How to Secure Data on RAG Architectures? Roadmap for Enterprises

How do you ensure data security in enterprise AI applications? From RAG architectures to LLMs, from KVKK compliance to anonymization processes, discover all the risks and Skymod's native solutions in this guide.

AI-powered assistants and intelligent chatbots are enabling organizations to access information transforming their processes. Especially large language models Large Language Models (LLM) and the Retrieval-Augmented Generation RAG architecture are preferred to increase employee productivity and access the right information quickly. However, this transformation brings with it a critical responsibility: ensuring data security. Every model and system used in enterprise AI applications also interacts with sensitive data. Therefore, technology selection should not only focus on performance, but also on transparency of data processing, legal compliance and local security architecture. Especially in markets like Turkey where data Localization is a priority, this issue is of strategic importance in terms of business continuity and legal responsibility.

What are the Main Security Risks in Large Language Models?

Data security is no longer solely on the agenda of IT teams; today, it stands as one of the most critical components of corporate trust, reputation, and sustainability. This is because employees process customer information, financial records, internal reports, and strategic documents with artificial intelligence systems daily. Ensuring that this data is accessible only by authorized individuals has become both a legal imperative and a matter directly impacting the institution’s credibility.

In today’s environment, regulatory compliance is not just about avoidingn penalties; it is also crucial for maintaining the trust relationship established with customers. Regulations such as GDPR and KVKK provide a framework for data protection, but the real difference lies in the sincerity of adherence to these rules. Customers are no longer just looking for good products and services; they also want to believe that their data is genuinely protected.

At this juncture, a robust data security policy is not merely a technical measure but a guarantee for the institution’s reputation. Proactive measures taken before crises occur prevent both financial losses and solidify long-term trust in the brand. Conversely, a data breach can lead not only to financial losses but also to the erosion of customer relationships, lawsuits, and the undermining of the reputation built over years

What are the Main Security Risks in Large Language Models?

Large Language Models have become powerful tools for understanding natural language and generating responses. However, when these models interact with corporate data, the potential technological benefits are accompanied by significant security risks. At this point, a general understanding of how these models function and the key considerations has become mandatory for both technical teams and management.

One of the most prevalent risks is the unintentional transmission of sensitive information to the model. Employees within the organization can unknowingly input customer data, financial spreadsheets, or excerpts from confidential documents into conversational interfaces. When such content is sent to external services, the relevant data can leave the organization’s perimeter. Moreover, some AI services may process and even temporarily store this data. This situation is not merely a technical issue; it also represents regulatory non-compliance, erosion of customer trust, and significant financial exposure.

Skymod’s data anonymization and control layer is designed to prevent such inadvertent data leaks. Every user query is scanned by specifically trained algorithms before reaching the system; sensitive elements such as names, identifiers, and customer information are identified and automatically masked. This ensures that only anonymized and secure content is shared with external APIs.

Another critical concern is the tendency of models to occasionally generate incorrect or fabricated information, a phenomenon known as “model hallucination.” This can produce highly convincing but erroneous results. Content such as non-existent legal statutes, incorrect pricing details, or statements contradicting company policy can have severe consequences if presented directly to customers.
Skymod’s architecture addresses this risk as well. Responses from LLMs pass through control layers before reaching the user. The system evaluates the accuracy of sources and checks for sensitive data leakage in the responses. Furthermore, the format of the responses can be adjusted according to the organization’s policies.
A risk that should not be overlooked is the injection of incorrect data into RAG (Retrieval-Augmented Generation) systems, either internally or externally. Such data “poisoning,” whether intentional or accidental, can cause the model to make flawed decisions. For example, a manager relying on inaccurate information retrieved by the system might base a significant business decision on a faulty document, leading to serious losses.
Skymod takes precautions in this area too. The system records a digital fingerprint (hash) of all indexed documents, allowing for easy detection of subsequent content modifications. Simultaneously, the secure segmentation and vectorization of documents, without altering their context and meaning, are performed exclusively on servers located in Turkey. This protects both content integrity and prevents data egress to external sources.
Finally, it’s important to address the potential technical vulnerabilities that can arise when integrating with external LLM services. Unencrypted connections, inadequate API management, or weak key protection methods can create weaknesses in an organization’s data flow. Skymod’s end-to-end architecture, featuring TLS 1.3 encrypted data transfer, HSM-based key management, and isolated data environments for each customer, minimizes these types of risks.

Security Vulnerabilities Specific to RAG Systems

The Retrieval-Augmented Generation (RAG) architecture is rapidly being adopted to overcome the limitations of large language models and generate more accurate responses grounded in enterprise knowledge. RAG’s primary advantage lies in its ability to connect LLMs to internal resources such as corporate documents, databases, and knowledge bases, enabling the generation of current and organization-specific content. However, this powerful framework also introduces several sensitive components. Specifically, the embedding process, reranker models, and LLM APIs are key areas that require careful consideration from a data security perspective.

