Elasticsearch powers critical search and analytics workloads across industries, often handling highly sensitive data such as personal identifiers, financial records, and health information. Ensuring that this sensitive data remains secure within Elasticsearch is paramount. This blog explores Field-Level Security (FLS) and encryption techniques to safeguard sensitive information, maintain compliance, and minimize data exposure risks.

What is Field-Level Security in Elasticsearch?

Field-Level Security (FLS) allows fine-grained control over which document fields a user can view or query. Unlike broad index-level access control, FLS restricts access to specific fields within documents, enabling:

  • Data masking or hiding of confidential fields such as passwords, SSNs, or credit card numbers.
  • Role-based filtering of sensitive content on a per-user basis.
  • Compliance with data privacy regulations like GDPR by limiting data exposure.
How FLS Works
  • Define roles with field_security settings specifying grant (allowed fields) or except (excluded fields).
  • Assign roles to users so queries return only permitted fields.
  • Works alongside Document-Level Security (DLS) for granular data access control.

Setting Up Field-Level Security

  1. Create a role with field restrictions in the roles.yml or via the Elasticsearch API: 
    {
  "indices": [
    {
      "names": ["customer_data"],
      "privileges": ["read"],
      "field_security": {
        "grant": ["name", "email", "phone"]
      }
    }
  ]
}

  2. Assign the role to users requiring limited access.

  3. Test access by querying Elasticsearch as those users to verify restricted fields are hidden.

Encryption Techniques to Protect Sensitive Data

Encryption at Rest

Encrypt stored data to prevent unauthorized access to raw files or backups:

  • Use built-in disk encryption at the OS or cloud provider level.
  • Employ Elasticsearch encryption plugins or integrate with Key Management Services (KMS) to manage encryption keys securely.
  • Regularly rotate encryption keys following security best practices.
Encryption in Transit

Protect data traveling between clients and Elasticsearch nodes or between nodes themselves:

  • Enable TLS/SSL for HTTP and transport layers in the Elasticsearch configuration.
  • Use strong ciphers and certificate-based authentication.
  • Optionally implement mutual TLS (mTLS) for added node authentication.
Encrypting Sensitive Fields Within Documents

While Elasticsearch doesn’t natively encrypt individual fields, you can:

  • Encrypt sensitive data before indexing using application-level encryption.
  • Store encrypted data and restrict access using FLS, ensuring only authorized users can decrypt or view data.
  • Use search tokenization or homomorphic encryption techniques in advanced use cases.

Best Practices for Combining FLS and Encryption

  • Always use encryption in transit to prevent interception of sensitive queries or data.
  • Pair FLS with RBAC to control both what fields users see and which indexes they can access.
  • Monitor audit logs for attempts to access restricted fields or unusual access patterns.
  • Educate developers and admins on sensitive data handling within Elasticsearch workflows.
  • Regularly update Elasticsearch and security configurations to patch vulnerabilities.

Conclusion

Protecting sensitive data within Elasticsearch demands a multi-layered approach. Field-Level Security provides granular access control to limit sensitive field exposure, while encryption safeguards data at rest and in transit from unauthorized access. Together, these strategies enable organizations to maintain strong data privacy, comply with regulations, and secure their Elasticsearch deployments against evolving threats.

Implementing these techniques requires thoughtful planning and continuous monitoring but offers invaluable peace of mind when managing critical data at scale.