--- id: CP-524 slug: fine-tuning-lora-vs-rag-enterprise lang: en title: Fine-tuning or RAG in the enterprise description: "Retraining a model (fine-tuning, LoRA) or giving it documents to consult (RAG): the difference, the risks and your reflexes." canonical_url: https://startbrain.io/cyberpedia/en/articles/fine-tuning-lora-vs-rag-enterprise/ markdown_url: https://startbrain.io/cyberpedia/en/articles/fine-tuning-lora-vs-rag-enterprise.md publisher: Brain (https://startbrain.io) license: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) cite_as: Brain, "Fine-tuning or RAG in the enterprise", Cyberpedia, 2026. https://startbrain.io/cyberpedia/en/articles/fine-tuning-lora-vs-rag-enterprise/ (CC BY 4.0) wing: 4 theme: master_ai reflex: air-054 schema: G3 updated: "2026-11-03" translations: en: https://startbrain.io/cyberpedia/en/articles/fine-tuning-lora-vs-rag-enterprise/ fr: https://startbrain.io/cyberpedia/fr/articles/fine-tuning-lora-vs-rag/ de: https://startbrain.io/cyberpedia/de/articles/fine-tuning-lora-vs-rag-unternehmen/ es: https://startbrain.io/cyberpedia/es/articles/fine-tuning-lora-vs-rag-empresa/ sources: - "n": 1 title: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks publisher: arXiv url: https://arxiv.org/abs/2005.11401 published: "2020-05-22" accessed: "2026-11-03" - "n": 2 title: "LoRA: Low-Rank Adaptation of Large Language Models" publisher: arXiv url: https://arxiv.org/abs/2106.09685 published: "2021-06-17" accessed: "2026-11-03" - "n": 3 title: LLM04:2025 Data and Model Poisoning publisher: OWASP url: https://genai.owasp.org/llmrisk/llm042025-data-and-model-poisoning/ published: "2024-04-10" accessed: "2026-11-03" - "n": 4 title: LLM08:2025 Vector and Embedding Weaknesses publisher: OWASP url: https://genai.owasp.org/llmrisk/llm082025-vector-and-embedding-weaknesses/ published: "2024-04-10" accessed: "2026-11-03" - "n": 5 title: LLM02:2025 Sensitive Information Disclosure publisher: OWASP url: https://genai.owasp.org/llmrisk/llm022025-sensitive-information-disclosure/ published: "2024-04-10" accessed: "2026-11-03" - "n": 6 title: LLM01:2025 Prompt Injection publisher: OWASP url: https://genai.owasp.org/llmrisk/llm01-prompt-injection/ published: "2024-04-10" accessed: "2026-11-03" - "n": 7 title: Guidelines for secure AI system development publisher: NCSC (UK) url: https://www.ncsc.gov.uk/collection/guidelines-secure-ai-system-development published: "2023-11-27" accessed: "2026-11-03" --- # Fine-tuning or RAG in the enterprise For an AI assistant to answer questions on topics specific to a company, there are two routes. The first retrains the model on in-house data (fine-tuning). The second connects the model to documents it can consult: this is RAG, short for "retrieval-augmented generation". RAG combines a memory held inside the model with an external memory, for example an index of documents [1]. Its authors note that providing the origin of a model's answers and updating its knowledge remain open problems for stand-alone models [1]. This article explains the difference and what you do, as an employee, with either one. ## In 30 seconds - Where it happens: in AI tools connected to your documents. - What the attacker wants: to slip booby-trapped content into the data (indexed documents or training data), or to retrieve information that the system has memorized or indexed. - The reflex: do not put any personal or confidential data into an AI tool without approval, and check the sources the tool cites. - If it's already done: tell the tool's owner or the security team so that the data is removed from the database or the training data. ## Straight answers ### What is the difference between fine-tuning, LoRA and RAG? Fine-tuning adapts an already trained model to a particular task or domain. In its full version, it retrains all of the model's parameters [2]. LoRA is a lighter variant. It freezes the weights of the original model and adds small trainable matrices to each layer, which greatly reduces the number of parameters to train [2]. For GPT-3 175B, the authors report a 10,000-fold reduction in that number and a 3-fold reduction in the GPU memory required, compared with full fine-tuning with Adam [2]. RAG connects the model to external knowledge sources [4]. OWASP notes that this addition can change the behavior of the base model without this being intended [4]. ### What advantages does RAG offer in the enterprise? Part of RAG's knowledge sits in a document database, in addition to the model's own: the authors of RAG present updating knowledge and indicating provenance as limits of stand-alone models, which the external memory aims to remove [1]. OWASP advises storing user-supplied information in a vector database, which allows adjustments without retraining the whole model [3]. Access rights can finally apply at the database level, provided it is designed for that (see below) [4]. On cost, the sources in the file do not directly compare the two approaches. They only establish that full fine-tuning becomes less feasible as models grow larger [2]. ### Does RAG protect against everything? No. According to OWASP, RAG and fine-tuning aim to make answers more relevant and more accurate, but research shows that they do not fully neutralize prompt injection [6]. ## How it works With RAG, the