TrustLedger: A Financial Risk Quantification Framework for AI-Augmented Software Supply Chains Using the FinOps Model

The proliferation of AI-assisted coding tools, including GitHub Copilot, Cursor, and ChatGPT, has fundamentally altered the software dependency intake process. Engineers now introduce third-party packages at rates three to five times higher than pre-AI baselines, yet organizational security review processes have not scaled commensurately. Existing software composition analysis tools output CVSS severity labels and CVE identifiers but provide no financial quantification of the introduced risk. Finance and operations leadership cannot allocate remediation budget without a dollar figure attached to the exposure. This paper introduces TrustLedger, a conceptual framework that applies the Linux Foundation FinOps discipline to software supply chain security. Seven original intellectual contributions are presented: (1) the Dependency Financial Risk Pricing Formula, which converts any CVE into a dollar-denominated financial risk value at the dependency level; (2) the Pipeline Stage Cost Multiplier Model, an exponential cost growth model calibrated to incident response data; (3) the AI Origin Detection Algorithm, a weighted four-signal classifier for attributing dependency origin to AI coding tools; (4) the Risk Amplification Ratio, a new board-level KPI relating AI tool spend to introduced security liability; (5) the Pipeline Health Score Algorithm, a composite 0–100 index analogous to a credit rating for a software pipeline; (6) the Drift Detection and Forecasting Model, which applies time-series analysis to dependency accumulation; and (7) the Shift-Left ROI Model, which produces a per-sprint financial return on remediation investment. The framework is illustrated through six synthetic case studies spanning diverse organizational profiles. Prior art review conducted in May 2026 found no pre-existing work for any of the seven contributions. All seven are candidates for US utility patent protection under 35 U.S.C. §101. U.S. Copyright Registration No. 1-15159350671.