Data is at the heart of all machine learning (ML) initiatives – and bad actors know it. As AI continues to occupy the limelight of modern tech discourse, ML systems are becoming increasingly attractive targets for attack. With the Identity Theft Resource Center reporting a 72% spike in data breaches in 2023, it’s critical to  […]

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This post provides an overview of machine learning security risks and highlights key threats and challenges. It covers topics such as data security, tools security, ML model security, hardware security, end device security, and the human factor. The post also discusses the top four machine learning security risks: package security vulnerabilities, data poisoning, adversarial attacks, and data privacy. It concludes by providing best practices to improve machine learning security and mentions some open-source AI solutions.

An overview of machine learning security risks

The top four machine learning security risks

Best practices to improve your machine learning security