AI Product Security: A Primer for Developers
Ebenezer R. H. P. Isaac, Jim Reno
TL;DR
This paper provides an overview of AI product security, emphasizing the importance of understanding threats and best practices for developers to ensure secure AI systems.
Contribution
It offers a primer for developers on AI security threats, pitfalls, and best practices in AI product development, highlighting the shift from AI for security to security of AI.
Findings
Identifies key security threats to AI products
Highlights common pitfalls in AI development
Provides guidelines for secure AI product design
Abstract
Not too long ago, AI security used to mean the research and practice of how AI can empower cybersecurity, that is, AI for security. Ever since Ian Goodfellow and his team popularized adversarial attacks on machine learning, security for AI became an important concern and also part of AI security. It is imperative to understand the threats to machine learning products and avoid common pitfalls in AI product development. This article is addressed to developers, designers, managers and researchers of AI software products.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdversarial Robustness in Machine Learning · Ethics and Social Impacts of AI