Security Risks in AI and Machine Learning: Categorizing Attacks and Failure Modes (2025)
Security Risks in AI and Machine Learning: Categorizing Attacks and Failure Modes
Released: 11/2025
Duration: 1h 46m 33s | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 215.88 MB
Genre: eLearning | Language: English
Released: 11/2025
Duration: 1h 46m 33s | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 215.88 MB
Genre: eLearning | Language: English
Like any software or process, machine learning (ML) is vulnerable to attack. In order to protect something, you must first understand where and how a system is vulnerable. In this course, Diana Kelley shows experienced threat modelers the ways that ML shifts the focus based on potential impact and from the vast amount of data that ML systems need to fuel their operation. Diana shows how ML can fail in a number of ways when under attack from adversaries and how design flaws can also lead to operational failure, data leakage, and other security and privacy risks.
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