AI Security
- Emerging Threats and Challenges
- Advances in AI Security Technologies
- Preparing for the Future Security Landscape of AI
Module 8: Capstone Project
- Participants will work on a project that involves identifying potential security and ethical issues in a hypothetical generative AI application, proposing a comprehensive security and ethical framework to mitigate these issues, and presenting their solution.
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, hands-on workshops, and project-based learning, facilitated by experts in the field of AI security. Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Generative AI Security.