LLM Observability and Data Security

Alternate approaches when data cannot be passed to LLM

When a customer does not want to pass data to an LLM due to privacy, security, or compliance concerns, here are some alternative approaches:

1. On-Premise or Private Cloud Deployment

• Deploy an LLM on-premise or within a private cloud environment (e.g., Azure Private Cloud, AWS Outposts).

• Use Azure OpenAI on Azure Kubernetes Service (AKS) or Azure OpenAI with Virtual Network (VNet) isolation to keep data within a secure environment.

2. Embeddings & Vector Search (RAG Approach)

• Instead of sending raw data, extract text embeddings using Azure OpenAI Embeddings API and store them in Azure Cognitive Search or FAISS.

• Perform Retrieval-Augmented Generation (RAG) where only relevant context is retrieved and processed locally before feeding into the LLM.

3. Model Fine-Tuning on Redacted/Synthetic Data

• Train a domain-specific smaller LLM on synthetic data that mimics real-world data but does not expose PII.

• Use differential privacy techniques to ensure anonymized data training.

4. Edge Computing & Federated Learning

• Run LLM inference locally on edge devices (e.g., hospital workstations, IoT devices in healthcare).

• Use federated learning, where models train on local data and share only model updates instead of raw data.

5. Zero-Shot & Few-Shot Learning with Contextual Prompts

• Instead of passing full data, use structured prompts with minimal metadata to guide the LLM without exposing sensitive details.

• Example: Instead of sending a full patient report, only send encoded categorical values or summary statistics.

6. Hybrid AI Models (LLM + Traditional Rule-Based Systems)

• Combine LLM reasoning with traditional rule-based AI (e.g., Azure ML, Decision Trees) to minimize dependency on LLMs for data-intensive tasks.

List of Offline Language Models

Here are some offline Large Language Models (LLMs) that can run on-premise, edge devices, or private cloud without sending data to external servers:

1. Open-Source LLMs (General Purpose)

• Llama 2 (Meta) – Available in 7B, 13B, 70B parameters. Can run on-premise or locally using Ollama, vLLM, or Text Generation Web UI.

• Mistral 7B – Highly efficient model, strong reasoning ability, can run on GPUs with limited memory.

• Mixtral (Mistral AI) – A mixture of experts (MoE) model, activated sparsely for efficient inference.

• Falcon (TII, UAE) – Available in 7B, 40B, optimized for offline use.

• GPT4All (Multiple Models) – Lightweight models that can run on consumer-grade CPUs.

2. Healthcare-Specific LLMs

• Med-PaLM 2 (Google) – Designed for medical question answering.

• BioGPT (Microsoft Research) – Optimized for biomedical research & documentation.

• GatorTron (University of Florida) – Focused on clinical NLP for EHR analysis.

• ClinicalBERT & PubMedBERT – Pretrained models on medical literature.

3. Microsoft Azure Private AI Options

• Azure OpenAI (Private Deployment) – GPT-4, GPT-3.5 hosted inside a private VNet.

• Phi-2 (Microsoft) – Small yet powerful 4.3B parameter model, useful for healthcare AI on limited hardware.

4. Offline LLM Frameworks

• Ollama – Easy way to run models like Llama 2, Mistral on Mac, Linux, Windows.

• vLLM – Optimized for fast inference on GPUs.

• LM Studio – GUI-based tool for running local LLMs.

• PrivateGPT – Allows running RAG-based local AI with offline documents.

Healthcare specific Large Language Models

Here are some LLMs specialized for healthcare that can be used for clinical documentation, medical reasoning, diagnostics, and AI-driven decision support:

1. General Healthcare LLMs

• Med-PaLM 2 (Google DeepMind) – Trained on medical knowledge and performs well on USMLE-style questions.

• Meditron (Hugging Face) – Open-source 7B model, fine-tuned for clinical and biomedical tasks.

• GatorTron (University of Florida) – Optimized for electronic health records (EHR) processing.

