Published: 08 May 2023
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.
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.
Data and analytics (D&A) professionals responsible for
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