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AI Security Engineering - Modeling - Detecting - Mitigating New Vulnerabilities

RSA Conference via YouTube

Overview

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7000+ certificate courses from Google, Microsoft, IBM, and many more.
This course focuses on AI Security Engineering, covering the modeling, detecting, and mitigating of new vulnerabilities. The learning outcomes include understanding how to protect and defend services against AI and machine learning security threats, detecting threats as a data scientist, and triaging as a first responder. The course teaches skills such as identifying adversarial attacks, classifying attacks, and implementing proactive defenses. The teaching method involves in-depth exploration of research and guidance on security issues. The intended audience includes security engineers, data scientists, and first responders looking to enhance their knowledge and skills in AI security.

Syllabus

Intro
Customer Compromise via Adversarial ML-Case Study
Higher Order Bias/Fairness, Physical Safety & Reliability concerns stem from unmitigated Security and Privacy Threats
Adversarial Audio Examples
Failure Modes in Machine Learning
Adversarial Attack Classification
Data Poisoning: Attacking Model Availability
Data Poisoning: Attacking Model Integrity
Poisoning Model Integrity: Attack Example
Proactive Defenses
Threat Taxonomy
Adversarial Goals
A Race Between Attacks and Defenses
Ideal Provable Defense
Build upon the Details: Security Best Practices
Define lower/upper bounds of data input and output
Threat Modeling Al/ML Systems and Dependencies
Wrapping Up
AI/ML Pivots to the SDL Bug Bar

Taught by

RSA Conference

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