AI Security Essentials BY

Instructor-led Introductory

Training highlights 1. Understanding how AI is built Intended to make sense of underlying algorithms and their use cases. The model-building demos will strengthen the intuition behind the algorithms and prepare us to be introduced to AI Security vulnerabilities.

• Foundations of AI, Basic terminologies, techniques, and introduction to frameworks to secure AI learning.

• Building Neural Networks.

• Training the models: Optimization algorithms.

• Computer Vision: Object detection, Image classification, Face recognition, etc.

• GenAI application: Understanding how LLMs work.

• Building RAG and Agentic AI applications.

• Deployment: MLOps, SaaS, AWS/Azure/GCP etc.

2. Understanding AI Security

This section intends to explore potential loopholes in AI applications. With an actionable approach to detect and mitigate the vulnerabilities.

• Adversarial Learning Attack: Fooling the neural network models & A adversarial patches - attacks in physical domain.

• Bypassing face recognition systems.

• Model Extraction Attack: Stealing models deployed on the cloud and on users and devices.

• Model Skewing and Data poisoning: Hacking our way through feedback loops to control the information that trains the AI models.

The process of understanding how something breaks gets easier when you know how to build it. So, this AI Security training starts by getting the participants familiar with AI fundamentals. Then we start our journey that ventures deep into the trenches of AI Security Landscape.

Recommended For • Cyber Security professionals responsible for the security of AI systems.

• Developers looking to design, implement, and maintain secure AI applications.

• AI enthusiasts and professionals.

• Students with a Computer Science background and a taste for AI and Infosec.

H A C K I N G P O I N T

Single Seat

• Prompt injection and Jailbreak attacks: Knowing how an attacker can own the LLMs and get them to do or predict whatever the attacker wants.

• Attacking the RAG and Agentic AI applications.

• Understanding what can go wrong with MCP tools.

• Insecure serialization: Understanding how malicious actors can inject executable code into publicly distributed ML models.

• Exploring existing security frameworks for AI: MITRE and OWASP.

Outcomes & Impact • Understanding the fundamentals of AI development.

• Intuitive understanding of AI algorithms.

• Specially crafted labs for ML and Infosec enthusiasts.

• Actionable insights on AI vulnerabilities and how to mitigate them.

Purchase & Redemption Options AI Security Essentials is available via:

• IGS Credits

• Check Point COOP funds

• Credit Card

H A C K I N G P O I N T

Get Started Today Click here to enroll in AI Security Essentials by Payatu.

https://protect.checkpoint.com/v2/r02/___https://igs.checkpoint.com/courses/3123___.YzJlOmNwYWxsOmM6bzo4MWI5OTBiNmM3MjIxNDU2NTkxZDUxYTI3ZWE1ZTQ1YTo3OjU0NDQ6NDM2NmQ4ZmMyYTY3ODQ0NjNjZmIxZDc2NzNkNDZkNDY2NGMyOTU2MGE4OTRjMDhhMjVkYjI5MmIxZTAxOGUwZDpwOlQ6Tg