Keep your Data Processing Compliant with Data Audits

A data audit is an important part of any organization’s governance, risk management, and compliance (GRC) program. It helps ensure that data is accurate and complete, and that it is being used properly. A data audit can also identify areas where improvements can be made. There are many different ways to conduct a data audit. The most important thing is to have a clear understanding of the organization’s goals and objectives, and to design the audit around those. There are a number of different data audit frameworks that can be used, and the choice of which to use will depend on the organization’s specific needs. A data audit is auditing and reviewing the data processing activities for compliance with prescribed standards. Let’s take a more closer look into the importance of data auditing.

Why do we need a Data Audit?

Businesses can use data auditing to achieve:

• Data audits allow you to spot flaws in your data management system while ensuring that all data-related procedures comply with organizational policies and guidelines.

• By conducting an effective data audit program you will ensure your organization’s continued compliance with regulatory requirements such as Sarbanes-Oxley Act (SOX), HIPAA Privacy Rule, etc.

• It improves access to quality data by employees and customers of the company.

• It ensures transparency between the organization and the public.

• It helps to examine the behavior of third-party applications.

How do you conduct a data audit?

• 1. Determine stakeholder: Finding out where data is stored is crucial. It’s essential to figure out which stakeholders are knowledgeable about how data is collected, stored, and used at this point. They supply the most accurate statistics and information about the organization.

• 2. Define data storage: Identify where data is stored either in a single channel or shared across multiple departments.

• 3. Connect data repositories to business operations: Having a merged data repository allows businesses to make faster decisions, be more productive, and have more educated staff.

• 4. Establish measures for data quality: Data quality measures should meet six dimensions that should be distinctive, complete, uniform across all departments, timely, valid, and correct.

• 5. Formulate policies for monitoring and adherence: Data policies should be in place to monitor the adherence to internal standards to ensure compliance with internal corporate policies and processes.

• 6. Documentation: The value of thorough documentation in data auditing cannot be overstated. All policies, actions, and data changes must be adequately documented during data discovery, data collection, storage, and updates.

Data Audit Framework

• (i) Audit Preparation: The audit’s aim and objectives are specified during the planning stage. To maximize hours spent with the organization’s employees, a preliminary study is undertaken and sessions are established.

• (ii) Asset identification and classification: this stage aims to identify all data assets and classify them within the data audit’s scope.

• (iii) Assessing the management of Assets: the stage before these feeds into this one because it helps to point out gaps in how assets are managed.

• (iv) Reporting and recommendations Auditors can make recommendations based on the outcome of the above stages.

Open Source Auditing Tool: Open-Audit

Open-Audit is an open-source audit management solution that enables businesses to provide precise asset location data in seconds.

See also

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