The Challenges of Protecting Customer Data in a Growing Organization

We all know customer data is growing in use in this AI-dominated technology era. But I wanted to know how important it is for the companies we work with.

“If I had to stop or even delay using customer data, our growth would be in jeopardy. We use customer data to shape the future of our product, generate more customers, and save costs.” This was the response I got when asking how paramount customer data is to the business of one of our customers, a midsize SaaS startup.

That’s not surprising. In fact, in a research study done by Nielsen, 86% of the respondents from medium and large businesses considered customer data to be the most important aspect of their media strategy.

And what about the customers? They expect their data to be used. In fact, a McKinsey survey found that 76% of consumers expect personalized experiences and feel disappointed when they don’t receive them.

What customers do not expect, and do not tolerate, is mishandling of their personal data. We see that in the growth of data privacy laws and compliance frameworks that are becoming increasingly demanding. In other words, it’s quite simple: companies are expected to use their customer data to deliver more value, but to do so in a responsible way.

There’s no doubt that it’s both important for businesses to be able to use customer data in an effective way, and also much easier to do than in the past. Everything from collecting data to BI and AI has become easier by an order of magnitude in the past decade. Because it’s become so easy to make use of data, there’s been a flood of new users who can do it in many different ways, and for many different reasons, as a result. 

Let’s break down the key challenges in securing customer data:

The volume and velocity of data being collected and used has grown rapidly. Over time, data is getting cheaper and cheaper to store and access, and the requirements for doing so are also becoming less strict. For example, users (such as analysts, engineers, or data scientists) no longer need to define specific schemas for data hoarded in data lakes. This inevitably means you have more data, and within it, customer data. Sometimes that customer data is not easy to locate. Not only that, but data is also more complex and diverse, scattered across more technologies and cloud environments.

Not only is customer data stored in more places and harder to find, but there are more eyeballs looking at it. The reason is a combination of the ease of using data and the business expectations to use customer data for more projects.

These “new data users” have diverse roles, tech skillsets, and privileges. Data users are no longer limited to “classic” roles like designated data analysts – Chris from customer success or Sarah from sales are now just as likely to interact directly with customer data.

This increases the risks of both compliance breaches and data breaches.

A growing number of data-driven projects and initiatives are using customer data. Many of these are AI-focused, where it’s not always clear what customer data is actually being used and accessed by LLM models, either in training or as retrieval-augmented generation (RAG). This increases the complexity and risks of data misuse and unauthorized access.

In addition to these business drivers, the threat landscape keeps evolving, with sophisticated cyberattacks targeted at customer data, which is as valuable to threat actors as it is for the company itself. Adherence to data privacy regulations (such as GDPR, CCPA, HIPAA) and other compliance frameworks also makes using customer data in a secure way more challenging than ever.

Security teams are often thrown right in the middle of this conflict. They can’t practically stop the organization from using customer data. However, keeping customer data secure in the classic way of mapping assets, modeling threats and mitigating them does not even begin to manage the problem.

With all of this complexity, security teams need to shift to a paradigm that will be much more operational and automated, and will help them answer the key questions of Customer Data Protection:

1. How can we effectively manage and protect our growing data assets containing customer data?
2. How can we maintain visibility into who has access to customer data and who is actually accessing it? How can we detect and respond to breaches?
3. How can we ensure that only authorized users have access to customer data, and only in the right context (at the time they need it, for the purpose they need it for)?
4. How can we ensure that our granular security policies are enforced across our organization’s entire data landscape, so that users will only get customer data when they need it?
5. How can we balance innovation with security and compliance, so that we are enablers and not the “naysayers” of the organization?

Security teams use Satori’s Data Security Platform to keep their customers’ data secure, mainly in the following ways:

• Centralized Visibility: Satori provides a unified view of all data assets, regardless of their location or data store technology.
• Automated Discovery and Classification: Satori continuously and automatically identifies and categorizes sensitive data, such as PII, PHI, and PCI DSS.
• Enforcing Granular Access Controls: Satori limits access to sensitive data to only authorized users. This is done in a scalable manner that can even be managed by the data-owning teams or data stewards.
• Detecting and Responding to Threats: Satori provides Data Activity Monitoring across all databases, data warehouses, and data lakes.
• Complying with Regulations: Satori ensures adherence to data privacy regulations like GDPR, CCPA, and HIPAA with reporting and control over access, approvals, and purpose.

• To learn more, you can set a personalized demo with our data security experts.
• Download our Customer Data Protection checklist, to help build a data security strategy for your customer data.
• Read more about our platform and how it addresses the challenges of protecting customer data.