While it’s important to implement processes and procedures that safeguard data security and privacy, you can also focus on more strategic data governance goals.

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An organization’s data is one of its most precious assets: It must be properly stored, used, protected and analyzed to get maximum value from it.

Without the proper data management strategies in place, sensitive business and consumer data is more easily exposed to security threats. One of the most important elements of data management and security strategy is a data governance framework.

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What’s the difference between data governance, data privacy and data security?

Data governance

Data governance refers to the system of policies, rights, responsibilities and procedures that are created and used to control data assets. The objective of data governance is to minimize risk, increase the value of data, meet regulatory compliance requirements, establish rules for data use within the organization, and improve external and internal data communication.

SEE: The Data Protection & Privacy Bootcamp Bundle (TechRepublic Academy)

Organizations have two main options for their data governance models. They can use a passive data governance model, in which the data is input into the system and then all verification and data cleansing procedures are performed. Or they can use an active data governance model, in which data is verified and cleaned before it is input into the system.

Data security

Data security involves creating a balance between security and data usability, using a technical framework to keep data secure from external hackers, accidental threats and malicious insiders. Although data security refers to the protection of data assets, it does incorporate some aspects of overall infrastructure security, as the security of data is ultimately reliant on sound underlying infrastructure.

Organizations have access to multiple types of data security tools to detect, identify, protect against and respond to threats. These solutions include multi-factor authentication, firewalls, antivirus software, backup and recovery software, security information and event management solutions, and data loss prevention solutions.

Data privacy

Data privacy strategies help organizations determine what data can be shared with third parties and how it is shared. Data privacy is also referred to as information privacy. One of the major components of data privacy is access control, which can be done through various tools, policies and procedures. Along with access control, an organization can use mechanisms to prevent unauthorized access to data.

Regulatory compliance is an important element of data privacy, as an organization must meet its legal obligations to keep data safe. This includes how they store, process and share their employee and customer data. If an organization fails to meet compliance requirements, it can face significant fines. Some of the most common regulations that relate to data privacy are the CCPA and the GDPR.

How data governance influences data privacy and security

As data governance frameworks support the integrity and overall quality of data, data governance initiatives have a major influence on data privacy and security. Although data governance, data security and data privacy are distinct concepts, they share the common goal of getting the most value from data and making it accessible to the organization.

Even though data governance is primarily a strategic concept, it still defines actionable processes and procedures that should be followed to get the maximum value from data while continuing to protect it. This helps the organization achieve data privacy objectives such as reducing the risk of data loss, theft or misuse.

SEE: The Data Protection & Privacy Bootcamp Bundle (TechRepublic Academy)

Detecting, identifying and responding to data security issues require collaboration, communication and organizational alignment. Data governance can help align resources that are used for data security so organizations can respond to threats without delay.

Best practices for data governance in cybersecurity

Data governance has evolved from being a choice to become a necessity for successful organizations, and one of the reasons for this change is cybersecurity. Through proper data governance, organizations can prevent and neutralize attacks, anticipate threats and recover from data breaches more quickly.

When setting up your data governance strategy, it’s important to understand how your plans fit into your overall cybersecurity strategy. Here are some best practices for applying data governance to cybersecurity needs in your business:

Take control of data privacy

Although organizations understand the importance of data privacy, they often lack the tools or strategy to implement data privacy processes. Consider investing in data governance tools, especially those that specialize in your industry’s or region’s specific privacy laws. This step helps organizations to remain compliant, maintain consumer trust and ensure top-tier security.

Identify vulnerabilities

In order to identify existing organization vulnerabilities, data and security teams should conduct regular audits of data and other sensitive business assets. A comprehensive cybersecurity audit should be able to identify existing threats and determine potential vulnerabilities for future threats. Once threats have been identified, data governance best practices can be used to establish policies and procedures that defend critical assets.

Boost data visibility

With improved data visibility, organizations can get better at data management, which helps them meet their cybersecurity objectives at a data level. When organizations know where and how their most sensitive data is stored, they can develop a more effective strategy for how they will react to security threats in real time.

Data governance tools are increasingly becoming an important part of enterprise security strategy. Learn about the Top data governance tools here.