Symmetry Systems, a modern AI-powered data security company, is proud to celebrate a successful 2023 filled with great customer outcomes, product updates, and a roadmap to place data and identities at the heart of enterprise security.
Cybersecurity teams have to let the business innovate wildly with GenAI projects – and do so on a secure foundation. They are turning to Symmetry to ensure that appropriate data is used to train GenAI models, and to help answer questions like, “Can any third-party contractors working on our GenAI project access sensitive customer data this type of sensitive data across any of our three clouds?”. Being able to answer this accurately in organizations with hundreds of petabytes and cloud accounts, and also identify specific data objects where they did so here, here, and here is an outcome that only Symmetry can do today. This has led to Symmetry’s successful deployment at, amongst others: Fortune 50 manufacturing companies, leading pharmaceutical and financial services companies, and a highly regarded federal organization at the forefront of the aeronautics and space industry. Symmetry produces tangible outcomes – e.g., enabling a Fortune 100 organization on Google cloud to delete over 25% cloud assets such as Projects, Identities, and production data without any business impact.
Throughout the year, Symmetry Systems has continually improved its ability to answer simple questions about an organization’s data, but also drive real outcomes beyond classifying and mapping access to data that an organization has. Most notably, Symmetry has made continuous advancements across cloud and data storage coverage, ML-powered data identifiers and classifiers, and comprehensive data security capabilities. With these enhancements and feature updates, Symmetry Systems helps customers overcome the complexity of securing their data across multiple platforms. The announcement of these features comes shortly after Symmetry Systems achieved Amazon Web Services (AWS) Security Competency status in the Data Protection category, demonstrating the company’s commitment to helping organizations easily and efficiently protect their most important asset – their data.
The new features from Symmetry Systems released throughout the past year include:
- Expanded Cloud and Data Store Coverage: Symmetry Systems, already renowned as the most comprehensive cloud platform providing feature parity across major cloud providers, has further extended its capabilities. This year Symmetry expanded coverage and integrations for Microsoft Azure to include OneDrive and SharePoint, and strengthened its on-premise deployments with Hadoop, Apache Ranger, and SQL Server integrated with Azure Active Directory, and expanded integrations for self-managed “in your cloud” deployment, tailored for IL-6 Air Gapped cloud environments. Symmetry’s existing data store coverage already includes support for Amazon S3, Azure Blob Storage, Google Cloud Storage, Amazon Redshift, Amazon RDS, Google BigQuery, MySQL, Postgres, and more.
- Market leading Machine Learning -powered Data Identifiers and Classifiers: Symmetry Systems has raised the bar for data discovery and classification, surpassing competitors in terms of speed, accuracy, and cost-effectiveness. Through intelligent data object sampling, context-aware classification and identity first prioritization, Symmetry reduces false positives to industry leading levels, optimizing the speed and cost of classification and enhancing the customers’ remediation processes to optimize resource allocations and enhance security efficacy.
Symmetry Systems also expanded its built-in set of identifiers to over 60, including the detection of Azure SAS Tokens, AWS Pre Signed URLs, and public/private keys, enhancing the capability to identify a variety of sensitive data.
- Comprehensive Data Security Capabilities in single platform: Symmetry Systems’ extensive range of data security features now covers capabilities across all NIST functions (Identify, Protect, Detect, Respond and Recover). Building upon its market-leading Data Security Posture Management (DSPM), Data Detection and Response (DDR), and Cloud Identity and Entitlement Management (CIEM) capabilities, Symmetry has continued to incorporate state-of-the-art machine learning techniques to monitor cloud identity behavior and data operations, offering advanced anomaly detection across all major cloud service providers and on-premise datastores.
The company also automated enforcement and remediation of excess, dormant and unauthorized permissions to sensitive data before they become an issue, and now offers out-of-the-box data risk reports that provide comprehensive insights about data risks and vulnerabilities with respect to sensitivity, volume, location, and accessibility to data in just a few clicks. These assessment reports provide insights with recommended actions to mitigate risks and strengthen security within hours of deployment.
Also Read: Strategies to Develop a Cyber-Resilient Security Posture
The majority of these features are already in production for customers or in the final stages of performance testing. Symmetry currently helps organizations secure their crown jewels across thousands of their AWS, Azure and GCP cloud accounts, effortlessly analyzing over 50 million on-prem and cloud data stores and data objects, analyzing and safeguarding over 20 million identities and analyzing over 2 billion operations per month. The product development and feature addition sets up Symmetry for further success in an even larger scale and versatility next year
“Modern enterprises already run on data. And LLMs, large language models, promise to open up more of their data to more of their people to unlock its value. Symmetry’s mission is to help organizations be great stewards of data – to accelerate this integration of people and data so that organizations can be both agile and safe.” said Dr. Mohit Tiwari, Co-Founder and CEO at Symmetry Systems. ”To this end, we’ve made simple declarative policies that map directly to plain English outcomes such as ‘contractor access to customer data’, that compile to all three clouds and on-premise data stores like Tensorflow did for ML models; and an AI-driven visual interface that welcomes every persona in an organization to collaboratively steward the data.”
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