Companies are increasingly concerned about security in Kafka. Here’s how to help secure it against breaches, misuse, and compliance failures by implementing data encryption and following Kafka best practices.

Key Takeaways:

• Improving security in Kafka beyond its defaults should be a priority, especially if you handle sensitive data that is protected by government regulations.

• ACLs are insufficient in scaling deployments. You need advanced tools for managing user and application access to avoid misconfiguration and gaps in security.

• Implement Kafka encryption as early as possible, using a solution that does not introduce incompatibility with connected applications.

In sectors such as finance, healthcare, and retail, Apache Kafka usage increasingly includes streaming personally identifiable information (PII) and other sensitive data inside and outside the network. This makes the security of Kafka deployments, and the encryption and protection of data in them, paramount.

This poses a problem because Kafka isn’t secure out of the box. While access control lists (ACLs) are suitable for granting access to a small number of applications, they can be cumbersome and time-consuming to manage for larger organizations with complex access control requirements.

There is also no built-in way to deal with sensitive information that needs to be hidden for privacy reasons, while being accessible enough for the development team to debug issues. Enterprises also increasingly need to share data with third parties in Kafka, which introduces additional security concerns.

If you’re tasked with implementing Kafka encryption and security in your organization, you must consider your present security requirements and how they will change as you scale. This article explains the key technologies and processes you should consider when establishing your Kafka security stance, such as access control, PII masking, encryption, and data sharing best practices.

Using ACLs for securing Kafka can be a DevOps headache

Kafka ACLs are adequate for development, testing, and smaller deployments, but can quickly become too complex to manage when used in production in large organizations with lots of users, topics, and applications. This complexity is primarily because ACLs are verbose and require specificity when writing access rules: whenever you add a user or group, you must add or remove an ACL entry, and the access lists become increasingly harder to maintain.

Another weakness with ACLs is that they don’t cover all of the resources in the Kafka ecosystem. While they can control access to Kafka topics and consumer groups, they can’t control access to other components in the ecosystem such as Kafka Connect and third-party schema registries.

ACLs are also quite rigid, adding friction to security processes in enterprise use cases. Enterprises often need to grant and revoke access dynamically based on user rules, groups, and other contextual information. Access management is a time-consuming manual process when working directly with ACLs. When granular permissions are inconvenient to implement, it’s tempting to grant blanket permissions to a user to reduce the number of access requests, which opens the door to potential data misuse.

Security in Kafka: best practices to follow as you scale

While access control is a significant security factor, it’s not the only consideration for planning and implementing data security in Kafka. You must also consider the configuration of your deployment, methods of restriction, PII masking, Kafka encryption, and how and where your data is shared.

Kafka is a complex system with complex configurations, and no matter how careful you are, mistakes happen. The best way to mitigate this is by making it harder to mess things up by adding guardrails. Placing a layer of abstraction between you, the configuration, and ACLs helps ensure the configuration works as you intend and highlights potential issues.

This approach is also beneficial for Kafka producer settings. Although having a large number of producer settings provides flexibility, it presents risks such as misconfiguring batch sizes, creating performance bottlenecks, and introducing compatibility issues. By contrast, having a set of rules that ensures acceptable producer configurations simplifies the producer configuration process, which greatly reduces the chances of making mistakes.

Enforce time-scoped access for specific purposes