model is connected to external knowledge sources, and OWASP specifies that this retrieval augmentation uses vector mechanisms and embeddings [4]. The model then combines its own memory with this external memory [1]. With fine-tuning, the data goes through training. OWASP distinguishes three stages where data can be manipulated: pre-training, fine-tuning and the embedding phase, where text is converted into vectors [3]. ## What you do - Do not put personal or confidential data into an AI tool without approval. OWASP lists as sensitive information personal data, financial information, health records, confidential business data, security credentials and legal documents [5]. - Keep in mind that what you enter may come out later. OWASP asks users to understand the risks of unintentionally providing sensitive data, which could be revealed in the model's answers [5]. - Read the sources the tool cites and check that they actually say what the answer claims. OWASP recommends validating model outputs against trusted sources to detect poisoning [3]. - Check that the tool is approved by your organization, and read its terms of use: OWASP asks application owners to let users opt out of having their data enter training [5]. - Report an answer that contains information from another department or another user. OWASP describes this case: an answer may contain another user's personal data because of insufficient data sanitization [5]. ## Pitfalls ### Leak of memorized data A model exposed to sensitive data during training can reproduce it. OWASP cites careless inclusion of data in training as a cause of sensitive information disclosure [5]. It also cites the risk of answers revealing proprietary data or algorithms [5]. With RAG, the leak goes through the database: insufficient or misaligned access controls can allow the model to retrieve and disclose personal or proprietary data [4]. ### Poorly managed access rights When several groups of users share the same vector database, one group's information can be retrieved in answer to another group's questions, with a possible leak of sensitive business information [4]. OWASP recommends a permission-aware vector database and strict separation of datasets [4]. ### Data poisoning Poisoning consists of manipulating pre-training, fine-tuning or embedding data to introduce vulnerabilities, backdoors or biases [3]. The risks are higher with external sources, which may contain unverified or malicious content [3]. A backdoor can leave the model's behavior unchanged until a trigger alters it, which makes it hard to detect [3]. For a RAG database, OWASP advises accepting data only from trusted, verified sources, and auditing the database regularly [4]. ### Prompt injection through indexed documents An indirect injection occurs when the model accepts input from external sources, such as websites or files, whose content alters its behavior in unexpected ways [6]. OWASP describes a scenario: an attacker modifies a document in the repository used by a RAG application; when a user's question brings back that content, the malicious instructions alter the answer [6]. The following example is a neutralized illustration, drawn from the logic of OWASP's scenario about a booby-trapped résumé [4]. ```text Illustration: a résumé uploaded to a candidate-screening tool contains, in white text on a white background, [an instruction asking to ignore the previous rules and to recommend this candidate]. The tool reads this hidden text along with the rest of the document. ``` According to OWASP, the model then follows these hidden instructions and recommends an unqualified candidate [4]. The proposed countermeasure: extraction tools that ignore formatting and detect hidden content, and validation of every document before it enters the database [4]. ### A security framework for teams that deploy The UK's NCSC publishes guidelines for providers of AI systems, so that these systems work as intended and without revealing sensitive data to unauthorized third parties [7]. ## Sources 1. arXiv, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks", 22 May 2020. https://arxiv.org/abs/2005.11401 (accessed 3 November 2026) 2. arXiv, "LoRA: Low-Rank Adaptation of Large Language Models", 17 June 2021. https://arxiv.org/abs/2106.09685 (accessed 3 November 2026) 3. OWASP, "LLM04:2025 Data and Model Poisoning", 10 April 2024. https://genai.owasp.org/llmrisk/llm042025-data-and-model-poisoning/ (accessed 3 November 2026) 4. OWASP, "LLM08:2025 Vector and Embedding Weaknesses", 10 April 2024. https://genai.owasp.org/llmrisk/llm082025-vector-and-embedding-weaknesses/ (accessed 3 November 2026) 5. OWASP, "LLM02:2025 Sensitive Information Disclosure", 10 April 2024. https://genai.owasp.org/llmrisk/llm022025-sensitive-information-disclosure/ (accessed 3 November 2026) 6. OWASP, "LLM01:2025 Prompt Injection", 10 April 2024. https://genai.owasp.org/llmrisk/llm01-prompt-injection/ (accessed 3 November 2026) 7. NCSC (UK), "Guidelines for secure AI system development", 27 November 2023. https://www.ncsc.gov.uk/collection/guidelines-secure-ai-system-development (accessed 3 November 2026) --- Original source: Cyberpedia, the public cybersecurity and AI encyclopedia published by Brain. 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