San Mateo headquartered graph database company Neo4j (with roots in open source) is working with French defence company Thales (pronounced ta-less).
A graph database is a database designed to treat the relationships between data as equally important to the data itself — it is intended to hold data without constricting it to a pre-defined model… instead, the data is stored showing how each individual entity connects with or is related to others.
What is data-at-rest?
In basic terms, data-at-rest is data that is stored physically in any digital form in a database, data lake, spreadsheet, tape, disk or any other form of storage media or repository — it is, of course, the opposite of data-in-transit.
The firms say that the new integration provides ‘industrial-strength’ encryption-at-rest for the Neo4j graph database and helps Neo4j users meet more stringent security and compliance requirements.
Magical analyst house Gartner states, “The application of graph processing and graph databases will grow at 100% annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science.”
Neo4j explains that its challenge comes from the real-time nature and extreme performance requirements of many of its mission-critical enterprise deployments, where sacrificing performance for security isn’t an option.
VP of products at Neo4j Philip Rathle points to security-sensitive industries such as financial services, insurance and healthcare, where this kind of encrption will be needed most.
The Neo4j and Thales integration is meant to ensure enterprise policy and regulatory compliance for Neo4j instances including data-at-rest encryption with centralised key management, privileged user access control and security intelligence to meet compliance reporting requirements.
Developers & graph databases
The solution is deployed without any changes to infrastructure so developers and data engineers working in security teams can implement encryption with minimal disruption.
The integration protects data wherever it resides: on-premises, across multiple clouds or within big data and container environments.