Securing the AI Software Supply Chain
Abstract
As AI-powered features gain traction in software applications, we see many of the same problems we’ve faced with traditional software—but at an accelerated pace. The threat landscape continues to expand as AI is further integrated into everyday products, so we can expect more attacks. Given the expense of building models, there is a clear need for supply chain solutions.
This paper explains our approach to securing our AI supply chain using provenance information and provides guidance for other organizations. Although there are differences between traditional and AI development processes and risks, we can build on our work over the past decade using Binary Authorization for Borg (BAB), Supply-chain Levels for Software Artifacts (SLSA), and next-generation cryptographic signing solutions via Sigstore, and adapt these to the AI supply chain without reinventing the wheel. Depending on internal processes and platforms, each organization’s approach to AI supply chain security will look different, but the focus should be on areas where it can be improved in a relatively short time.
Readers should note that the first part of this paper provides a broad overview of “Development lifecycles for traditional and AI software”. Then we delve specifically into AI supply chain risks, and explain our approach to securing our AI supply chain using provenance information. More advanced practitioners may prefer to go directly to the sections on “AI supply chain risks,” “Controls for AI supply chain security,” or even the “Guidance for practitioners” section at the end of the paper, which can be adapted to the needs of any organization.
This paper explains our approach to securing our AI supply chain using provenance information and provides guidance for other organizations. Although there are differences between traditional and AI development processes and risks, we can build on our work over the past decade using Binary Authorization for Borg (BAB), Supply-chain Levels for Software Artifacts (SLSA), and next-generation cryptographic signing solutions via Sigstore, and adapt these to the AI supply chain without reinventing the wheel. Depending on internal processes and platforms, each organization’s approach to AI supply chain security will look different, but the focus should be on areas where it can be improved in a relatively short time.
Readers should note that the first part of this paper provides a broad overview of “Development lifecycles for traditional and AI software”. Then we delve specifically into AI supply chain risks, and explain our approach to securing our AI supply chain using provenance information. More advanced practitioners may prefer to go directly to the sections on “AI supply chain risks,” “Controls for AI supply chain security,” or even the “Guidance for practitioners” section at the end of the paper, which can be adapted to the needs of any organization.
Research Areas