• ClinicalBERT & PubMedBERT – Pretrained on PubMed abstracts and clinical notes for biomedical NLP tasks.

• BioGPT (Microsoft Research) – Specialized for biomedical literature analysis and clinical text generation.

2. LLMs for Medical Imaging & Diagnosis

• ChestXray-BERT (NIH) – Built for radiology report generation.

• PathologyBERT (MIT & Harvard) – Focused on pathology and histology analysis.

• DermGPT (Stanford) – Skin disease classification and dermatology-focused NLP.

3. Open-Source Healthcare LLMs (Self-Hostable)

• Meditron-7B – Open-source, fine-tuned for clinical reasoning and summarization.

• BioMedLM (Stanford CRFM) – Supports biomedical text processing and clinical predictions.

• EHR-BERT (Google Health) – Trained on EHR datasets for better patient record analysis.

• EMRBERT (Mayo Clinic) – Designed for clinical text mining from electronic medical records (EMR).

4. Microsoft Azure Healthcare AI Solutions

• Azure OpenAI GPT-4 (Private Deployment) – Can be fine-tuned with healthcare-specific data in Azure Healthcare AI environments.

• Phi-2 (Microsoft Research) – 4.3B parameter model, efficient for clinical NLP tasks.

• Azure Cognitive Search + LLM (RAG-based Healthcare AI) – Combine Azure Cognitive Search with an LLM to retrieve medical documents without exposing patient data.

LLM Data Security Checklist

When deploying LLMs in a secure environment, especially in healthcare (HIPAA, GDPR) or enterprise AI, follow this checklist to protect sensitive data, prevent leaks, and ensure compliance.

1. Data Privacy & Protection

• Minimize Data Exposure – Only send essential data to the LLM (use structured prompts instead of full patient records).
• Mask & Anonymize PII – Use de-identification techniques for PHI, names, IDs, and addresses before processing.
• Use Local or Private Deployment – Prefer on-premise models or Azure OpenAI with Private VNet to avoid external exposure.
• Implement Role-Based Access Control (RBAC) – Restrict who can access LLM data (Azure AD, IAM policies).
• Log & Monitor Data Access – Track who queries the LLM and detect unauthorized access.

2. Secure Model Deployment

• Use Private Endpoints – Deploy models inside a VNet to prevent exposure to the public internet.
• Encrypt Data in Transit & At Rest – Use TLS 1.2+ for transmission, and AES-256 for storage encryption.
• Limit API Exposure – Only expose LLM endpoints to trusted applications within the organization.
• Use Container Security – If deploying LLMs on Kubernetes, enable Azure Defender for Containers.
• Run Regular Security Audits – Perform penetration testing to check for vulnerabilities.

3. Prevent Prompt Injection & Data Leaks

• Sanitize User Inputs – Strip malicious input patterns that could trick the LLM into exposing internal data.
• Limit Context Window Access – Restrict how much of a conversation history an LLM retains.
• Set Token Limits – Prevent long prompts that could manipulate or extract unwanted data.
• Filter Responses for PII – Use regular expressions or AI classifiers to remove unintended disclosures.
• Enable Content Moderation – Use Azure OpenAI Content Filtering to block unauthorized queries.

4. Compliance & Governance

• Adhere to Healthcare Regulations – Ensure compliance with HIPAA, GDPR, ISO 27001, SOC 2, and NIST standards.
• Use Audit Logging – Maintain logs of LLM interactions for regulatory audits.
• Apply Differential Privacy – Add noise to model outputs to prevent re-identification of sensitive data.
• Limit Model Training on Sensitive Data – If fine-tuning, only use de-identified or synthetic datasets.

5. AI Ethics & Bias Mitigation

• Monitor Model Bias – Regularly test for biased outputs in medical or staffing recommendations.
• Implement Human-in-the-Loop – Require human review for critical AI-driven decisions.
• Provide Explainability – Use interpretable AI techniques to explain why a model made a decision.

