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Originally published by New Context.

Controlling enterprise data is much more challenging today than it’s ever been. Juggling the many compliance regulations, along with the massive stores of information, is an extensive task. However, dynamic data compliance solutions leverage automation to simplify management.��

Of course, one can’t just jump into automation. Companies have to build a data platform that supports it. The right level of preparation will help firms develop solutions that are flexible, integrated, and simpler to implement on a mass scale.��

Challenges in Data Compliance��

Challenges in modern data compliance vary and can change by industry. However, regardless of the type of data or its use, three issues will impact all firms:

Regulatory compliance Observability Segmentation Firms have to stay up to date with a wide range of ever-evolving requirements both in the US and abroad. Consider the General Data Protection Regulation (GDPR) of Europe. Any contact with a European customer will trigger compliance with this act. Meanwhile, in the US, compliance provisions for data can occur at a state level—like the California Consumer Privacy Act (CCPA). The act creates certain protections for residents of California, so companies must be prepared to maintain those standards for these specific customers. This problem with data compliance in regulatory issues is that there is no one broad standard to impact all customers. Instead, the firm will have multiple groups of clients covered by a varying level of regulations based on their location, age, and other personal information. On top of that, all these regulations can change at a moment’s notice. Juggling this can be a herculean task that requires some level of automation. Data is a double-edged sword for businesses. While it allows companies to know their customers on a deeper level and provide a better experience, it’s also a massive management issue. Every second, . All this collected data requires protection, but the sheer volume makes this seem like an impossible task. Observability—or the ability to see who is accessing data and why—is a challenge, as this could generate logs that are hundreds of pages long. The excess of information makes observability near impossible without some way to automate the detection of anomalies that indicate inappropriate access. Of course, this is especially challenging in the face of unstructured data, which can be difficult to tag and monitor. Today’s data isn’t centralized, nor is it accessed in a single space. The popularity of “bring your own device” policies expands the resources of organizations, but it also creates massive security gaps. An unsecured personal phone or laptop could provide a pathway into secure company networks. It’s estimated that . While this number has gone down in recent years, it’s still concerning. The increased work from home policies seen following COVID-19 will likely only aggravate the issue. Companies may establish rigorous policies with regard to using personal devices on internal networks. However, if there isn’t a way to enforce data security compliance, the risk of a potential breach remains high. Also, accessing customer data on personal devices may run afoul of certain compliance regulations. ��

Data compliance solutions must include some level of automation to manage requirements. However, automation leaves significant gaps as there is no human oversight to double-check issues. To accurately leverage automation in data management, companies must take a more holistic approach to their security.��

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Data Compliance �ֲ���ýapp���°汾 for Common Issues

is something that requires a proactive approach. The right coding and criteria early on make data simpler to manage for the long term. It helps companies identify and address the needs of information while building a scalable solution that grows. Here are a few vital parts of the strategy:��

• Data tagging: Tagging data with the appropriate levels of privacy, compliance needs, and specific criteria groups make it easier to organize. This step is the first to automation.
• Encryption and restriction: The ability to connect external hard drives or personal devices requires default restrictions. Only after the device has been vetted and the user notes their understanding of the requirements should it be allowed to connect. Also, all data should be encrypted and anonymized to ensure that if a bad actor accesses it, it will not be usable.��
• Flexible architecture: Infrastructure as code replaces old static solutions by making the infrastructure something that can grow with the needs of the company. The basis of this lies in reproducible infrastructure built from code that is standardized and easy to update.��
• Built-in compliance: Any compliance regulations should be built in at a system level to allow for automated enforcement and warnings in the event of inappropriate access or transmission. This can work in conjunction with data tagging by applying specific compliance measures only to the records appropriately marked.����
• Collaboration: Data teams should work closely with development teams to ensure the needs of data are built into any solutions from the bottom up. As data drives business decisions, information security teams should be heavily involved in its use in company initiatives.��
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These various protocols help to support automated data compliance solutions for the long term. With scalable, observable options, companies overcome common pain points and protect their customers’ private information.��

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