What Are the Most Common Types of Data Controls?

Data controls refer to the tactics, policies, and procedures that organizations use to meet their data governance and data management objectives. Put another way, they are the rules and systems that businesses rely on to ensure that only authorized users can access their data, ensuring its security and integrity. Data controls can be used to identify risks, track and manage data quality, implement policies, safeguard data, and help remediate an array of potential data security issues.

As such, data controls can play an important role in both risk mitigation and compliance with the latest regulatory requirements that govern their data usage. They’re also critical for ensuring that data is handled properly, as well as for identifying and resolving potential data security issues.

In this blog, we’ll look at the necessity of data controls, the main types you should be aware of, and best practices for implementing them in your data ecosystem.

Why Do I Need Data Controls?

Data controls help organizations protect the sensitive personal data they possess by safeguarding it from leaks and breaches. These controls can be either preventive, allowing you to proactively manage access control or detective, allowing you to monitor data access and usage. Without these controls, incident response would be reactionary rather than proactive, and would likely result in considerable noncompliance penalties.

Having the right data controls in place has become essential for data-driven organizations looking to maintain regulatory compliance and reassure increasingly vigilant customers that their personal data will be safe. Ultimately, data controls are critical for enabling this compliance, auditability, and transparency, as well as for revealing specific risks that organizations are exposed to and how effectively they’re addressing them.

What Are the Main Types of Data Controls?

Before examining the range of different data controls, it’s important to distinguish between data privacy control and data security control. Although these terms are often used interchangeably, they refer to distinctly different things.

Data access control and privacy hinges on ensuring the correct handling, processing, storage, and use of personal information. It remains focused on the rights of individuals with respect to their personal information. By contrast, data security refers to the means, methods, and policies organizations use to secure sensitive personal data. In other words, data privacy is about complying with regulatory requirements, while data security is about taking actions to prevent unauthorized third parties from accessing personal data.

As data use has matured and expanded, access control mechanisms have taken a variety of forms to keep up with the need for security. These include:

  • Role-based access control (RBAC): An approach to data security that permits or restricts system access based on an individual’s role within the organization. It assumes that users will only have access to the data that pertains to their job functions.
  • Attribute-based access control (ABAC): Takes a more dynamic approach to data security. It defines logical roles by combining the observable attributes of users and data, and determining access decisions based on those attributes. ABAC allows for controls to be determined in multiple dimensions, making it a very flexible model.
  • Purpose-based access control (PBAC): A method of data access control that makes access decisions based on the purpose that a given user or tool intends to use the data for. These purposes can include running a report, performing an audit, creating a new application, and much more. The variety of purposes provides flexibility that data governance teams can use to build a high-powered, granular access control model.

It’s also important to note that there are a variety of data masking techniques that organizations can use to help control data access. These include k-anonymization, encryption, differential privacy, nulling, redaction, pseudonymization, averaging, substitution, and tokenization.

[Read More] Data Masking 101: A Comprehensive Guide

5 Best Practices for Implementing Data Controls 

If your organization is creating its own data control framework, there are a variety of best practices that you should follow to achieve the best results. These include:

1. Understanding your data

Have a system for sensitive data discovery and classification to keep track of the types of data you have, where they live, and how they’re used. Being able to automatically detect sensitive data and generate standard tagging across multiple compute platforms eliminates manual, error-prone processes while enabling universal data access control and visibility into sensitive data. It’s also important for understanding what regulations your data may be subject to.

2. Planning for regulatory requirements

Once you know what regulations are relevant to your business, you can build controls to meet their requirements. Some of the most common contemporary regulations that could impact your business include CCPA, HIPAA, GDPR, and COPPA, amongst many others.

3. Aligning your stakeholders

It’s important to ensure that all of your technical stakeholders (data engineers, architects, consumers, governance) and business stakeholders (IT, legal, compliance, data owners) understand and sign off on all of your data controls. Having this alignment will make implementing your data controls a much smoother process.

4. Building for scale

It’s also important to adopt a system that can scale as your data environment — including your platforms, data sources, users, and use cases — continues to grow. Using controls like dynamic ABAC can help provide this necessary flexibility.

5. Enabling auditability

Always implement a data control framework that you can monitor to ensure that it is working as intended and regularly audited to prove compliance. This is critical for risk mitigation in today’s highly regulated markets.

The Case for Implementing Effective Data Controls

In a world where companies are collecting more sensitive personal data, having the right data controls in place is an important step toward ensuring the security and integrity of that data and mitigating potential risks.

It’s important to assess which data controls are right for your organization’s unique situation. So too is working with the right partner to help you implement them as efficiently and effectively as possible. Immuta helps data teams discover, secure, and monitor their data use with dynamic, attribute-based access controls. That way, data teams can proactively ensure and verify that their data controls are consistently being enforced and working as intended.

The Immuta Data Access platform allows data teams to easily create and enforce dynamic data masking controls at scale. By separating policy from platform, Immuta acts as a centralized access control plane from which to apply attribute- and purpose-based access control (PBAC) consistently across all cloud platforms in the data stack. With these fine-grained access controls, data platform teams can implement advanced data masking techniques that simplify compliance with any rule or regulation, and can easily monitor data access and use for auditing purposes.

To experience creating a plain language data policy in Immuta, try our self-guided walkthrough demo!

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