6. Azure-Specific Security Enhancements

• Azure OpenAI Private Deployment → Keeps data within an isolated VNet.
• Azure Key Vault → Securely store API keys & encryption keys.
• Microsoft Purview → Enable data governance & compliance tracking for LLM queries.
• Azure Defender for Cloud → Continuously monitor for LLM security risks.

Observability Layer for LLM-Based Applications

The observability layer in LLM-based applications provides real-time monitoring, logging, tracing, and analytics to track model performance, security, and user interactions. It helps detect anomalies, optimize costs, and ensure compliance.

Key Components of LLM Observability

1. Logging & Monitoring (Track Model Behavior & Usage)

• Prompt & Response Logging – Store all queries and responses for auditing and debugging.
• Latency Monitoring – Track response times to optimize inference speed.
• Token Usage Tracking – Monitor API token consumption to control costs.
• Error Logging – Capture failed requests, API errors, or unexpected model outputs.

🛠 Tools: Azure Monitor, OpenTelemetry, Datadog, Prometheus + Grafana

2. Tracing & Performance Optimization (End-to-End Visibility)

• Distributed Tracing – Monitor LLM API calls across microservices.
• Model Response Analysis – Track hallucinations, biases, and drift over time.
• Load Balancing Insights – Optimize requests between local models and cloud-based LLMs.

🛠 Tools: OpenTelemetry, Jaeger, Zipkin, Azure Application Insights

3. Security & Compliance Monitoring (Prevent Data Leaks & Abuse)

• PII/PHI Detection – Automatically flag sensitive data exposure.
• Prompt Injection & Jailbreak Detection – Identify malicious inputs attempting to exploit the LLM.
• Access Logs & Role-Based Auditing – Ensure only authorized users interact with the model.

🛠 Tools: Azure Purview, Microsoft Defender for Cloud, LangKit (for AI Security)

4. Feedback & Continuous Improvement (Improve Model Performance)

• Human-in-the-Loop (HITL) Feedback – Enable real-time user feedback on LLM responses.
• A/B Testing for Model Variants – Compare fine-tuned vs. base models.
• Auto-Retraining Triggers – Use data drift detection to retrain models when necessary.

🛠 Tools: Azure ML Model Monitoring, MLflow, Weights & Biases

Architecture for Observability Layer in LLM Apps

1️. User Query → Logged via Azure Monitor / OpenTelemetry

2️. LLM API Call → Tracked via Jaeger / Zipkin for latency

3️. Response Analysis → Filtered for bias, hallucinations, security risks

4️. Feedback Storage → Insights stored in Azure Data Lake / ElasticSearch

5️. Automated Alerts → Triggered if sensitive data exposure or API misuse detected

Final Thoughts

Adding an observability layer to LLM apps ensures trust, reliability, security, and compliance—crucial for healthcare AI, finance, and enterprise applications.

Here's a reference architecture for an LLM Observability Stack:

LLM Observability Architecture Components

1️. User Interaction & Logging

• Frontend / API Gateway logs all incoming queries

• Azure Monitor / OpenTelemetry captures API requests and responses

2️. LLM Request Processing & Tracing

• LLM Model (Cloud or On-Premise)

• Jaeger / Zipkin for distributed tracing across AI pipelines

• Azure Application Insights monitors model response times

3️. Security & Compliance Layer

• Azure Purview scans for PHI / PII leaks

• Microsoft Defender for Cloud detects unauthorized access

• Prompt Injection Detection (e.g., LangKit)

4️. Performance & Token Usage Monitoring

• Prometheus + Grafana visualize API latency, token usage, and throughput

• Azure Cost Management tracks model inference costs

5️. Feedback & Continuous Learning

• Human-in-the-Loop (HITL) Dashboard stores flagged responses

• Azure ML Model Monitoring detects data drift & bias

• Retraining Pipeline triggered if model performance degrades