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.
“Executives should care about this because there’s nothing worse than doing the wrong thing very quickly and confidently,” says Zach Hanif, vice president of machine learning platforms at Capital One. And while Hanif works in a regulated industry—financial services—requiring additional levels of governance and security, he says that every business adopting ML should take the opportunity to examine its security practices.
Devon Rollins, vice president of cyber engineering and machine learning at Capital One, adds, “Securing business-critical applications requires a level of differentiated protection. It’s safe to assume many deployments of ML tools at scale are critical given the role they play for the business and how they directly impact outcomes for users.”
Continue reading: https://www.technologyreview.com/2022/11/14/1062881/best-practices-for-bolstering-machine-learning-security/
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.
“Executives should care about this because there’s nothing worse than doing the wrong thing very quickly and confidently,” says Zach Hanif, vice president of machine learning platforms at Capital One. And while Hanif works in a regulated industry—financial services—requiring additional levels of governance and security, he says that every business adopting ML should take the opportunity to examine its security practices.
Devon Rollins, vice president of cyber engineering and machine learning at Capital One, adds, “Securing business-critical applications requires a level of differentiated protection. It’s safe to assume many deployments of ML tools at scale are critical given the role they play for the business and how they directly impact outcomes for users.”
Continue reading: https://www.technologyreview.com/2022/11/14/1062881/best-practices-for-bolstering-machine-learning-security/
