Best practices for bolstering machine learning security

MachineLearning security
MachineLearning security

Nearly 75% of the world’s largest companies have already integrated AI and machine learning (ML) into their business strategies. As more and more companies — and their customers — gain increasing value from ML applications, organizations should be considering new security best practices to keep pace with the evolving technology landscape.

Companies that utilize dynamic or high-speed transactional data to build, train, or serve ML models today have an important opportunity to ensure their ML applications operate securely and as intended. A well-managed approach that takes into account a range of ML security considerations can detect, prevent, and mitigate potential threats while ensuring ML continues to deliver on its transformational potential.

Machine learning security is business critical 

ML security has the same goal as all cybersecurity measures: reducing the risk of sensitive data being exposed. If a bad actor interferes with your ML model or the data it uses, that model may output incorrect results that, at best, undermine the benefits of ML and, at worst, negatively impact your business or customers.

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