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BrewSLM Blog | March 17, 2026

Fine-Tuning vs. RAG For Security And Governance

Security review often changes architecture decisions. Fine-tuning and RAG expose different control points, so governance programs need architecture-specific policies.

Primary keyword: fine-tuning vs. RAG

Secondary keywords: AI governance controls, secure model deployment, retrieval security, compliance for LLM systems, policy gating

Security and governance comparison for fine-tuning versus RAG

Map data residency and retention exposure

Fine-tuning pipelines centralize sensitive data during adaptation phases, while RAG pipelines distribute exposure across indexes and retrieval layers. Both can be secure, but controls must match data flow shape. Start with data lineage before selecting controls.

Design least-privilege access by layer

Model registries, retrieval stores, and runtime endpoints should have separate permission boundaries. Avoid broad shared credentials across training and serving planes. Layered access reduces blast radius during operational mistakes.

Auditability requirements differ by architecture

RAG requires retrieval traceability and source provenance logging. Fine-tuning requires dataset version lineage and model promotion evidence. Governance should demand proof artifacts relevant to each path, not generic checklists.

Gate promotions with policy evidence

Promotion should require explicit evidence packs: quality metrics, security checks, and change approvals. Make gate decisions machine-readable so audits are reproducible. Governance is strongest when it is operationalized in the release flow.

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