We are extremely excited to announce that Immuta has achieved Google Cloud Ready – BigQuery designation, validating our native integration with BigQuery and providing joint customers with confidence in our partnership. As part of the validation process, Google Cloud engineering teams run a series of data integration tests, compare results against benchmarks, work closely with partners to fill any gaps, and refine documentation for our customers.
This designation will give Immuta’s engineering team the opportunity to collaborate more closely with Google Cloud partner engineering and BigQuery teams to develop joint roadmaps. The end result: better interoperability, improved features that solve joint customers’ biggest data challenges, enhanced data security for BigQuery, and much more. The designation also proves that the Immuta platform has met a core set of functional and integration requirements to guarantee a seamless, secure user experience when connected to BigQuery.
BigQuery continues to see large-scale adoption in the enterprise across a variety of use cases including, but not limited, to traditional business intelligence and analytics, data science and machine learning, and data warehousing/data lakes. Its serverless architecture helps achieve unparalleled scalability, and BigQuery has been proven to be redundant and resilient.
Still, the modern data stack presents challenges to data security that BigQuery users must be prepared to solve. These include:
As the volume of data and users grows across an organization, so does the complexity in managing access to data. Data and security teams are tasked with ensuring that only users with correct privileges can access data. But, in highly dynamic business environments, those privileges might constantly change, making it extremely difficult and burdensome for teams to ensure only the right people are accessing the right data. Failing to effectively secure data leaves it open to the risk of misuse by malicious actors, both internally and externally. The result: regulatory fines, costly remediation, and negative brand impact. Effective data security becomes the key enabler for safe and effective data use that unlocks innovation.
As technology has evolved, so have the methods used to support it efficiently. With regard to data security, some data teams have gone down the path of managing access across individual roles via tools that leverage RBAC (role-based access control). While this may have worked well enough with few data users, assets, and use cases, relying on static policies that must be constantly created or updated quickly becomes a scalability and manageability nightmare. In other cases, data teams might choose to leverage a limited set of native platform controls, but they find that having to write and manage complex SQL becomes burdensome over time.
Adding to the complexity of accommodating more data and users, the modern data landscape is becoming decentralized. While trends like multi-platform, multi-cloud, and data mesh create a more distributed, flexible ecosystem, they also require a framework to support centralized and decentralized policy management. Data teams need to be sure that policies are consistently enforced across all data, regardless of its domain. This increases security overhead in the midst of increasing data compliance regulations and enforcement.
The Immuta Data Security Platform was built ground-up to address data and security teams’ most pressing security and access management concerns. One of the key tenets of our design philosophy is manageability. Building policies manually across hundreds or even thousands of tables, users, and policies can quickly become a management nightmare – something that Immuta has solved through automation.
Immuta solves the manageability conundrum through three key components: Discover, Secure, and Detect.
With Discover, data teams are able to centralize metadata management in order to power data discovery, policy automation, and orchestration. Immuta monitors for and registers schema changes in BigQuery, and when tables are discovered through the registration process, Immuta inspects the table data for sensitive information and tags it as such.
The Secure module focuses on streamlining policy authoring and enforcement as well as the application of privacy enhancing technologies, such as differential privacy and obfuscation. Immuta abstracts out the complexity of writing, applying, and maintaining policies with a high level of automation through its simple-to-use plain language policy builder, as well as policy-as-code for teams that want to programmatically integrate and automate policy into their data pipelines. Ultimately, the idea is to allow teams – both technical and non technical – to create and evolve data policies faster and with minimal risk.
Finally, the Detect module provides data, security, and/or compliance teams enriched data audit trails that can be used to build reporting that proves proper data use in compliance with any regulatory requirements. Audit trail information includes details like who accessed what data and when, what kind of policies were applied, policy changes, and much more.
Writing and maintaining policies can be a complex undertaking for data teams. With Immuta’s proven policy engine, complex policy statements can be modeled and applied at scale to secure data in BigQuery. Furthermore, Immuta’s attribute-based access control (ABAC) requires 93x fewer data policies compared to different approaches like RBAC.
Immuta makes it possible to prove compliance with rules and regulations, even when securing hundreds of thousands of tables. And, with continuous activity monitoring, Immuta enables data and security teams to proactively identify and remediate risk.
Immuta helps organizations get 100x faster access to data, which translates to improved productivity. With Immuta, more data can safely reside in the cloud, more users can access that data, and more data products can be deployed – all with less risk.
As the adoption and usage of BigQuery rapidly increases across the enterprise, the need for effective data security and governance continues to grow in importance, especially as increased security threat vectors – both internal and external – emerge. The lack of effective controls to secure a company’s most critical data could leave the door open to malicious characters executing their agendas, ultimately leading to major data leaks, regulatory fines, and negative brand impact.
Immuta’s integration with BigQuery helps avoid these adverse outcomes, so users get secure, real-time access to data, without impacts to performance. To read more about how customers are using Immuta to unlock more data for more use cases, check out Building an Agile Data Stack for the Top Data Use Cases, or request a live demo with our team.
Get in touch with our team to see how Immuta works with BigQuery.
