In this article, I’ll discuss the advantages of using graph databases over LDAP for metadata labeling and verification. Verifying identity data, either by using a third-party verification or some internal mechanism, allows you to trust the data you have and use it for potentially higher-risk transactions. That’s where graph databases come in, as they offer several advantages over LDAP for metadata labeling and verification. In addition to storing whether the property has been verified, you can also store information on the verification process and when it was verified.
In many cases, customers and organizations are looking to increase the value of their identity data. One common challenge is managing metadata, such as whether an email address has been verified or not. As a senior identity and access architect and executive identity leadership advisor, I often work with clients who are looking to improve their identity and access management systems, including their metadata labeling and verification processes.
By verifying identity data, either by using a third-party verification or some internal mechanism, you are able to trust the data you have and use it for potentially higher-risk transactions. This is where graph databases come in, as they offer several advantages over LDAP for metadata labeling and verification. In addition to storing whether the property has been verified, you can also store information on the verification process and when it was verified.
Advantages of Graph Databases over LDAP:
Let’s start with the advantages of using graph databases for metadata labeling and verification. Graph databases are much more flexible than LDAP when it comes to modeling metadata. In LDAP, you have to define a new attribute or object class for each type of metadata you want to add, which can be limiting. Graph databases, on the other hand, allow you to model metadata as nodes and edges, which can be connected to any entity in the database. This makes it easy to add new types of metadata without modifying the schema.
Another advantage of graph databases is scalability. They are designed to handle large volumes of metadata, making them ideal for high-traffic applications or large datasets. Graph databases are also great for handling complex queries involving multiple levels of relationships between entities.
One of the most significant advantages of graph databases is their ability to visualize relationships between entities and their associated metadata. This can be helpful when debugging or troubleshooting metadata-related issues.
Applying NIST 800-63 Assurance Levels:
NIST 800-63 is a set of guidelines for identity assurance in digital transactions that defines three levels of assurance: low, medium, and high. In graph databases, you can add a metadata node to represent the assurance level of a property, which can be connected to the entity node via an edge. This allows you to easily add the assurance level and verification date as properties of the metadata node.
In LDAP, you can use an object class to define the metadata you want to add and then associate that object class with the entry that represents the property. While it’s possible to apply NIST 800-63 assurance levels in LDAP, it requires more effort than in graph databases.
Comparison with LDAP:
When it comes to managing metadata, graph databases have several advantages over LDAP. Graph databases are better suited for highly interconnected data and metadata that changes frequently. They are also more scalable and can handle complex queries involving multiple levels of relationships between entities.
In contrast, LDAP is more suitable for managing hierarchical data with less complex relationships between entities. It can handle metadata, but it may require more effort to update and modify the schema.
Integrating Graph Databases:
Introducing or integrating graph database technology doesn’t necessarily have to be a lift and shift where you completely replace the LDAP systems that you’ve spent years building and maintaining.
Oftentimes, a graph database can be added as a complementary service that can be used to enrich your existing identity data and enable you to gain more knowledge about the identities, their relationships, and actions.
For example, you can use a graph database to add new types of metadata to your existing identity data without having to modify your LDAP schema. This can help you gain more insights into the identities in your system and their relationships with other identities and entities.
You can also use a graph database to create a more detailed picture of your identity data, which can help you detect potential security threats or fraud. For instance, you can use a graph database to track the relationship between different identities and their access to different resources. This can help you identify anomalous behavior that may indicate a security breach.
Adding information on the verification process and when it was verified to your metadata labeling system can help you achieve a higher level of assurance in your identity data. By storing this information in a graph database, you can easily access and analyze it to gain insights into the verification process and ensure that it was performed correctly and within the required time frame.
In conclusion, if you’re looking for a more flexible, scalable, and efficient way to manage metadata, then a graph database is the way to go. Graph databases are better suited for highly interconnected data and metadata that changes frequently. However, if you’re dealing with hierarchical data with less complex relationships between entities, LDAP may be a better choice.
Introducing or integrating graph database technology doesn’t have to be a daunting task. You can start by identifying the areas where a graph database would add value to your existing identity data and gradually integrating it into your system. If you’re currently working on a project involving metadata labeling and verification or would like to know more about how graph databases can help you manage metadata more effectively, please feel free to reach out. I would love to hear about your experience with graph databases and help you get the most out of this powerful technology.