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Data Access Governance

What is Data Access Governance (DAG)?

The short definition: Data access governance refers to management and control of who has access to what data in an organization, and what they can do with it.

Data Access Governance

Data Access Governance

What You Need to Know

The primary objective of DAG is to maintain the security, integrity, and privacy of an organization's data assets. Many users and applications require legitimate access to data, but being overly-generous with permissions can increase the risk of data breach

Security teams require oversight of access permissions to data in order to ensure these are granted according to the principle of least privilege. This oversight requires tools that allow them to:

  • Identify, classify, and monitor access to sensitive data
  • Understand which users, applications, and systems have permission to view or modify sensitive data. 
  • Implement policies and procedures that limit access.
  • Maintain a clear audit trail and accountability of historical permissions to data assets.

>> Read our blog: Solving Cloud Data Access Challenges with DSPM

Data Access Governance in Compliance and Auditing

Businesses need to comply with various data protection and privacy regulations such as GDPR, HIPAA, and PCI-DSS. These frameworks often impose strict requirements on how data is accessed, stored, and processed. 

Key aspects of DAG in compliance and auditing include:

  • Identifying sensitive data that requires stricter levels of access control.
  • Applying granular access controls that align with regulatory requirements.
  • Monitoring and auditing access to detect potential violations.
  • Mapping and creating reports on access controls ahead of an audit

DAG in Cloud Data Security

The cloud makes data access governance more difficult to manage due to data sprawl, permissions sprawl, and complex multi-cloud architectures. However, access data governance is an essential component in cloud security, since unauthorized exposure of sensitive data is typically the first step to a cybersecurity attack (such as ransomware or IP theft).

From a security aspect, effective data access governance includes:

  • Mapping access to sensitive data across multiple cloud services to ensure that only authorized users and systems can view, modify, or share the information.
  • Monitoring and detecting unusual access patterns or data movement that may indicate a security breach or insider threat.
  • Implementing consistent policies and procedures for managing access permissions across different cloud environments and platforms.
  • Maintaining a holistic view of data access across the organization, enabling security teams to effectively prioritize risks and respond to incidents quickly.

Software Used for Data Access Governance

Different tools and software solutions can help organizations implement effective data access governance by providing visibility, control, and reporting capabilities. Some popular software used for DAG include:

  1. Data security posture management (DSPM) tools: DSPM solutions provide comprehensive visibility into sensitive data assets, roles, and permissions across multiple cloud environments. They also help prioritize and manage access risks and streamline governance-related tasks. Dig Security incorporates DSPM into a broader data security platform.
  2. Identity and access management (IAM) tools: These tools enable organizations to manage user identities, access controls, and permissions across various systems and applications. They are used to revoke or grant permissions but aren’t designed to give a complete overview of access control, and are not contextually-aware of the data stored in each cloud resource. Examples include Okta, Azure Active Directory, and AWS Identity and Access Management (IAM). 
  3. Data loss prevention (DLP) tools: DLP solutions focus on preventing data leakage, whether intentional or accidental. They monitor, detect, and block sensitive data transmission, often incorporating DAG features to help manage access to sensitive data.