Small-Language Models for Privacy-Aware Institutional Knowledge Support

Adrian Keller

Small-Language Models for Privacy-Aware Institutional Knowledge Support

Author(s)	Dr. Adrian Keller
Country	United States
Abstract	Institutional knowledge is increasingly distributed across policy manuals, internal reports, project repositories, standard operating procedures, meeting records, technical documentation, research files, email archives, knowledge bases, and other digitally stored resources. Generative artificial intelligence can make such information easier to retrieve and interpret, but conventional cloud-centered language-model deployments may create privacy, confidentiality, access-control, and governance concerns when sensitive institutional information is transmitted to or processed by external systems. Small-language models offer an alternative architectural direction because their reduced computational requirements can support local, edge, or institution-controlled deployment for narrowly defined knowledge tasks. Smaller model size, however, does not itself guarantee privacy, factual reliability, or secure institutional use. This study develops a Privacy-Aware Institutional Knowledge Support Framework (PIKSF) combining small-language models with retrieval-augmented generation, access-aware retrieval, data minimization, local inference, source attribution, human oversight, and continuous security governance. Because no real institutional deployment dataset was provided, the study adopts an explicitly simulation-based methodology. Four hypothetical architectures are compared: cloud large-language-model processing with raw context, hosted language models with filtered retrieval, local small-language models with governed retrieval, and privacy-aware small-language models with controlled institutional knowledge. Six dimensions—data exposure control, authorization alignment, retrieval grounding, response reliability, operational efficiency, and governance observability—are incorporated into a Privacy-Aware Knowledge Support Index. The simulated score rises from 42 for externally processed raw-context systems to 92 for privacy-aware small-model architectures with controlled knowledge retrieval. The analysis indicates that the principal privacy advantage of small-language models is architectural rather than intrinsic: institutions can reduce unnecessary information movement, preserve stronger control over inference, and separate model reasoning from authoritative institutional knowledge. The study concludes that small-language models are especially promising for bounded institutional knowledge-support tasks when combined with retrieval governance, source-level permissions, secure deployment, documented evaluation, and human accountability.
Keywords	small-language models, institutional knowledge, privacy-aware AI, retrieval-augmented generation, local inference, enterprise knowledge management, data minimization, responsible artificial intelligence
Field	Engineering
Published In	Volume 3, Issue 1, January-February 2022
Published On	2022-01-17

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American Journal of Advanced Multidisciplinary Innovation and Research

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Small-Language Models for Privacy-Aware Institutional Knowledge Support

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