Know Everything about Data Risk Assessment

In today’s digital world, safeguarding sensitive data is crucial for businesses. One key aspect of data protection is conducting a thorough Data Risk Assessment (DRA). This comprehensive guide will walk you through the importance of DRA, its benefits, and a step-by-step process to conduct one efficiently.

Understanding Data Risk Assessment

Data Risk Assessment is a systematic process that entails reviewing, analyzing, and evaluating the locations where sensitive data is stored and managed. This data can include intellectual property, personally identifiable information (PII), and other critical business information.

The main objective of a DRA is to identify potential risks to sensitive data and implement appropriate measures to mitigate these risks.

Importance of Data Risk Assessment

Conducting a Data Risk Assessment is vital for several reasons:

  • Visibility: A DRA provides insight into all potential threat vectors that could lead to security or privacy violations, ensuring you know exactly what data you have and where it is stored.
  • Risk Management: Identifying and assessing the risks associated with managing PII and other sensitive data enables you to make informed decisions about data security investments and risk tolerance.
  • Compliance: A DRA helps you maintain and demonstrate compliance with legal, regulatory, and industry-standard requirements.
  • Vulnerability Analysis: By conducting a DRA, you can identify potential vulnerabilities that may increase the likelihood of data leakage or breaches.
  • Security Metrics: With a DRA, you can establish key performance indicators (KPIs) for your data security efforts, allowing you to track progress and make improvements.

Primary Steps in Data Risk Assessment

A comprehensive Data Risk Assessment typically follows a three-step process:

1. Map Data to Applications

The initial step in a DRA involves gaining full visibility into all data stored, collected, and transmitted by your organization. This process is known as creating a data footprint. Key elements to define during this step include:

Data Owners/Data Stewards

Identify individuals responsible for the collection, protection, and quality of data within a specific department or domain.

Data Types and Attributes

Identify and tag sensitive files with classifications to enhance controls.

  • Data Classification

Determine the risk level and potential impact on the organization if data is compromised.

For effective data classification, consider assigning risk levels such as high, medium, or low, and classification categories like:

  • Restricted

Data whose unauthorized disclosure, alteration, or destruction poses a high level of impact on the organization.

  • Private

Data that is only to be seen by a selected few eyes. Unauthorized disclosure of this data could lead to fraud, and significant damage to the organization and consumers. 

  • Public

Data whose unauthorized disclosure, alteration, or destruction poses a low level of impact on the organization.

Once you have covered all the responsible parties and the level of risk associated, you need to map the data to the apps that use it. This mapping should include:

  • Applications: A list of applications that query or use the data.
  • Data Environment: Geographic locations or regions where data is stored.
  • Data Flows: The path data takes between applications, databases, and processes.
  • Controls: Security measures used to protect the data in question.

2. Assess Risk

This stage involves reviewing, analyzing, and evaluating threats and vulnerabilities that could put data at risk. Risks to consider include:

  • Excess Access: Users who have more access than necessary to complete their job functions.
  • Outdated User Permissions: Users who retain access from previous roles within the organization and no longer require that level of access.
  • File Sharing: Permissions allowing access to data by anyone with a link.
  • Collaboration Tools: Sharing data through chat tools like Slack or Microsoft Teams.

Automated solutions can help streamline the risk assessment process by scanning data repositories and analyzing data storage, handling, and security processes, practices, and controls.

3. Remediate Vulnerabilities

After assessing potential risks, it is essential to mitigate these risks by addressing the identified vulnerabilities. Some remediation activities include:

  • Principle of Least Privilege: Ensure users have the least amount of access needed to complete their job functions using role-based access controls (RBAC) and attribute-based access controls (ABAC).
  • Multi-factor Authentication (MFA): Implement additional authentication controls around sensitive data, including step-up authentication when users move between applications and modules.
  • Data-centric Security Policy: Focus on securing sensitive data types with policies and controls that consider business context and data transmission across applications and storage locations.

Transitioning from a traditional security approach to a data-centric security approach can be challenging. 

However, with distributed workforces connecting to your data from the public internet, securing the transmission itself is crucial. This can be achieved using a virtual public network (VPN) or Secure Access Service Edge (SASE) to protect data in transit.


Performing a comprehensive Data Risk Assessment is crucial for any organization to safeguard sensitive data and maintain regulatory compliance.

The three-step process outlined in this guide will assist you in identifying potential risks, mapping data to applications, assessing vulnerabilities, and implementing effective remediation strategies.