Future-focused businesses are adopting a data mesh architecture as part of their data and analytics modernization strategy to reach their business goals. And they're building it in the cloud.

Of course, there are always security, governance, and compliance issues to consider. If you have data in the wrong hands at the wrong time, this can lead to data breaches, hefty fines, and significant brand damage. So, what can you do to secure your business' data?

Before we explore the solution, let's recap some of the basics.

What is a data mesh?

A data mesh is essentially a decentralization of data that puts data management responsibilities in the hands of the people who are closest to it. With a data mesh, you can create data products for different personas and business units. This makes it far easier to meet the demands of your data stakeholders and improve data quality.

Building a data mesh supports the approach of data as a product. The data mesh will allow for relevant teams to take ownership of the data products – tailoring and updating them whenever necessary – and allowing access via a data catalog.

Common data mesh security challenges

Data security is always a top priority. And a data mesh comes with new – and some familiar – challenges:

  • Visibility: If you can't see who is accessing what data and when, you can't protect it. Unauthorized or unnecessary access is one of the biggest challenges of a data mesh – but if you can't see the problems, how can you address them?
  • Access: When you have the visibility, how can you make sure the right people have the right level of access? With so many data regulations across industries and countries, it's easy to misstep. After all, a user from marketing should not have the same level of access as other departments, such as HR.
  • Management: When data is a product, you need product owners. They must take ownership for visibility, access, and control. It's why the data catalog is so crucial – each user must have customized access to it.

The security solution

With these challenges in mind, it's easy to see why data leaders want clear governance, risk mitigation, compliance, and ownership of the data product lifecycle. Thankfully, we have five tips to help you better manage your cloud-based data mesh:

  1. Don't forget the fundamentals: You can still use many of the data policies and standards you've relied on in the past as starting points. For example, as the data owners create data products, show them the company processes and policies they will be following. And make sure to enforce them globally.
  2. Adopt a continuous security assessment approach: Whether it's today, tomorrow, or in the future, make sure you have a way to continuously assess the data mesh environment's security. And check the data solutions – including analytics workloads or machine learning models – your company relies on to use current and reliable curated datasets. If you don't, it could result in significant data drift, governance, and compliance issues.
  3. Adopt zero trust principles for your data mesh architectures: It's time for your security teams to implement a zero-trust approach for all data traffic and adhere to fundamental security guideline principles – including the principle of least privilege. The best advice is to look for a cloud security specialist who can guide you through this.
  4. Add extra layers: When it comes to security, the more layers of protection you put in place, the stronger your business' protection is. Consider a "defense in depth" approach including network security, microsegmentation, data encryption, and additional layers of authorization – among other measures – to fortify the security already in place.
  5. Monitor and log: Keep track of every action – and every red flag – across your data mesh. If something goes wrong, you'll know where to look, boosting your ability to act with agility. Remote updates and patching will also be essential.

These five tips are just the start. Your data mesh will continue to grow and evolve. Perhaps you want to add new analytics workloads or build new machine learning models. But you can only do this on a secure foundation, which is why you must put security at the top of your priority list.