March 16, 2022
Continual is proud to announce that we are now SOC 2 Type 1 certified and compliant and SOC 2 Type 2 in progress. This certification is a publicly visible milestone that demonstrates our core commitment to keeping your data secure. We expect to make additional announcements around our security certification efforts over the coming months.
Beyond third party attestations, Continual is built from the ground up with data security and governance in mind. We believe Continual represents the future of both how companies will deliver operational AI across their business and architect their emerging modern data stacks.
Over the last few years, cloud data warehouses have become the de-facto choice for companies that want to store and analyze data from disparate systems and sources without operational burden. Unifying data in a central platform opens the door to new business processes and analytical insights and eliminates data silos. Cloud data platforms like Snowflake, Databricks, BigQuery, and Redshift combine both tremendous power and flexibility with robust security and governance.
Continual brings operational AI/ML to this emerging modern data stack. Using Continual, data teams can build predictive models – from customer churn to inventory forecasts – directly on top of their existing cloud data warehouse without complex engineering. Unlike competing AI platforms, there are no integration pipelines to maintain or new data environments to govern and secure.
Continual sits directly on top of your cloud data warehouse and works seamlessly with your existing data security policies and practices. It integrates with your existing data engineering practices and tools, such as dbt, to ensure your entire workflow is well governed.
From a data access perspective, you can provide predictive models read access to as little, or as much, input data as you deem necessary for model performance using your existing database user/role access grant control mechanisms.
From a security monitoring perspective, since Continual only queries data through the warehouse, all your existing query logging and auditing mechanisms can be used.
From a data governance perspective, you can define all your predictive models declaratively in source control, and lean on your existing software development practices. Continual tracks all changes to your configuration, features, and models and logs all actions to an event log audit stream.
From a data storage perspective, all your data stays in your data warehouse. Continual only accesses your data temporarily during training and inference. There are no ETL pipelines to third parties, customer data is not duplicated in Continual, and output predictions are written back into your warehouse where they can be secured and governed alongside the rest of your data.
We believe this hybrid architecture, which puts the data warehouse as center, is the future of how modern data teams will operationalize data from analytics to AI.
For more information about Continual’s security mechanisms, refer to the Security page.
Continual has implemented Drata as a continuous security and compliance monitoring platform. It provides a real time view of our security controls, and performs daily automated checks on our cloud infrastructure and internal business tools. To view this or other compliance reports, including our Data Processing Agreement (DPA), or for questions about our security architecture or product in general, contact us or book a demo with a product expert.
Andrew Tsao discusses the main reasons he chose to join the Continual team.
Traditional AI platforms require users to manage complex data infrastructure and write bespoke ML pipelines for each case. We let users build predictive models using a declarative workflow that radically streamlines operational AI at scale.