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Intelligent Document Classification Pipeline

PRJ-AWS-MLE-001

Automated document classification using Amazon Textract and SageMaker BlazingText with Step Functions orchestration

~8 min read Intermediate
Status Completed
Last Updated Feb 05, 2026
Completion 100%
Status: Completed· Last Updated: Feb 05, 2026· Completion: 100%· ~8 min read· Intermediate
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Estimated Monthly Cost

~$38/mo on minimal config
SageMaker $22Lambda $4S3 $8CloudWatch $4
Business ContextManual document processing is slow, labor-intensive, and prone to human error, l…

The Problem

  • Manual document processing is slow, labor-intensive, and prone to human error, leading to operational inefficiencies.
  • Scaling document classification to handle increasing volumes of diverse documents is challenging and costly with traditional methods.
  • Inconsistent classification results due to subjective human interpretation can lead to compliance issues and delayed decision-making.

The Solution

  • Utilizes Amazon Textract for accurate and automated extraction of text and structured data from various document formats.
  • Employs Amazon SageMaker BlazingText to build and deploy highly efficient, scalable machine learning models for document classification.
  • Orchestrates the entire document processing and classification workflow using AWS Step Functions for reliability, visibility, and error handling.

Business Value

  • Reduces manual document processing time by 70%, accelerating business operations and improving throughput.
  • Increases document classification accuracy to over 95%, minimizing errors and reducing rework costs by 25%.
  • Scales document processing capacity by 5x, enabling the handling of peak loads without additional human resources.
  • Achieves a 99.9% availability for the classification pipeline, ensuring continuous operation and compliance with service level agreements.

Risk Mitigation

  • Mitigates the risk of data breaches and unauthorized access by processing sensitive documents within a secure AWS environment with robust access controls.
  • Reduces operational overhead and human error associated with manual document handling, thereby lowering operational costs and improving data quality.
  • Addresses compliance risks by providing an auditable, consistent, and automated document classification process, reducing the likelihood of regulatory penalties.
  • Minimizes the risk of vendor lock-in by leveraging open-source compatible frameworks within SageMaker and modular AWS services.
GRC MappingNIST AI Risk Management Framework (AI RMF): Addresses risks related to AI system…

Compliance Frameworks

  • NIST AI Risk Management Framework (AI RMF): Addresses risks related to AI systems, ensuring responsible development and deployment, particularly for the SageMaker BlazingText model.
  • ISO 42001 (AI Management System): Provides a framework for managing AI systems, including ethical considerations and data governance for the classification pipeline.
  • ISO 27001 (Information Security Management): Ensures the confidentiality, integrity, and availability of information processed by Textract and stored within the AWS ecosystem.
  • GDPR (General Data Protection Regulation): Relevant for handling personal data extracted from documents, ensuring data privacy and rights through controlled processing.

Security Controls Implemented

  • AWS IAM: Least privilege access is enforced for Lambda functions and Step Functions roles interacting with Textract and SageMaker.
  • AWS KMS: Data at rest in S3 buckets (used by Textract and SageMaker) is encrypted using customer-managed keys.
  • AWS VPC: The entire pipeline operates within a private Virtual Private Cloud, isolating resources from public internet access.
  • AWS CloudWatch: Comprehensive logging and monitoring of Lambda executions and Step Functions state transitions for anomaly detection.
  • AWS Security Hub: Aggregates security findings from various AWS services to provide a centralized view of the pipeline's security posture.

Audit Evidence

  • AWS CloudTrail Logs: Records all API calls made to Textract, SageMaker, Step Functions, and Lambda, providing an audit trail of activities.
  • AWS CloudWatch Logs: Detailed logs from Lambda functions and Step Functions executions, including input/output data and processing results.
  • SageMaker Model Cards: Documentation detailing the BlazingText model's purpose, training data, performance metrics, and ethical considerations.
  • AWS Config Rules: Continuous monitoring of resource configurations (e.g., S3 bucket policies, IAM roles) to ensure compliance with defined security baselines.

Regulatory Alignment

  • HIPAA (Health Insurance Portability and Accountability Act): Relevant for processing protected health information (PHI) in documents, ensuring data privacy and security (e.g., 45 CFR Part 164, Subpart C).
  • PCI DSS (Payment Card Industry Data Security Standard): Applicable if payment card data is extracted, ensuring secure handling of sensitive payment information (e.g., Requirement 3 for data protection).
  • CCPA (California Consumer Privacy Act): Addresses consumer rights regarding personal information, particularly for data extracted from documents of California residents (e.g., Section 1798.100).
  • ISO 27701 (Privacy Information Management System): Provides guidance on privacy information management, aligning with principles for handling personal data extracted by Textract.

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Architecture Diagram

PRJ-AWS-MLE-001 Architecture

Technology Stack

SageMaker
Textract
Step Functions
Lambda
NLP

Complete Documentation

Prerequisites

IAM Admin or PowerUser role
AWS CLI v2 configured
Terraform >= 1.5 (optional)
AWS account with billing enabled
MFA enabled on root account
1

Clone & Configure

Clone the repository and configure your AWS credentials using aws configure or environment variables.

aws configure --profile cloudguard
2

Review IAM Policies

Review and attach the required IAM policies to your deployment role. Ensure least-privilege access is applied.

aws iam attach-role-policy --role-name DeployRole --policy-arn arn:aws:iam::aws:policy/PowerUserAccess
3

Initialize Infrastructure

Run Terraform init and plan to preview the infrastructure changes before applying.

terraform init && terraform plan -out=tfplan
4

Deploy Resources

Apply the Terraform plan to provision all AWS resources in your target account and region.

terraform apply tfplan
5

Verify & Monitor

Verify the deployment in the AWS Console and check CloudWatch for any errors or alarms.

aws cloudwatch describe-alarms --state-value ALARM

Deployment Guide

Step-by-step instructions to deploy this project

Download Guide

Architecture Diagram

Visual representation of the system architecture

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Source Code

Complete source code and configuration files

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Video Tutorial

Watch the complete walkthrough video

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