Securing the Future: Generative AI and its Security Implications

Description:

This training on "Securing the Future: Generative AI and its Security Implications," offers a comprehensive exploration of Generative AI, delving into its foundational principles, diverse models, and wide-ranging applications. Participants will gain insights into the security landscape of Generative AI, identifying potential threats and vulnerabilities, and learning robust techniques for mitigating these risks. Through real-world case studies, the course emphasizes practical applications and emerging solutions, while also addressing ethical and regulatory considerations. By the end of the course, learners will be equipped with the knowledge and skills necessary to develop and secure Generative AI systems effectively.

Course Duration: 1 Day

Course Code: BDT355

Learning Objectives:

• Comprehend the foundational principles and models of Generative AI.
• Identify and assess security threats and vulnerabilities associated with Generative AI.
• Implement best practices and techniques for mitigating security risks in Generative AI.
• Analyze real-world applications and case studies to understand the impact of Generative AI on security.
• Explore ethical and regulatory considerations in the development and deployment of Generative AI.

• No coding experience required; suitable for beginners

• AI practitioners and researchers looking to deepen their understanding of Generative AI security.
• Cybersecurity professionals aiming to integrate AI into their security practices.
• Tech enthusiasts interested in the implications of Generative AI on security and privacy.

• AI practitioners and researchers looking to deepen their understanding of Generative AI security.
• Cybersecurity professionals aiming to integrate AI into their security practices.
• Tech enthusiasts interested in the implications of Generative AI on security and privacy.

Course Outline:

Module 1: Introduction to Generative AI

• Explore the fundamental principles of Generative AI (GANs, VAEs, etc.)
• Understand the different types of Gen AI models and their capabilities.
• Discuss the various applications of Gen AI across industries.

Module 2: Security Landscape of Gen AI

• Identify potential security threats associated with Gen AI, such as deep-fakes, synthetic data poisoning, and adversarial attacks.
• Learn about techniques used to manipulate and exploit Gen AI models for malicious purposes.
• Analyse the impact of Gen AI on data privacy and security.

Module 3: Mitigating Gen AI Security Risks

• Explore best practices for securing Gen AI development pipelines.
• Discuss techniques for data sanitization and adversarial training to improve model robustness.
• Analyze methods for detecting and preventing Gen AI-based attacks.
• Learn about responsible AI development principles for secure Gen AI applications.

Module 4: Case Studies and Applications

• Analyze real-world case studies of Gen AI security breaches and vulnerabilities.
• Explore how organizations can leverage Gen AI for security applications like anomaly detection and threat analysis.
• Discuss the future of Gen AI security and emerging solutions.