ABAC for Big Data: Easier Options

TL;DR

Attribute-Based Access Control (ABAC) can be complex to set up with big data systems. This guide shows simpler ways to implement it, focusing on tools and techniques that reduce overhead without sacrificing security.

Implementing ABAC for Big Data: A Step-by-Step Guide

1. Understand Your Requirements
• Before you start, clearly define *who* needs access to *what* data and *why*. This is crucial. List your users/groups (subjects), the data resources, and the actions they need to perform (read, write, delete etc.).
• Identify the attributes that will govern access decisions. Examples: department, job title, security clearance level, data sensitivity classification.
2. Choose an ABAC Engine

Full-blown ABAC solutions can be heavy for big data. Consider these lighter options:

• AWS IAM with Attribute-Based Access Control: If you’re on AWS, this is a good starting point. It integrates well with S3, Athena and other services.
• Apache Ranger: Open source and designed for Hadoop ecosystems (HDFS, Hive, Spark). It provides centralised access control policies.
• Open Policy Agent (OPA): A general-purpose policy engine that can be integrated with various big data platforms using Rego as the policy language. It’s very flexible but requires more coding.
3. AWS IAM ABAC Example

Here’s a basic example of how to use AWS IAM policies for ABAC:

{ 
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::sensitive-data/*",
      "Condition": {
        "StringEquals": {
          "iam:User.Department": "Finance"
        }
      }
    }
  ]
}

This policy allows users in the ‘Finance’ department to access objects within the ‘sensitive-data’ S3 bucket.

4. Apache Ranger Configuration
• Install and configure Apache Ranger with your Hadoop cluster.
• Define policies based on attributes (users, groups, data tags).
• Ranger uses a UI to create these policies; it’s less code-focused than OPA.
• Apply the policies to relevant services like Hive or HDFS.
5. Open Policy Agent (OPA) Integration

This requires more technical skill:

• Install OPA and write Rego policies defining access rules. Example:

  package example
  
  default allow = false
  
  allow { 
    input.user.department == "Engineering" && input.resource.classification == "Confidential"
  }
  

• Integrate OPA with your big data platform (e.g., using a custom authorizer in an API gateway).
• OPA evaluates the policy against incoming requests and determines access based on attributes.
6. Attribute Management

Where will you store user and resource attributes? Options include:

• LDAP/Active Directory: For user attributes.
• Tagging Systems (AWS Tags, Hadoop tags): For data resource attributes.
• Custom Databases: If you need more complex attribute storage.
• Ensure your ABAC engine can access these attribute sources.
7. Testing and Monitoring
• Thoroughly test your policies with different user roles and data resources.
• Monitor access logs to identify any policy violations or unexpected behaviour.
• Regularly review and update your policies as your requirements change.