Classify Documents → Train Custom AI Model → Deploy Secure Chatbot

Set up AWS Macie to scan your document repositories and automatically classify files by sensitivity level (public, internal, confidential, classified). Configure classification rules based on keywords, patterns, and metadata to ensure only appropriate documents are used for training.

Create separate S3 buckets with different security policies for each classification level. Enable encryption at rest, configure VPC endpoints for private access, and set up IAM policies that restrict access based on user roles and document classification.

Use Hugging Face's enterprise platform to fine-tune a base language model (like Llama or GPT) on your classified documents. Set up the training pipeline to only access documents marked as appropriate for AI training, with automated monitoring of data usage.

Deploy your trained model using SageMaker in a VPC with no internet access. Configure endpoint encryption, enable model monitoring, and set up logging that captures queries without exposing sensitive content in the responses.

Build a custom Teams app that connects to your SageMaker endpoint. Implement user authentication, audit logging, and response filtering to ensure the chatbot only provides information appropriate for each user's clearance level.