The Metadata Trap: Securing RAG Pipelines with Presidio and AWS Lambda

We are currently witnessing the GenAI gold rush. Companies everywhere are rushing to implement Retrieval-Augmented Generation (RAG) to allow their Large Language Models (LLMs) to chat with proprietary data. It is a transformative technology, allowing employees to query knowledge bases, legal contracts, and HR policies in natural language.

But there is a trap waiting in the unstructured data. We call it The Metadata Trap.

When you dump thousands of PDFs, emails, and Word documents into a vector database, you aren't just ingesting the text you see on the screen. You are often ingesting author names, hidden comments, version history, and Personally Identifiable Information (PII) embedded in the metadata or body text. If an LLM retrieves a vector containing a social security number or a confidential internal comment, it will dutifully generate an answer that includes it.

At Nohatek, we believe that security cannot be an afterthought in AI architecture. In this post, we will explore how to build a robust, automated sanitization layer using Microsoft Presidio and AWS Lambda to clean your data before it ever touches your vector database.

The Risk: Why 'Garbage In' Means 'Leakage Out'

In traditional software development, we sanitize inputs to prevent SQL injection or XSS attacks. in the world of Generative AI, we must sanitize inputs to prevent Data Exfiltration and Hallucinated Compliance Violations.

Consider a typical RAG workflow:

1. A user uploads a PDF.
2. The system chunks the text.
3. The system creates embeddings (vectors) via an API like OpenAI or Bedrock.
4. The vectors are stored in Pinecone, Weaviate, or pgvector.

If that PDF contains a phone number, an email address, or a credit card number, that PII is now immortalized in your vector store. When a user asks a question that is semantically similar to that PII context, the RAG system retrieves the sensitive chunk and feeds it to the LLM.

The Reality Check: LLMs are designed to be helpful. If you provide them with sensitive context, they will use it to answer the user's prompt, effectively bypassing your organization's access controls and privacy policies (GDPR, CCPA, HIPAA).

The solution is not to restrict the AI, but to sanitize the data stream. We need a 'washing machine' for documents that sits between the raw upload and the embedding process.

The Architecture: Serverless Sanitization

To solve this efficiently, we need a solution that is scalable, event-driven, and cost-effective. We don't want a dedicated server running 24/7 just waiting for documents. This is a perfect use case for AWS Lambda paired with Microsoft Presidio.

Why Microsoft Presidio?
Presidio is the industry standard open-source library for data protection. It uses predefined recognizers (regex and ML models) to detect PII entities like credit card numbers, crypto wallets, names, and locations. Crucially, it separates the Analysis (finding the PII) from the Anonymization (redacting or hashing it).

The Workflow:

• S3 Ingestion Bucket: The entry point where raw documents are uploaded.
• S3 Event Trigger: An upload event triggers an AWS Lambda function.
• The Sanitizer Lambda: This function downloads the file, extracts the text, runs Presidio to identify and replace PII with placeholders (e.g., <PERSON_NAME>), and strips metadata.
• S3 Clean Bucket: The sanitized file is saved here, which then triggers the embedding pipeline.

By decoupling these stages, you ensure that your vector database only ever sees clean, redacted data. Even if the LLM hallucinates, it cannot leak data it doesn't have.

Implementation: Coding the Presidio Layer

Let's look at the code. Deploying Presidio on Lambda requires handling the NLP model dependencies (like spaCy). The best approach is to package the Lambda as a Docker container.

Here is a simplified Python snippet demonstrating how to initialize the engine and redact text:

from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine
from presidio_anonymizer.entities import OperatorConfig

# Initialize engines (load NLP models outside handler for performance)
analyzer = AnalyzerEngine()
anonymizer = AnonymizerEngine()

def sanitize_text(text):
    # 1. Analyze: Find the PII
    results = analyzer.analyze(text=text, 
                               entities=["PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD"],
                               language='en')

    # 2. Anonymize: Redact the found PII
    anonymized_result = anonymizer.anonymize(
        text=text,
        analyzer_results=results,
        operators={
            "DEFAULT": OperatorConfig("replace", {"new_value": "<REDACTED>"})
        }
    )

    return anonymized_result.text

Handling False Positives
One challenge with automation is over-redaction. Presidio allows you to set a score_threshold. For RAG pipelines, we usually recommend a threshold between 0.4 and 0.6. A lower score is safer (redacts more) but might obscure context needed for the LLM to understand the document. You can also create Allow Lists for terms that look like PII but aren't (e.g., your company's support email address or CEO's name).

Beyond Text: Handling PDFs and Images

Text files are easy. But the corporate world runs on PDFs and scanned images. To make this pipeline production-ready, your Lambda function needs an extraction layer before the sanitization layer.

For searchable PDFs, libraries like PyMuPDF or pdfplumber are essential. They allow you to extract text, run Presidio on it, and importantly, reconstruct the document structure so the semantic meaning isn't lost during vectorization.

For scanned documents (images), you will need to integrate OCR (Optical Character Recognition). You can use AWS Textract within the same Lambda workflow:

• Call Textract to get raw text from the image.
• Pass the raw text to Presidio.
• Save the text output to the Clean Bucket.

Pro Tip: Always strip the file metadata properties (Author, Title, Creation Date) explicitly. In Python, clearing the doc.metadata dictionary is a simple step that prevents legacy employee names from leaking into the system.

Building a RAG pipeline is easy; building a secure RAG pipeline requires diligence. The "Metadata Trap" is real, and the consequences of leaking PII through a chatbot can be severe for your reputation and compliance standing.

By implementing an automated sanitization layer with AWS Lambda and Microsoft Presidio, you create a security checkpoint that scales with your data. You ensure that your AI is smart enough to answer questions, but not loose-lipped enough to reveal secrets.

Need help securing your AI infrastructure? At Nohatek, we specialize in building enterprise-grade, secure cloud and AI solutions. Reach out to our team today to discuss your architecture.