LLM Observability and Data Security

Alternate approaches when data cannot be passed to LLM

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

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1. On-Premise or Private Cloud Deployment

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

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

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2. Embeddings & Vector Search (RAG Approach)

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

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

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3. Model Fine-Tuning on Redacted/Synthetic Data

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

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• Use differential privacy techniques to ensure anonymized data training.

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4. Edge Computing & Federated Learning

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

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

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

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

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

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

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

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List of Offline Language Models

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

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1. Open-Source LLMs (General Purpose)

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

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

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

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

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

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2. Healthcare-Specific LLMs

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

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

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

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? ClinicalBERT & PubMedBERT – Pretrained models on medical literature.

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3. Microsoft Azure Private AI Options

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

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

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4. Offline LLM Frameworks

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

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? vLLM – Optimized for fast inference on GPUs.

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

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

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Healthcare specific Large Language Models

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

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1. General Healthcare LLMs

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

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

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

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

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

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2. LLMs for Medical Imaging & Diagnosis

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

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

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

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

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

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

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

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

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4. Microsoft Azure Healthcare AI Solutions

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

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

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

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LLM Data Security Checklist

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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.

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1. Data Privacy & Protection

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• 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.

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2. Secure Model Deployment

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• 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.

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3. Prevent Prompt Injection & Data Leaks

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• 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.

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4. Compliance & Governance

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• 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.

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5. AI Ethics & Bias Mitigation

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• 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.

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6. Azure-Specific Security Enhancements

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• 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.

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Observability Layer for LLM-Based Applications

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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.

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Key Components of LLM Observability

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

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• 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.

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

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

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• 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.

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

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

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• 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.

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

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4. Feedback & Continuous Improvement (Improve Model Performance)

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• 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.

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

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Architecture for Observability Layer in LLM Apps

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1?. User Query → Logged via Azure Monitor / OpenTelemetry

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

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

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

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

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Final Thoughts

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

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

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LLM Observability Architecture Components

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1?. User Interaction & Logging

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? Frontend / API Gateway logs all incoming queries

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? Azure Monitor / OpenTelemetry captures API requests and responses

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2?. LLM Request Processing & Tracing

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? LLM Model (Cloud or On-Premise)

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? Jaeger / Zipkin for distributed tracing across AI pipelines

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? Azure Application Insights monitors model response times

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3?. Security & Compliance Layer

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? Azure Purview scans for PHI / PII leaks

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? Microsoft Defender for Cloud detects unauthorized access

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

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4?. Performance & Token Usage Monitoring

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

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? Azure Cost Management tracks model inference costs

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5?. Feedback & Continuous Learning

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

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? Azure ML Model Monitoring detects data drift & bias

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? Retraining Pipeline triggered if model performance degrades

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