Hands-On Workshops: Build, Fine-Tune, and Secure RAG Systems

You are leading an AI team at a healthcare startup tasked with deploying a retrieval-augmented generation (RAG) system. The goal: enable doctors to query patient histories and medical literature in real time. During testing, the system hallucinates dosage advice and accidentally exposes a patient’s HIV status. This is a compliance and trust disaster.

The actual job is to build RAG pipelines that are not only accurate and performant but also secure, compliant, and auditable. You must prove to regulators and customers that your system guards sensitive data and recovers quickly from attacks or failures. This lesson guides you through hands-on labs to build, fine-tune, and secure RAG systems tailored for regulated sectors like healthcare and finance.

Build a HIPAA-Compliant Medical RAG System

Building a compliant medical RAG system requires embedding safeguards throughout the data lifecycle — from storage to retrieval to generation. HIPAA demands strict control over Protected Health Information (PHI), ensuring it never leaks through AI outputs or logs.

Step 1: Set Up a Secure Cloud Environment

Start by creating an encrypted and access-controlled data store. Use AWS S3 with HIPAA-eligible configurations and AES-256 encryption for data at rest.

aws s3api create-bucket --bucket encrypted-ehr-data --region us-east-1
aws s3api put-bucket-encryption --bucket encrypted-ehr-data --server-side-encryption-configuration '{"Rules": [{"ApplyServerSideEncryptionByDefault": {"SSEAlgorithm": "AES256"}}]}'

This ensures all electronic health records (EHR) stored are encrypted and compliant with HIPAA standards.

Step 2: De-Identify Sensitive Data Using Microsoft Presidio

Before feeding data into your RAG pipeline, strip or mask all PHI to prevent accidental exposure.

from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine

analyzer = AnalyzerEngine()
anonymizer = AnonymizerEngine()

text = "Patient John Doe (ID: 123) has HIV and needs 50mg Lamivudine."
results = analyzer.analyze(text=text, language="en")
anonymized = anonymizer.anonymize(text=text, analyzer_results=results)

print(anonymized.text)
# Output: "Patient [PATIENT_1] (ID: [ID_1]) has [CONDITION] and needs [DRUG_1]."

This automated de-identification prevents PHI from leaking during retrieval or generation. Test adversarial queries like “Show me John’s HIV status” to confirm zero PHI exposure.

Step 3: Deploy Retrieval with AWS HealthLake

Use HealthLake’s FHIR-compliant datastore to query de-identified patient data securely.

import boto3

client = boto3.client("healthlake")

response = client.query(
    DatastoreId="ehr-store",
    QueryString="""
    SELECT * FROM Patient WHERE condition = 'diabetes'
    """
)

HealthLake enforces encryption in transit and at rest, with role-based access controls aligned to HIPAA.

Step 4: Secure Generation with NVIDIA Clara

Integrate your retrieval step with a generation model that forces citations to reduce hallucinations.

from transformers import RagTokenizer, RagRetriever

tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact")

input_text = "Context: {documents}\nQuestion: {query}\nAnswer with citations:"

This ensures any medical advice returned cites authoritative guidelines, e.g., “Metformin (500mg twice daily) [NICE Guidelines 2023].”

Expected Outcome: A chatbot that answers medical queries accurately, with zero PHI leaks and traceable citations.

Fine-Tune a Financial Fraud Detection RAG Model

Financial institutions require RAG systems that not only detect fraud but also maintain audit trails for regulatory compliance such as SOX.

Step 1: Generate Synthetic Banking Data with Gretel

Real banking data is sensitive and cannot be used directly for training. Generate synthetic transactional data that preserves patterns without privacy risks.

from gretel_client import configure_session
import gretel.models as gm

configure_session(api_key="YOUR_KEY", endpoint="")

synthetics = gm.Synthetics("transaction-rag")
synthetics.train(data_path="transactions.csv")
synthetics.generate(num_records=10000, output_path="synthetic_transactions.csv")

This synthetic data enables safe model training while mimicking real fraud patterns.

