Secure Model Development Life Cycle (SMDLC)

Understand why SMDLC is important and learn to embed security into all stages of the MDLC.

Improve model robustness

“Increasingly, AI/ML models are becoming core to enterprise systems and the cybersecurity of those models has become a priority for the safe and secure deployment of responsible AI”, says James Stewart, founder and CEO of TrojAI, “and we’ve realized that our clients needed a comprehensive framework to assist them in their efforts to deploy responsible and trusted AI.”

Executive Summary

This document justifies the need of security and robustness as a core function within the AI/ML model development lifecycle and provides a comprehensive discussion on how to effectively build and develop secure machine learning models.
Companies must evolve their current model development activities to not just solve a business problem but to do so in a way that mitigates risk and is robust, explainable and responsible. Regulatory requirements for the deployment of responsible AI are imminent, with strict penalties for those that do not adhere. We can see the direction in which the AI/ML landscape is evolving; organizations that are proactive in taking the necessary next steps towards robustness and security now will be vastly more prepared when regulations are established.

Robustness and Security of AI

Current AI systems, such as those used for object detection and classification, have different kinds of failure - characterized as rates of false positives and false negatives. They are often brittle when operating outside of lab environments at the edges of their performance boundaries, which are difficult to anticipate. AI models are also vulnerable to adversarial attack by malicious actors and can exhibit unwanted bias in operation. –National Security Commission on Artificial Intelligence

Download SMDLC