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 take the proper precautions to ensure your ML projects don’t provide a back door to your data.
This blog gives an overview of machine learning security risks, highlighting the key threats and challenges. But it isn’t all doom and gloom; we’ll also explain best practices and explore possible solutions, including the role of open source.
The machine learning attack surface
Every technology is subject to security concerns, but the challenge is even greater with machine learning because of the lack of talent and the innovative applications of AI. Some of the security factors include:
- Data security: ML projects need a lot of data, which often includes…
