As more companies start to use the cloud, the threat of a data breach and the rules and fines that go with it has only grown. Therefore, companies and agencies need to anticipate and adapt to their changing data and IT landscape. For that, a zero trust approach to data security and privacy might be the ideal framework. Take a look at how combining it with data discovery and classification can close gaps in your walls and help you work more efficiently at the same time.
Why Zero Trust?
A zero trust model operates based on the idea that any user may pose a threat and cannot be trusted. Zero trust principles require ongoing trust checks of users and processes. Those, in turn, are based on context. Without them, you couldn’t have informed user access control and management. This real-time, context-aware zero trust framework ensures that security controls stay at the forefront of your plans. In addition, it takes into account that you’ll want to adapt to the modern hybrid multicloud environment. To support this model, you can start with data discovery and classification.
What Data Discovery and Classification Means in a Data Breach
In the face of internal and external threats, digital defenders need insight into their data. That includes knowing where it is stored, who has access to it, how sensitive it is and more. This lets you establish a baseline against which to measure odd behavior and potential data threats. From a data privacy standpoint, you also need to understand how personal data is being used and protected. That’s key in order to meet compliance needs. These might range from providing proof of adequate data protection controls to meeting data subject access requests. That’s a task that is made much easier when you know where your data is!
Data security and data privacy are closely related. Namely, data security is an essential technical layer to a successful strategy against a data breach. For both, data discovery and classification provide visibility into known and unknown areas of risk and exposure. Sadly, we cannot simply trust users or depend on them to report problems. As a result, businesses need to rely on tech to fill in the gaps.
Finding the Best-in-Class Data Discovery and Classification
With the right data discovery and classification solution, you can pinpoint sensitive and personal data on an ongoing basis. You can also monitor for changes to and maintain a catalog of that data. Data discovery should extend to data at rest and in motion, as well as structured and unstructured data. It should be able to uncover both known and unknown data lakes. That means all variations of data and not just the ones that an admin instructed the solution to discover and classify.
It’s not all about protecting against a data breach. Sometimes it’s about keeping data neat and at hand. You want to stay on top of an evolving data landscape in an ongoing manner. But that’s beyond even the powers of the largest teams without the right tools. You need a solution that can do a lot of different things. At the same time as it’s locating sensitive data in unknown locations, it needs to monitor the transfer and copy of personal data and classify a wide range of data types. After all, that’s how you get a complete, accurate and sustainable view of the data lineage or lifecycle.
One way to do this is with a zero trust data discovery solution that leverages artificial intelligence and machine learning. With it watching network transactions to find unknown personal data, your team will be better positioned to locate sensitive data and use this insight to take informed actions.
What You Need in a Data Breach
With a complete data inventory or catalog, you gain a view into the true risk facing sensitive data. From there, it’s easier to better rank and triage fixes. Whether these actions or controls come in the form of data activity monitoring, data encryption, data security analytics or response orchestration, data discovery and classification can help guide efforts to reduce data risk and address audit and compliance needs.
By having the right context, you can respond to incidents and customer requests alike in a much more streamlined and efficient way. For example, in response to a data breach, a data discovery and classification solution working with a data monitoring and analytics solution can provide much-needed contextual insight into affected personal data, the data subject and the various privacy laws by geography. This insight provides a more efficient response and a shorter time to resolution.
In another example, customers may request to know what personal data a company or agency has collected on them. To respond effectively, the data holder must have an automated and continuous method to discover, track, catalog and aggregate data according to the subject or customer. A well-maintained and dynamic view of the personal data landscape is also critical. After all, the group holding the data will need to execute data subject access request workflows within a reasonable and compliant timeframe.
In conclusion, a zero trust approach to data privacy and data security starts with sustainable and automated data discovery and classification. This crucial first step can protect organizations from cybersecurity threats, a data breach or regulatory non-compliance. It helps to ensure data privacy and security, while applying zero trust principles, by more precisely locating and identifying sensitive data so that security and privacy gaps are duly addressed.
Learn more at our webinar, “Fearless with zero trust: Data discovery and classification for privacy and security,” on July 28, 2021 at 11 a.m. EDT.
The post How Data Discovery and Zero Trust Can Help Defend Against a Data Breach appeared first on Security Intelligence.
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Author: Cynthia Luu