Vector Database and Embedding Vulnerabilities: In RAG systems, corporate documents are first segmented into smaller chunks, which are then converted into numerical vectors and stored in a vector database. This embedding process serves to represent the meaning of the content in a way that LLMs can understand. However, this transformation is not always a benign one. Research indicates that embeddings can be reverse-engineered to a form closely resembling the original text using specific techniques. This means that although vectors might not be considered raw data, they can contain sensitive content such as customer information, contract clauses, or financial details. Consequently, the security of the infrastructure and database where the embedding model operates becomes critical.

At this juncture, Skymod offers a two-pronged security solution:

Embedding operations are exclusively executed on private servers located within Turkey’s borders. This ensures that no text fragments leave the country before being converted into numerical form.

The resulting vectors are stored in a physically isolated and encrypted vector database. Furthermore, the corresponding document fragment for each vector is recorded with a hash-based digital fingerprint. This architecture enables the detection of unauthorized modifications to the content.

Risks Associated with Reranker APIs: Reranker models select the most relevant document fragments from those found by the search engine and present them to the LLM. While this layer is often overlooked, it represents a critical security checkpoint. This is because the texts sent to rerankers are frequently processed through APIs operating on external cloud services. The contextual snippets transmitted to these APIs contain the semantic content of corporate documents. If transparency regarding the reranker’s operating server is limited, it is unclear in which region this content is processed, how long it is stored, and who has access to it.

Skymod’s solution, however, brings this risky area entirely under corporate control:

Reranker models operate completely independently from LLMs and are hosted on local GPU servers. Since all ranking operations occur within a closed system located in Turkey, no data undergoes cross-border transfer. Additionally, Skymod’s observability layer logs reranker decisions, providing a transparent audit trail. This allows the system to explain why specific document fragments were selected.

Lack of Transparency and Control in LLM APIs: One of the most common risks encountered in enterprise AI projects is accessing LLM models via cloud-based APIs. While this structure is flexible and powerful, it introduces certain control challenges. Queries and contexts sent to these APIs are processed on external servers. The exact methods by which the API provider processes, stores, or integrates the data into the model are often opaque. Furthermore, in multi-tenant systems, the potential for commingling content from different companies can create vulnerabilities for data breaches.

Skymod’s hybrid security architecture offers a distinct advantage at this point: User queries and context are not sent to LLM APIs without first passing through Skymod’s proprietary anonymization layer. During this process, fields such as names, surnames, ID numbers, customer codes, and contract numbers are automatically identified and replaced with tokens, preserving semantic integrity while ensuring data privacy. These anonymized prompts are transmitted over TLS 1.3 encrypted connections only to API providers with whom a Data Processing Agreement (DPA) has been signed. Furthermore, every response received from the LLM undergoes a validation layer before being presented to the user. This validation checks the response’s format, meaningfulness, and potential data leakage risks.

How Can We Safely Use Artificial Intelligence? A Local and Safe Answer to the Question: Skymod

Today, for organizations working with large language models and RAG architectures, data security is not an abstract concern — it’s a very real and experienced set of risks. Scenarios such as the unintentional transfer of sensitive information to external systems, memory isolation issues, uncontrolled data sharing, and insider manipulation are now on the agenda of every sector.

The fundamental question organizations must answer at this point is: “How do we keep our data secure while integrating artificial intelligence into our business processes?”

This is precisely where Skymod provides the solution.Skymod not only makes AI technologies accessible to organizations but also offers a local and regulation-friendly infrastructure that ensures these systems are used securely.

Your data does not leave the country. Sensitive processes like embedding, vector databases, and rerankers operate entirely on servers within Turkey. This ensures both KVKK compliance and data sovereignty.

Thanks to the anonymization layer, sensitive information in user queries is automatically processed through masking algorithms before being handled. This ensures that only anonymized data is sent to external LLM services.

Our hybrid model, which integrates with LLM APIs, only works with providers that have signed a Data Processing Agreement (DPA). All API calls are encrypted with TLS 1.3, securing connections.

Our SkyLLM solution offers dedicated local LLM infrastructure for requesting organizations, enabling us to establish closed-circuit systems for both performance and privacy.

Furthermore, we don’t just focus on the technical infrastructure; we also provide customized AI security training for organizations. This transforms employees from mere users of the system into informed and security conscious individuals.

We prevent data leaks with tangible security measures. We simplify processes and manage technical complexities on your behalf. We make the internal use of artificial intelligence secure and sustainable.