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Generative AI Security

Course Summary

The Generative AI Security course provides participants with the skills needed to navigate the unique security challenges of Generative AI. The course is intended for professionals interested in enhancing their understanding of Gen AI security and ensuring the safe deployment of AI models, while staying ahead of emerging threats in the rapidly evolving AI landscape.

Prerequisites
In order to succeed in this course, you will need:

  • Basic understanding of AI/ML concepts.
  • Familiarity with cybersecurity principles.
  • Some experience with programming (e.g., Python) is beneficial but not required.
Purpose
Understand what is needed to ensure the safe deployment of AI models.
Audience
Professionals wanting to understand the safe deployment of AI models
Role
Software Developers | Data Engineers | Cybersecurity Engineers | Technical Managers
Skill level
Beginner
Style
Lecture | Hands-on Activities | Group Discussions
Duration
1 day
Related technologies
Python | Artificial Intelligence

 

Course objectives
  • Explore the specific security risks of Gen AI
  • Learn how to manage and anonymize training data, implement differential privacy and protect sensitive information
  • Understand defense practices against adversarial attacks

What you'll learn:

In this Generative AI Security course, you'll learn:

Understanding Generative AI Risks

  • Overview of GenAI capabilities (e.g., GPT, diffusion models)
  • Security risks in GenAI
    • Model theft and reverse engineering
    • Data leakage through prompts and outputs
    • Adversarial attacks and model manipulation
  • Misinformation and deep fakes

Data Security and Privacy in GenAI

  • Managing and anonymizing training data.
  • Understanding privacy risks in generative models
  • Differential privacy techniques.
  • Role of encryption and secure storage in AI pipelines.

Adversarial Attacks and Defense Strategies

  • Overview of adversarial attacks (e.g., input manipulation, prompt injection)
  • Defending against poisoning and evasion attacks.
  • Robustness testing and validation.
  • Use of adversarial training techniques.

Building Secure GenAI Applications

  • Secure integration of GenAI models into applications.
  • Implementing secure APIs and user authentication.
  • Role of cloud security in GenAI

Generative AI Security

real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
fully customized

Tailored Outlines

Once-size-fits-all doesn't apply to training teams. That's where we come in!

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