White Papers

Building an IAM Platform with TypeDB

The current business needs of Identity and Access Management (IAM) platforms have grown in both scope and complexity as a result of the sudden shift to hybrid and remote work, the emergence of machine identities, the drive for just-in-time-access, the adoption of cloud infrastructure.

This white paper explains how to define and query an extensible data model for managing permissions with a IAM platform built on TypeDB. The core requirements for the data model are based on RSA's Identity Governance and Administration Platform, as well as standards defined by governing bodies such as NIST.

Specifically, this white paper will detail how to:

  • Design a logical data model based on entities, relations and attributes.

  • Extend the data model to support organization- and application-specific requirements.

  • Write TypeQL queries to manage permissions and access via pattern matching.

  • Infer data with automatic, rule-based reasoning.

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Strongly-Typed Data for Machine Learning

TypeDB's strong type system allows you to model your data natively based on the entity-relationship model, making it possible to build a knowledge base that encodes the context and semantics of your domain. It's then straightforward to learn and make predictions over interrelated data, for instance, data that has been ingested from various corporate silos using ontologies or taxonomies.

TypeDB makes it possible to build the best possible features for your models to consume. Its strong type system ensures that your data has a clear structure defined by a schema. Checking that everything inserted adheres to a schema gives concrete assurance that the data is both typed and logically consistent. TypeDB provides type hierarchies, which means learning about one type can have implications for other types in the same hierarchy.


Cyber Threat Intelligence

Cybercrime is expected to cost organizations worldwide over $10 trillion annually by 2025, up from $3 trillion in 2015. That represents an annual growth of 15% – one of the greatest transfers of economic wealth ever. There is a huge need for innovative technologies to address the state of the industry. Cybersecurity data is inherently connected. To obtain a comprehensive picture to determine the severity of a particular threat, information from many different sources needs to be integrated. It's therefore essential that tools exist to make this integration, as well as the analysis of the data, more effective and efficient.

Given the heterogeneous and complex nature of cybersecurity data, TypeDB is a perfect fit. TypeDB's expressive schema language, which allows for concepts such as type hierarchies and hyper relations, gives you a level of expressivity which can model the most complex cybersecurity data as accurately as possible. And through that semantic richness, TypeDB makes it easy to discover new insights, improving prevention of cyberattacks and helping to secure your enterprise.


The Role of Symbolic AI with ML in Robotics

Data in robotics is often incredibly heterogeneous as it needs to represent real-world data, planning systems, hardware data, and much more. This requires a database system that can build the type of knowledge bases that can model this type of information and all its semantic richness. This is why many robotics organisations use TypeDB to natively model constructs such as hyper relations, nested relations, type hierarchies and much more.

With TypeDB, you'll have the right framework to build knowledge-enabled robots. TypeDB gives robots the ability to reason independently without having to rely exclusively on human intervention or expensive machine learning approaches.

Download the white paper below to learn how TypeDB can be used to build robotics systems. The white paper includes code examples that you can start using today.


Accelerating Drug Discovery

Systems biology produces a tremendous amount of heterogeneous data between biological components such as genes, proteins, tissues, and cells. Integrating this data presents challenges for bioinformaticians due to the data's inherent complex nature and rich semantics. Furthermore, analyzing large volumes of biological data through traditional database systems is troublesome and challenging.

With TypeDB, working with life sciences data becomes much easier, enabling you to accelerate the entire drug discovery process.