GSA seeks public comment on draft regulation for safeguarding data in LLM acquisitions
The General Services Administration proposes a new acquisition clause to address data safeguarding in federal purchases of large language model AI systems, opening a 60-day comment period.
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- The GSA published a draft GSAR clause aimed at basic safeguarding of data within Large Language Model AI systems for federal acquisitions.
- The agency is soliciting public feedback via a 60-day comment period before proceeding with formal rulemaking.
- The move follows broader federal efforts to establish safeguards for AI technologies procured by government agencies.
The General Services Administration (GSA) published a draft clause for the General Services Acquisition Regulation (GSAR) focused on basic safeguarding of data within Large Language Model Artificial Intelligence Systems (LLMs). The draft targets federal acquisitions of LLM technologies, aiming to establish baseline security and privacy requirements during procurement processes.
The agency announced a 60-day public comment period to gather stakeholder feedback before advancing to potential deviation or formal rulemaking. The notice emphasizes the complexity of addressing data safeguarding in AI systems and seeks input from industry, academia, and other interested parties to refine the proposed requirements.
This action aligns with broader federal initiatives to develop safeguards for AI technologies procured by government agencies. The draft clause reflects an early-stage regulatory effort to integrate AI-specific considerations into federal acquisition practices, particularly concerning data protection and secure handling of sensitive information within LLM deployments.
The Federal Register notice does not specify the exact safeguards or technical controls included in the draft clause, nor does it provide a timeline for finalization beyond the open comment period. The GSA’s approach suggests a phased development process, with further revisions expected based on public input and evolving AI risk management frameworks.
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