Companies have accumulated a ton of data over the past few years. According to some estimates, we have generated more data in the last two years than all data obtained throughout human history. One of the victims of this massive data collection and processing is consumer privacy. Countries have started passing new privacy laws to protect data and ensure privacy as a fundamental human right of their citizens.
Privacy laws require organizations to take appropriate measures to protect consumer data. Additionally, breaches and privacy violations result in financial damage and cause irreparable damage to a company's reputation and loss of customer trust.
Storage was cheap before GDPR. However, with penalties, data has become risky and expensive to hold. The new privacy laws, breaches, and privacy awareness have forced many companies to rethink their data collection. As a result, companies have to actively manage their data security posture.
Better compliance processes don't mean better data protection. Meeting regulatory requirements is just the first step. As data moves inside the organization, a few managers and policies can't ensure the data will be used correctly. Protecting data from privacy violations and breaches is a much more complex problem requiring technology investment.
As data volume and complexity continue to accelerate inside an organization, delivering data privacy and security is a complex problem. Data security in enterprises has become a multifaceted problem. Here are the reasons why data protection is a complex problem.
De-identifying or deleting all personal data is not practical and defeats the purpose of collecting the data in the first place. Data protection issues remain hidden until a major breach or a privacy incident happens.
When everything is a priority, nothing is a priority. Data mapping tools create a laundry list of personal data. Not every personal data is risky and toxic. A list of sources with personal data is not actionable. Data mapping and other tools are inadequate because they only analyze the content of data, while most of the penalties are caused by data use activities and a lack of proper controls. Data discovery tools and traditional processes aren't designed for data protection at today's scale.
Not all personal data is toxic and poses privacy risks. But data mapping tools can't differentiate between risky personal data and non-toxic personal data. For example, if you have two copies of a data table (A1 and A2), since both the data sets have identical personal data, data mapping tools will flag both tables as toxic. However, these files might have different risk profiles. In the above example, if we find only one employee has access to file A1 while a thousand employees have access to data set A2, then their risk profiles are completely different. File A2 is much riskier than A1. Hence to fully understand your risks, you must analyze context: metadata about data, activities on the data, and controls.
To determine your data security posture, your security team needs reliable answers to the following questions.
Once you understand the current state of your data, you can eliminate hidden issues and avoidable risks that can dramatically impact your data security.
Today, most companies spend their resources inventorying data and securing the perimeter without proper investment and tools to look into data security posture. Protecto (https://www.protecto.ai) is the first Data Security Posture Management (DSPM) solution to control your data security posture. We look deep into data and its context, such as who has access and uses the data to determine data security risks.
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