Data access governance (DAG) is a structured approach to creating and enforcing policies that control access to data. It’s an essential component of an enterprise’s overall data governance strategy.
DAG is like a home security system that uses a combination of smart locks, door sensors, and motion detectors to limit and track access to your home. Just like a security system, DAG uses access control policies and technologies to manage and monitor who can access your enterprise’s sensitive data, when, and what they can do with this access. By limiting data access, DAG ensures data security, privacy, and compliance.
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The importance of DAG
With no way to physically stop unauthorized entities from accessing cloud data, DAG has become an absolute necessity for most organizations. But why the need for DAG when there are traditional data governance methods?
In cloud environments, traditional data governance methods often fall short. After all, cloud data is constantly in motion, and the static data access controls of traditional approaches simply can’t keep up.
Plus, enterprises today generate—and have to secure—massive volumes of sensitive data on a daily basis, most of which are spread across regions and clouds. Ever-changing data privacy regulations are another major hurdle to overcome. And when it comes to keeping tabs on data access with all these fast-evolving changes, traditional data governance methods just don’t cut it.
That’s where DAG comes in. DAG establishes a structured approach that provides complete visibility into data access and enforces dynamic access controls across disparate cloud environments.
Data governance is particularly crucial in industries that handle sensitive information and face stringent regulatory requirements.
The banking and finance sector prioritizes data governance to ensure compliance with regulations like GDPR and Basel III, protect against fraud, and maintain customer trust.
Healthcare organizations also place a high emphasis on data governance due to the sensitive nature of patient information and the need to comply with laws such as HIPAA.
The retail industry focuses on data governance to protect customer data, including payment information and purchase history, which is essential for maintaining customer loyalty and preventing breaches.
The public sector prioritizes data governance to safeguard critical citizen information and national security data, ensuring public trust and adherence to strict security protocols.
Benefits of Data Access Governance
1. Security and compliance
DAG protects sensitive data by enforcing policies that comply with privacy laws (think GDPR, CCPA, HIPAA, and other data protection standards), a concept referred to as data security compliance. For example, enterprises can secure data by applying multifactor authentication (MFA), which authenticates users in two or more ways before granting them access to sensitive data.
Simply put, implementing DAG lessens the risk of unauthorized access, data breaches, and resulting fines and lawsuits. DAG also helps organizations meet compliance requirements by maintaining transparent audit trails of data access permissions.
2. Risk mitigation
By implementing controls to prevent unauthorized access, data access governance limits potential exposure to insider threats, accidental data leakage, and cyberattacks. It also monitors and logs data access to help organizations uncover and resolve access-related risks like authentication bypass and weak access controls in real time.
3. Operational efficiency
DAG contributes to streamlined operations by ensuring users and apps have no more and no less than the access needed to complete their tasks. This prevents excessive data access, data sprawl, and data management bottlenecks.
Key components of DAG
As we’ve seen, data access governance is all about ensuring secure data access. To do this, DAG spans key principles and components, which include:
Data classification refers to categorizing data based on its sensitivity and importance (e.g., public, confidential, restricted) so you can formulate proper access control measures and data protection efforts. To know how sensitive a dataset is and how strict its access permissions must be, just think of the consequences if it’s breached or accidentally leaked.
Access controls involve implementation and enforcement. You might choose access control models like role-based access control (RBAC) or attribute-based access control (ABAC). These methods rely on policies that clearly define roles, responsibilities, and appropriate permissions.
Least privilege ensures users are granted the minimum level of access required for their job roles, reducing exposure risks.
Visibility allows you to identify risks, detect unexpected access patterns, and resolve excessive permissions—in other words, it’s kind of like a watchtower. Similarly, monitoring in DAG is like using a security camera to capture events as they unfold. Monitoring lets you track and keep data access histories that provide valuable insights for incident response.
Auditing and reporting involve keeping detailed records of data access, permissions, and data modifications to meet compliance requirements, facilitate incident investigations, and demonstrate accountability.
How data access governance works in the cloud
With huge data volumes spread across multi-cloud environments, organizations often face challenges with accounting for where data is and who has access to it. On top of that, enterprises configure and continuously review more than 40,000 permissions across multiple clouds. The end result? DAG can be a long slog if you don’t have the right data governance framework.
Here's how an ideal cloud data access governance framework works:
1. Continuous data discovery and classification
Successful DAG starts with using automated data discovery tools to figure out what data you have. Afterwards, you can create a data classification framework that is consistent across all your environments, using uniform tags to make each data category easy to identify.
Next, you can deploy data access governance solutions to continuously classify sensitive data across your environments, identifying personally identifiable information (PII), protected health information (PHI), payment card information (PCI), and other critical information.
2. Policy creation
With sensitive data properly accounted for, create detailed lists of roles, apps, and teams; the datasets they require access to; and why they need access to these datasets. Using this information and leveraging built-in policies in DAG tools, create DAG policies that work for your unique needs.
Limit user access to specific durations and specify conditional access rules.
3. Policy enforcement
Once you have classified your data and configured policies, the next step is to enforce access policies using DAG tools. It’s a good idea to implement measures like RBAC, ABAC, MFA, and zero trust.
4. Risk management
At this point, you’ll use DAG solutions to continuously monitor the security posture of data assets while assessing risks associated with data exposure in real time. This includes spotting poor authentication, identities with privilege escalation abilities, misconfigured security groups, and other risks that allow for lateral movement to sensitive data.
