Recognition in Cloud Security Awards Category Extends Decorated Awards List for Cloud-Native Application Protection Product (CNAPP) with Hacker Toolkits to Help Expose Potential Data Breaches
Palo Alto, Calif.
Data Theorem, Inc., a leading provider of modern application security, today announced that its Cloud Secure, cloud-native application protection product (CNAPP), has been named a 2021 Cloud Computing Security Excellence Award winner. The Cloud Computing Security Excellence Award, presented by Cloud Computing magazine, recognizes companies that have most effectively leveraged cloud computing in their efforts to bring new, differentiated offerings to market.
“Recognizing leaders in the advancement of cloud computing, TMC and Cloud Computing magazine are proud to announce Data Theorem’s Cloud Secure as a recipient of the Cloud Computing Security Excellence Award,” said Rich Tehrani, CEO, TMC. “Data Theorem is being honored for its achievement in bringing innovation and excellence to this crucial market segment.”
Cloud Secure is the industry’s first solution delivering attack surface management security for cloud-native applications that starts at the client layer (mobile and web), protects the network layer (REST and GraphQL APIs), and extends down through the underlying infrastructure (cloud services). Its combination of attack surface management and run-time active protections enables both offensive and defensive security capabilities to best prevent data breaches of cloud-native applications and serverless cloud functions.
“Data Theorem is picking up in Q4 right where we left off last quarter, with continued industry recognition for Cloud Secure and its unique design helping customers secure their modern cloud applications,” said Doug Dooley, Data Theorem COO. “Since the public launch in March, Cloud Secure’s Hacker Toolkits have uncovered new attack vectors in cloud-native applications and APIs helping to prevent data breaches for our customers. Organizations turn to Data Theorem to protect their cloud applications in multi-cloud environments due to the benefits of our dynamic and run-time analysis with offensive attack surface management and defensive protection toolkits. This enables organizations to conduct continuous, automated security inspection and remediation of their most important cloud-native applications.”
Cloud Secure, Data Theorem’s latest product powered by its award-winning Analyzer Engine, helps organizations secure their cloud-native applications and address regulatory compliance for cloud monitoring and reporting. Data Theorem is one of the first vendors to provide a full stack application security analyzer that connects attack surfaces of applications starting at the client layers found in mobile and web, the network layers found in APIs, and the infrastructure layers found in cloud services.
Data Theorem’s broad AppSec portfolio protects organizations from data breaches with application security testing and protection for modern web frameworks, API-driven microservices and cloud resources. Its solutions are powered by its award-winning Analyzer Engine, which leverages a new type of dynamic and run-time analysis that is fully integrated into the CI/CD process, and enables organizations to conduct continuous, automated security inspection and remediation.
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Media ContactDan Spaldingdan@datatheorem.com(408) 960-9297
About Data Theorem
Data Theorem is a leading provider of modern application security, helping customers prevent AppSec data breaches. Its products focus on API security, cloud (serverless apps, CSPM, CWPP, CNAPP), mobile apps (iOS and Android), and web apps (single-page apps). Its core mission is to analyze and secure any modern application anytime, anywhere. The award-winning Data Theorem Analyzer Engine continuously analyzes APIs, Web, Mobile, and Cloud applications in search of security flaws and data privacy gaps. The company has detected more than 5 billion application incidents and currently secures more than 25,000 modern applications for its enterprise customers around the world.