In today’s virtual work environment, organizations store and access significant amounts of data. With all this data being stored, it is critically important to have reliable data security to prevent it from falling into the hands of malicious actors.
If organizations do not take data security seriously, it can result in sensitive data being compromised. Customers, patients, and partner data may be leaked and compromised by malicious actors, resulting in detrimental consequences for your organization.
Compromised data can result in reputational damage, lost customers, and revenue. Additionally, if your organization needs to maintain specific compliance requirements, compromised data can result in legal ramifications when data is non-compliant.
To avoid these consequences, your organization should incorporate data risk management procedures into your data security. However, it is important to understand the best practices for data risk management so you can ensure you have the most reliable data security to prevent data breaches.
COEO understands how important data security is and has helped thousands of organizations with data risk management to secure their data and prevent malicious actors from compromising data.
By the end of this article, you will understand what data risk management is, the best practices for data risk management, and whether it is appropriate for your organization.
What is data risk management?
Data risk management is the set of processes and workflows used to identify and mitigate risks and vulnerabilities within data security.
Data risk management consists of various elements including risk assessment, data governance, risk mitigation strategies, and monitoring and reporting tools to protect data from security risks.
Effective data risk management requires organization-wide alignment. If your team isn't fully committed to the established processes, there's a higher likelihood that protocols will be inconsistently followed, leaving gaps that can expose your organization to data security vulnerabilities.
If you would like to speak with our team to learn more about data risk management best practices or ask any questions you may have, you can schedule an appointment.
Best practices for data risk management processes
Data risk management processes vary widely depending on industry requirements and the size of the business.
While no single playbook exists for data risk management, there are best practices your organization should follow to ensure that you have the most reliable and updated data security. The best practices for data risk management processes include:
Frequently assess data risks
Data risks have become a dynamic threat with data constantly growing and changing.
For this reason, it is important to frequently assess the data inventory and review assets such as the data value, location, type, and potential vulnerabilities of these assets. These assets are needed to identify what risks exist and the consequences they may have on your data.
Businesses use data assessments to design and implement data security protocols to mitigate risks and potential threats and improve data protection.
It is also important to educate employees and raise awareness of the importance of data security and data risk management policies.
Develop data access control polices
Another best practice for data risk management is integrating data access control policies that authenticate and limit employee access to data. Incorporating zero-trust policies can help limit and authenticate data access.
Zero-trust requires verification from anyone attempting to access data by using Role-Based Access Controls (RBAC) and Multi-Factor Authentication (MFA).
Role-based Access Controls grant access to data based on the role or position of an employee within the company. Multi-Factor Authentication requires a user to authenticate their device in two or more ways to access data.
These strategies include SMS or phone call verification, PIN code, or email verification. Multi-factor authentication enables your organization to confirm that the end user logging into the network is verified and proves who they say they are to prevent malicious actors from accessing the data.
Monitor and log data quality and access
With so many employees accessing and sending data, it can easily be altered and corrupted unintentionally, even during legitimate access.
To avoid this, it is important to regularly monitor data quality and update when necessary to ensure data remains complete, consistent, and accurate.
Monitoring and logging data access gives visibility into what is being accessed and by whom to help match users to data quality issues.
AI leverage
Another best practice for data risk management is to leverage AI technology. AI technology has become much more prevalent for this purpose over the last few years.
Using AI technology to support your data risk management policies automatically analyzes data quality, reviews access patterns, and spots unusual behaviors that threaten data access and security.
Advanced AI platforms may also alert administrators and automatically take action to mitigate risks and potential threats.
Is data risk management appropriate for your organization?
Now you understand what data risk management is and the best practices for data risk management. However, it is also important to understand whether data risk management is appropriate for your organization.
If your organization stores significant amounts of sensitive data such as patient, customer, or partner information, data risk management processes and strategies may be appropriate for your organization.
If your organization is concerned about potential data security vulnerabilities or has previously discovered vulnerabilities within your security operation, data risk management may be appropriate.
However, if your organization does not store a significant amount of data in its network infrastructure, it may be harder to cost-justify incorporating risk management policies into your data operations.
Determining whether data risk management is appropriate for your organization
Now you understand what data risk management is, the best practices for data risk management, and whether it is appropriate for your organization. This will help you determine whether your organization should implement data risk management policies.
No organization can afford to have sensitive data exposed and compromised by malicious actors. If your organization has patient, customer, or partner data that is compromised, it can result in major consequences.
Consequences such as damaged business reputations, legal ramifications caused by non-compliance, lost revenue, and lost customers can significantly hinder the success of your organization.
If your organization is looking to use data risk management policies to prevent data breaches and the ramifications that come with them, it is also important to understand data risk management best practices so you have the most reliable data security.
COEO understands how important it is to secure sensitive data and has helped thousands of organizations with data risk management to help improve data security and prevent data breaches.
If you would like to speak with our team to learn more about data risk management best practices or ask any questions you may have, you can schedule an appointment.
Now that you understand the best practices of data risk management, read this article to learn what data loss prevention is and whether it might be beneficial for your organization:
