Infrastructure-as-code (IaC) is reshaping how cloud resources are managed. IaC users write high-level programs to define their desired infrastructure, and the underlying IaC platforms automatically deploy the constituent resources into the cloud. While proven powerful at creating greenfield deployments (i.e., new cloud deployments from scratch), existing IaC platforms provide limited support for managing brownfield infrastructure (i.e., transplanting an existing non-IaC deployment to an IaC platform). This hampers the migration from legacy cloud management approaches to an IaC workflow and hinders wider IaC adoption. Managing brownfield deployments requires techniques to lift low-level cloud resource states and translate them into corresponding IaC programs —— the reversal of the regular deployment process. Existing tools rely heavily on rule-based reverse engineering, which suffers from the lack of automation, limited resource coverage, and prevalence of errors. In this work, we lay out a vision for Lilac, a new approach that frees IaC lifting from extensive manual engineering. Lilac brings the best of both worlds: leveraging Large Language Models to automate lifting rule extraction, coupled with symbolic methods to control the cloud environment and provide correctness assurance. We envision that Lilac would enable the construction of an automated and provider-agnostic lifting tool with high coverage and accuracy.