Developing Secured GenAI in Highly Regulated Industries with Microsoft Fabric LLMs
In a recent conversation with a colleague who works in the largest banking message transmission system in the world, he indicated his willingness to develop generative artificial intelligence (GenAI) solutions for some of his company’s operations. One of the major concerns of the leadership team about GenAI is about security, with the company being highly regulated with such sensitive information. So, after some discussions, I decided to research how to develop secure GenAI applications in Fabric, taking into consideration the concerns around security when GenAI and large language models (LLMs).
Fortunately, Microsoft Fabric offers a robust framework to handle these requirements, ensuring that organizations can leverage GenAI developments securely and effectively.
Highly regulated industries are governed by a myriad of laws and regulations designed to protect sensitive data and ensure ethical practices. For instance, the banking and financial services sectors must comply with regulations, such as the General Data Protection Regulation (GDPR), the Payment Card Industry Data Security Standard (PCI DSS), and the Sarbanes-Oxley Act (SOX). Similarly, the health and pharmaceutical sectors are regulated by the Health Insurance Portability and Accountability Act (HIPAA) and the Food and Drug Administration (FDA) guidelines.
Role of Microsoft Fabric in Secured GenAI Developments
Microsoft Fabric provides a comprehensive platform that integrates security, compliance, and advanced AI capabilities. By leveraging Microsoft Fabric, organizations can ensure that their GenAI developments adhere to regulatory requirements while maintaining high standards of security and performance.
Data Security and Privacy
Microsoft Fabric incorporates data security measures, including encryption, access controls, and audit logs. These features ensure that sensitive information remains protected throughout the AI lifecycle.
Compliance Management
Microsoft Fabric offers tools for managing regulatory compliance, including automated compliance checks, policy enforcement, and reporting. Organizations can define policies that align with regulatory standards and ensure that all AI developments comply with these policies. Automated compliance checks continuously monitor AI processes for adherence to regulatory requirements, while reporting tools generate detailed compliance reports for audits and regulatory reviews.
Model Management and Governance
Secured GenAI developments require rigorous model management and governance to ensure that AI models are reliable, unbiased, and ethical. Microsoft Fabric provides tools for model versioning, performance monitoring, and bias detection. Model versioning allows organizations to track changes and updates to AI models, ensuring that the most accurate and reliable models are used in production. Performance monitoring tracks the accuracy and effectiveness of AI models, identifying any deviations or anomalies that may impact results. Bias detection tools analyze AI models for potential biases, ensuring that AI developments are fair and ethical.
Implementing LLM Models in Highly Regulated Industries
Implementing LLM models in highly regulated industries involves several key steps, including data preparation, model training, validation, and deployment. Microsoft Fabric provides a unified platform for managing these steps, ensuring that AI developments are secure and compliant. However, the most important: where data resides, how it is structured, and how secured it is.
Data Preparation
This involves cleaning, transforming, and anonymizing data to ensure that it is suitable for AI model training. Use Fabric tools for data preprocessing, including data cleaning, normalization, and anonymization. These tools ensure that sensitive information is protected and that data quality is maintained throughout the AI lifecycle.
Model Training
This involves using prepared data to train AI models. Leverage Fabric scalable training environments, allowing to train LLM models on large datasets securely. The platform supports distributed training, enabling multiple computing resources for faster and more efficient model training.
Model Validation
This involves testing AI models to ensure that they perform accurately and reliably. Fabric offers tools for model validation, including cross-validation, performance metrics, and error analysis. This is important before deploying anything to production.
Model Deployment
This involves integrating AI models into production environments. Secure deployment environments with access and usage controls are available in Fabric. This ensures that AI models are protected from unauthorized access and tampering. The platform supports continuous integration and continuous deployment (CI/CD) pipelines, allowing organizations to deploy AI models quickly and securely.
Final Thoughts
Handling secured GenAI developments using LLM models in highly regulated industries is a complex but achievable task. Fabric provides a comprehensive framework that ensures security, compliance, and performance throughout the AI lifecycle. By leveraging Microsoft Fabric, organizations in banking, financial services, health, and pharmaceuticals can harness the power of GenAI while meeting regulatory requirements and protecting sensitive data.
