An overview of ML attacks and countermeasures
The development and deployment of robust ML systems demand a comprehensive approach that covers various aspects of the technology. To ensure the security, reliability, and ethical use of ML models, organizations must focus on four key categories – model and data security, privacy and compliance, attack prevention and monitoring, and ethics and responsible AI. By addressing these categories holistically, organizations can mitigate risks, maintain user trust, and ensure the successful integration of AI technologies into their operations.
In the realm of robust ML, model and data security is of paramount importance. Implementing strong authentication methods ensures only authorized users access ML systems, while using interpretable, fair, or private models bolsters transparency and fairness. Thorough model documentation preserves knowledge and enables seamless transfer, whereas model management combines process and technology controls...