5. Compliance assessment
Enterprises must demonstrate compliance with various regulatory standards. DAG solutions help here by enforcing appropriate access controls, automating compliance assessments, and providing audit trails that are critical for compliance checks.
Integration with security technologies
A robust data access governance framework cannot stand alone. It must be integrated with various security technologies for effective detection, correlation, and resolution of risks associated with cloud and network access activity:
DSPM tools help you enforce DAG policies.
CSPM platforms correlate cloud and data risks to uncover toxic access combinations that put data at risk.
CIEM solutions provide insights into all access entitlements in enterprise clouds to detect and respond to over-permissioning and associated risks.
10 best practices for effective DAG
Following these best practices will help ensure your is DAG effective:
Define a clear data governance strategy. Be sure to include the goal of your DAG program, the policies your DAG tool will enforce, guidelines for employees accessing data, and specific responses for various unauthorized access incidents.
Classify and categorize data according to sensitivity, compliance obligations, and business significance. Data in the cloud moves around a lot, so you want to make sure sensitive data isn’t where it shouldn’t be.
For example, to protect your business and prevent your trade secrets from being stolen, intellectual property can’t be lumped in with non-sensitive data in storage buckets with lax access controls. Another example? To comply with GDPR, your EU customers’ data must remain within EU borders.
Adopt zero trust and the principle of least privilege (POLP). Grant data access strictly on the basis of job requirements to reduce the risk of over-provisioned identities. Even after these measures are in place, continuously re-authenticate entities at various points in your network to lower the risk of breaches.
Apply MFA at all data access points. Mutli-factor authentication methods such as passwords, OTPs, and biometrics prevent attackers from accessing sensitive data using stolen credentials. Establishing strong access controls will also help so that only authorized personnel can access data.
Implement RBAC and ABAC. RBAC limits user access based on roles rather than assigning permissions to individuals (whose access requirements will change as their roles evolve). This will help you maintain consistent access controls across all environments and reduce the risk of excessive permissioning.
ABAC lets you enforce even more granular access controls, for instance by specifying what kind of access—read or write—a user role has to specific datasets. For instance, a doctor may be allowed write access to patients’ current diagnoses and read access to their medical histories.
Continuously monitor access activities and review permissions. Access monitoring helps you track data usage and detect anomalies so that you can respond quickly to cyberattacks. Monitoring insights can also help spot entities with excessive access, inactive users, and shadow permissions. This enables data governance teams to discover, revoke, and downgrade access rights as required.
Audit user activity to support compliance and improve incident detection response times.
Automate access provisioning and policy enforcement so that your DAG policies are consistent across the board. Automation minimizes the risk of human error and improves response time.
Understand and tailor DAG policies to regional and industry-specific regulations. Use a DSPM tool that automatically picks up on new changes to regulatory standards and catches compliance failures in your stack.
Encrypt data in transit and at rest. If all your other access controls fail, encryption will have you covered.
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The role of data security posture management (DSPM) in DAG
DSPM is basically the GPS of your effective data access governance framework. It tells you where your data is, if it’s within secure domains, who has access to it, and what they are doing with it in real time, helping you navigate the bumpy roads of DAG in the cloud.
Here’s how DSPM fits into DAG:
Identifying and classifying data: DSPM tools automatically discover and classify data based on sensitivity, whether it's PII, PHI, or related to trade secrets.
Enhanced visibility and control: DSPM platforms provide a comprehensive view of data access and security posture, identifying who has access to what data and where potential risks are.
Policy implementation: Once policies defining various data sensitivity levels and corresponding access controls are in place, DSPM tools’ automated policy implementation capabilities free security teams from the burden of manually implementing policies.
Real-time threat detection: DSPM solutions continuously monitor for misconfigurations, over-permissive roles, orphaned permissions, unusual access patterns, privilege escalation, and more, allowing organizations to detect and address risks before they escalate.
Compliance alignment: DSPM helps maintain compliance by ensuring access governance aligns with industry regulations and standards, reducing the risk of non-compliance and associated fines.
Streamlined incident response: Using a DSPM-as-part-of-CNAPP solution helps you correlate risks by offering better, more accurate risk detection. These correlated insights help streamline incident response and speed up MTTR.
How Wiz DSPM enhances DAG
Data discovery is the foundation that makes or breaks every data access governance framework, and this is where the Wiz DSPM and CIEM combo outpaces other DSPM solutions.
The Wiz agentless data discovery engine uncovers organization-wide sensitive data—structured, unstructured, and shadow data alike—quickly and without requiring manual tuning. Its built-in controls give you a headstart over attackers, letting you trigger automated responses for access breach issues.
Between the Wiz data lineage mapping and the Wiz Security Graph, you can:
Enforce consistent policies across all environments with a single click
Trace entities with access to data on one screen
See what changes various entities have made to data over time.
Not only does this allow you to seamlessly troubleshoot incidents; it also provides audit trails for meeting compliance requirements.
Wiz lets you correlate data risks from code to cloud. Our DSPM-as-part-of-a-CNAPP approach allows you to instantly see all misconfigured permissions putting your stack at risk, from your software development to your runtime environments. Wiz DSPM then prioritizes these risks by criticality and business impact, empowering teams to resolve the most critical first.
Wiz also extends data access governance to AI training data. Wiz seamlessly discovers and prevents unauthorized access to sensitive AI data so you can steer clear of data poisoning attacks, prompt injection vulnerabilities, and other AI-data access risks. And our features don't end there.
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