From Chaos to Control: Solving the Data Security Puzzle

In the last few years alone, the task of securing customer data and other sensitive data has shifted dramatically, having gone from hard to borderline impossible. The hardship isn’t just because companies are dealing with 10x more data – the data is also stored in more data stores, used by more tools, and involved in more use cases than ever before.

It feels like every time you nail down a process to keep risk in check, something in the process needs to change. Sometimes it’s a minor tweak, but lately, these changes have been fundamental, forcing you to rethink your entire approach to data security.

The good news: by the end of this article, you’ll have a clearer view of what’s changed and how you can adapt.

Securing data, especially customer data, is harder than ever. According to the IBM 2024 Data Breach Report, customer data is involved in 43% of breaches. Many security teams still struggle to keep up, and not for lack of effort.— The reality is that while data technologies and use cases have gone through a transformation over the past few years, our processes and tools to secure data have only adjusted incrementally.

Security teams often follow what feels like the most logical plan:

1. Map everything: users, data, permissions, configurations, etc.
2. Identify all risks.
3. Implement controls.

This looks like a great plan. However, in practice, most organizations “run out of gas” doing step one, and are left with mediocre solutions for identifying risks and implementing appropriate controls. Mapping everything sounds straightforward, but when you’re dealing with rapidly changing data flows, cloud environments, and constantly shifting user roles, that map becomes obsolete before you even finish it.

Even when teams manage to get through the mapping stage, they often end up with a massive list of data tags and permissions that quickly become unmanageable. Worse yet, you may have all the visibility you need, but lack the ability to enforce controls based on that visibility. It’s like knowing where the fire is but not having the tools to put it out.

This is where many security teams get it wrong. They assume that if they can just map everything they’ll have no blind spots and can put a program in place to address the risks. But the pitfall is that visibility tools are not built to take you to the next step. If your security solution can’t seamlessly combine visibility and control, you’re always going to be two steps behind. You need both—integrated into a single approach—if you’re going to secure data at the speed of business.

Traditional security strategies were built for a different era. Back then, data lived in predictable places, access was controlled by a few gatekeepers, and security was about putting walls around the fortress. But today’s data environment looks nothing like that. Data is no longer siloed. It’s stored in more locations (across clouds and technologies). Data is also used by many more teams instead of a chosen few, and for a multitude of use cases. In many cases, AI has allowed teams to access data in unpredictable ways, often creating a black box situation. This is boosting the tech economy right now, but sends a strong message to security teams: the old processes and tools are becoming irrelevant, and you must adapt.

So when security teams stick to the old “map, identify, control” model, they end up running in circles. By the time they’ve mapped their data, someone’s spun up a new cloud instance or started using a new tool that wasn’t part of the plan. The result? You’ve got a security process that can’t keep up, and risks keep slipping through the cracks.

The best example in my eyes is AI adoption using LLMs (Large -Language Models). AI requires very large datasets to be trained on, which means that a lot of data is now available to many more people. Traditional security approaches weren’t built to handle this level of data access at scale, and they certainly weren’t designed to deal with the constant flux of permissions, use cases, and tools that AI demands. So, security teams either block access (which frustrates users and slows innovation) or allow it unchecked (which increases the risk of breaches).

The way forward is not more rigid processes or longer lists of permissions. It’s about building a dynamic, integrated approach to data security that adapts to your environment in real time. Don’t just tell me where my data is, don’t just tell me where my risks are, solve the problem now.

Here’s what that looks like:

1. Integrated Visibility and Control: It’s not enough to simply know where your data is and who’s accessing it. You need to be able to enforce controls at the same speed that data moves. Whether someone in HR is accessing a report or a data scientist is working on an AI model, you need real-time enforcement that adapts as roles, use cases, and data flows evolve.
2. Adaptive Security: Instead of building static security frameworks that require constant manual updates, look for solutions that can adapt to changes automatically. If a user’s role shifts or a new dataset is introduced, your security controls should adjust without needing an overhaul. This adaptability is critical in today’s fluid data environments.
3. Automation-First: Security teams are under more pressure than ever, and manual processes simply don’t scale. Without automating routine tasks—like monitoring data flows, enforcing policies, and identifying anomalies, you simply won’t keep pace. Automation keeps you from falling behind as your environment grows and changes.

The future of data security belongs to those who anticipate threats, not just react to them. It’s about building security into every layer of your organization, so you don’t have to constantly fight fires. If you’re ahead of the curve, you’re not just protecting your organization—you’re enabling it to thrive.

At the end of the day, your role isn’t just about keeping data locked down—it’s about empowering your organization to grow and move fast, without putting itself at risk. Security solutions should make your life easier, not harder. For data security, that means having integrated visibility and control, delivering a seamless experience for data consumers, and adopting an automation-first approach.