Gartner Research

How to Securely Design and Operate Machine Learning

Published: 08 May 2023

Summary

Machine learning (ML) introduces new security risks that target machine learning infrastructure, data and models. Data and analytics technical professionals must use a risk-based approach toward ML security and apply actionable security controls to mitigate these risks.

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Overview

Key Findings
  • ML adversarial attacks are starting to emerge. Some 27% of organizations that have experienced an AI privacy breach or security incident also indicated that the breach/incident involved intentional malicious attacks on the organization’s AI infrastructure.

  • ML security is a shared responsibility among teams within the organization (such as D&A and security) because many security controls are cross-departmental.

  • The vendor landscape of commercial ML security protection solutions is immature and nascent. However, startups are emerging.

  • ML security should be taken into account during the design, implementation and operation of ML systems and not as an afterthought.

Recommendations

Data and analytics (D&A) professionals responsible for

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Analysts:

Wilco van Ginkel

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