Step 2: Fine-Tune LLaMA-2 with LoRA for Efficiency

Use Low-Rank Adaptation (LoRA) to fine-tune large language models like LLaMA-2 on fraud detection without retraining the entire model, saving over 80% compute.

from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, Trainer, TrainingArguments

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b")

lora_config = LoraConfig(r=8, lora_alpha=16, target_modules=["q_proj", "v_proj"])
model = get_peft_model(model, lora_config)

trainer = Trainer(
    model=model,
    train_dataset=fraud_dataset,
    args=TrainingArguments(output_dir="fraud-llama"),
)

trainer.train()

Fine-tuning on synthetic fraud data adapts the model to detect suspicious transactions effectively.

Step 3: Deploy with Immutable Audit Logging

Regulators require detailed logs of all fraud detection decisions. Use tools like LangSmith to create tamper-proof audit trails.

from langsmith import Client

client = Client()

def log_fraud_decision(user_query, risk_score):
    client.create_feedback(
        run_id="fraud-check-123",
        key="audit_trail",
        metadata={"query": user_query, "score": risk_score}
    )

This ensures every flagged transaction can be traced and justified during audits.

Expected Outcome: A fraud detection model with 90% accuracy on synthetic data and full regulatory audit logs integrated with Splunk or Datadog.

Simulate and Recover from a Security Breach

RAG systems face adversarial threats like prompt injections that can leak sensitive data. Disaster recovery planning is critical.

Step 1: Detect Breaches Using Prometheus Alerts

Set up anomaly detection for unusual API traffic or error rates as indicators of compromise.

groups:
- name: breach-alerts
  rules:
  - alert: DataExfiltration
    expr: rate(http_requests_total{status="500"}[5m]) > 100
    labels:
      severity: critical

This rule triggers an alert if error traffic spikes abnormally, signaling potential data exfiltration attempts.

Step 2: Isolate Affected Nodes with Kubernetes

Immediately cordon off infected nodes and rollback deployments to a known safe state.

kubectl cordon infected-node-01
kubectl rollout undo deployment/rag-service --to-revision=2

This prevents further damage while restoring system integrity.

Step 3: Restore from Immutable Backups on AWS S3

Use versioned object storage to roll back to uncorrupted model or data snapshots.

aws s3api list-object-versions --bucket ai-backups --prefix prod-rag/
aws s3api restore-object --bucket ai-backups --key prod-rag/model-v2.gguf --version-id "ABCD1234"

Immutable backups guarantee zero data loss post-recovery.

Expected Outcome: Breach contained within 30 minutes with no data loss, minimizing regulatory and reputational risk.

Test Yourself: Deploying a HIPAA-Compliant RAG System

Key Takeaways

1. Compliance Through Code: Embed HIPAA and GDPR safeguards directly into RAG pipelines using encryption (AWS S3), automated de-identification (Microsoft Presidio), and audit logging (Splunk). Compliance cannot be an afterthought.

2. Efficient Fine-Tuning: Use LoRA to adapt large language models like LLaMA-2 for domain-specific tasks such as fraud detection, reducing compute costs by over 80% compared to full retraining.

3. Disaster Readiness: Simulate breaches and automate recovery with Kubernetes rollbacks and immutable backups on AWS S3, targeting containment within 30 minutes and zero data loss.

4. Synthetic Data Safety: Generate synthetic training data with Gretel to avoid privacy risks while maintaining model accuracy and realism.

5. Real-World Validation: Stress-test systems against adversarial inputs, such as queries attempting to extract PHI, to ensure zero leaks before production.

Where to go next

• If you want to deepen your AI system design skills: Advanced RAG Architectures and Techniques
• If you are preparing for AI governance and compliance roles: AI Ethics and Governance Frameworks
• If you want to master scalable AI deployments: Cloud AI Infrastructure and DevOps
• If you want to specialize in AI for regulated industries: Healthcare AI Systems
• If you want to build AI fraud detection pipelines: Fintech AI and Compliance

PL alumni now work at Razorpay, Swiggy, PhonePe, Flipkart, and Amazon — applying these skills to build real-world AI systems.