Privacy and Security in RAG Applications

Abstract

This project demonstrates a Retrieval-Augmented Generation (RAG) assistant designed with a focus on privacy and security.
The assistant only answers based on publication documents and ensures no hallucinations or leakage of sensitive information.
It serves as a research demo of building safe and reliable RAG systems with Groq LLM.

Methodology

The RAG pipeline was implemented in the following steps:

1. Data Ingestion

   • The target publication (Privacy and Security in RAG) was loaded and embedded into a vector database.

2. Embedding & Retrieval

   • Queries are converted into embeddings and matched against stored document chunks.
   • Relevant chunks are retrieved based on similarity thresholds.

3. Prompt Construction

   • A structured prompt is built, combining system rules, style/tone, and the retrieved documents.

4. LLM Response

   • The Groq LLM receives the constructed prompt and generates answers strictly based on retrieved content.

5. Constraints

   • Refuses to answer if the query is unrelated to the documents.
   • Responds in Markdown format, with concise bullet points where relevant.
   • Avoids hallucinations and enforces privacy/security guidelines.

flowchart TD
A[User Question] --> B[Retriever]
B --> C[Relevant Chunks]
C --> D[Prompt Builder]
D --> E[Groq LLM]
E --> F[Answer]

Results

Below are example interactions with the RAG Assistant:

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Q1: What is this publication about?
A1:

• The publication "Privacy and Security in RAG" addresses privacy and security concerns in RAG environments.
• Focuses on:
  • Confidentiality and integrity of data
  • Protection against unauthorized access
  • Secure storage and transmission
  • Compliance with regulations

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Q2: What tools are used?
A2:

• None mentioned in the provided document chunks.

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Q3: What will be used as input data?
A3:

• User queries
• Relevant/irrelevant documents
• User feedback in the form of judgments

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Q4: How do you ensure your AI system is fair?
A4:

• Not mentioned in the documents.
• The text focuses on latency and accuracy trade-offs, but not fairness.

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Q5: How do you keep your AI system secure?
A5:

• Implement robust security frameworks
• Apply access controls & encryption
• Ensure regulatory compliance (GDPR, HIPAA, etc.)
• Educate developers and users about security risks

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Q6: What must you tell data subjects?
A6:

• The personal data being collected, used, and shared.
• Their rights under regulations (GDPR, HIPAA, etc.), including:
  • Right to be informed
  • Right to access
  • Right to rectification
  • Right to erasure
  • Right to restrict processing
  • Right to data portability
  • Right to object
  • Right to withdraw consent

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Q7: How do you ensure data subject rights?
A7:

• Implement layered security (access controls, encryption, monitoring).
• Conduct regular assessments and updates.
• Ensure compliance when sharing with external services (e.g., GDPR/HIPAA).
• Proactively protect user data to prevent misuse.