As businesses collect more and more data from users, customers, and partners, the need for secure data access becomes ever more significant.

Think of it like this: if you don’t have tight controls on who has access to your valuable data, then anyone could potentially view or even modify it. Essentially, exactly why security and authentication in a semantic layer are vital to protecting your—or your users’—data.

So, let’s talk about how semantic layers help implement tighter data security. We’ll also explore the importance of multitenancy, authentication tokens, and data access controls in general (both row-based and column-based) as they relate to HIPAA and SOC 2 compliance.

What is a Semantic Layer?

Simply put, a semantic layer is a layer of abstraction that sits between the user interface (UI) and the underlying database of a data application. And, IOHO (in our humble opinion), a good semantic layer—or rather, a complete, universal semantic layer—should do more than only metrics definitions.

Its duties encompass data modeling, sure—ensuring that software engineering best practices such as the DRY principle are being employed in metrics orchestration to organize and make data across the stack consistent.

However, a semantic layer should also include a caching layer to speed up and application performance and make querying cost-effective.

Also in the requirements for a good semantic layer? An API layer to maintain complete data accessibility across a flexible stack.

And lastly—but certainly not least…ly—it must provide extra security for sensitive information stored within the database by limiting who can view or modify specific information.

Essentially, a good semantic layer must also include a governance layer or data access controls.

Why is it important to orchestrate data access controls upstream of data apps?

Having data access controls as a part of a semantic layer is crucial for maintaining data security and integrity. It enables organizations to limit access to sensitive data, ensuring only authorized individuals can access the information they need.

A fictional example of what not to do:

Imagine, if you will, a highly fictional, highly hypothetical credit card score company called Creditlandia, which holds the extremely precious financial data of millions.

Creed is a data engineer at Creditlandia. Moreover, Creed is very tired because he has to manually orchestrate security context for each of Creditlandia's downstream apps; they are all fed data from its massive data stores.

One day, Creed—the human he is—makes a mistake in the tedious and exhausting work that he does.

As a result, a begrudged member of the CX team suddenly sees they have read and write access to every customer’s data. They delightedly export this PII (Personal Identifiable Information) to a hard drive, sell it on the dark web, and retire happily at 36. Creed and management, meanwhile, are not so happy.

And, likely, neither are their millions of customers.

…so, do we see where Creditlandia went wrong?

By faciliating a data pipeline against best practices; one that necessitates duplicated processes and envitably opens gaps in consistency (of security, metrics, and so on).

Orchestrating security context and data governance with a semantic layer

Suppose data access controls are installed in a semantic layer—the middleware sitting downstream of every data source and upstream of every data app. In that case, Creed joyously no longer has to orchestrate security context for each manually.

An organization can then be rest assured that only the right people have the right access to the right information. An executive can have every kind of access to every customer record, but a customer service rep can only read anonymized versions of their open tickets.

Orchestrating granular data access controls in a semantic layer also enables organizations to maintain compliance with regulatory requirements. Centralizing security context provides a comprehensive view of data access across the organization. Ergo, companies can easily identify who has access to what data, enabling them to quickly audit and adjust access controls as necessary.

Overall, implementing data access controls upstream of every data application is vital for providing a more efficient means of managing data security across the organization. And in doing so, enhancing governance and ensuring compliance.

Basically, don’t be like Creditlandia.

The details about data access controls

What are Authentication Tokens?

Authentication tokens are unique identifiers assigned to users upon successful login or authentication. These tokens are then used to authenticate and authorize the same user's access to the system or application, particularly when accessing sensitive data.

In this way, authentication tokens are a crucial aspect of data access control because they ensure that only authorized and authenticated users can access confidential information or processes within an organization's information system.

What is Role-Based Access Control & what is Column-Based Access Control?

Role-based access control (RBAC) is a security feature that restricts access within a system by assigning permissions and privileges based on an individual's role or position in the organization. For instance, managers would have permission to access information that entry-level employees would not. This restricts unauthorized personnel from accessing sensitive information and provides an added layer of protection for confidential data.

For example, RBAC might be used within a financial institution. The bank has several departments such as customer service, accounting, and marketing. Each department has different duties and responsibilities, and there is a risk of data breaches if employees can access information outside their scope of work.

RBAC could be implemented to restrict data access based on employees' job titles or roles, preventing unauthorised personnel from accessing private client data. This enhances security and privacy while ensuring that employees can access the information necessary to perform their duties.

Column-based access control (CBAC) works similarly, but restricts which columns in a table are visible instead of restricting rows.

For example, an employee may be allowed to see all customer records but may only be able to view certain columns such as first name or last name but not address information or phone number information unless they have additional authorization tokens granting them permission to do so.

What is Multitenancy?

Multitenancy is when multiple organizations use the same instance of software, rather than each organization having its own. For example, many companies now use cloud software such as Salesforce or Microsoft Office 365, instead of buying a server for their organization's exclusive use.

Of course, this is highly applicable to embedded analytics applications—for example, e-commerce dashboards for small businesses. One vendor—Quirky Hats 4 U—should not have access to the sales information of another, say, Funny Hats 4 Us.

Multitenancy is essential in terms of security because it allows organizations to share resources while maintaining individualized protection over their own data sets. In addition, authentication tokens ensure that only authorized users can access specific databases within an instance of multi-tenant software.

Data Access Controls, HIPAA & SOC 2 Compliance

Data access controls help protect sensitive PII, such as social security numbers or health records, by prohibiting unauthorized users from accessing confidential files without proper authorization tokens.

These controls are critical when dealing with HIPAA compliance since healthcare organizations must adhere strictly to HIPAA regulations concerning patient privacy. Failing to do so can result in fines or other penalties imposed by the Department of Health and Human Services Office for Civil Rights (OCR).

Similarly, SOC 2 compliance requires organizations dealing with financial services, or handling large amounts of sensitive PII data, to adhere strictly to SOC 2 regulations regarding storage and transmission requirements. Failing here, too, can result in hefty fines from regulatory agencies like The Financial Industry Regulatory Authority (FINRA).

A well-implemented semantic layer can help guarantee compliance with these regulations by providing granular controls over who has access to what pieces of PII at any given time. By doing so, organizations ensure that data remains secure even after being transferred between systems or networks.

Summing it up: why a semantic layer is key in data security and authentication

All in all, security and authentication in a semantic layer are essential for protecting your valuable business assets—particularly when dealing with PII.

Not only does this type of setup allow you granular control over who has access to what kinds of data at any given time, but it also ensures your company complies with applicable laws like HIPAA and SOC 2.

If you’re interested in learning more about how crucial semantic layers are in a modern data stack—not just for security but also data consistency, speed, and accessibility—try Cube for free or reach out to us.

Onward and upwards, Tamar