Concentric announced the latest advancements available in its Semantic Intelligence data access governance solution that address the industry’s continued challenges with unstructured data security threats by autonomously identifying risk created by inappropriate access, sharing and management of unstructured data.
Concentric’s Semantic Intelligence solution now offers patent-pending Risk Distance analysis for autonomous assessment of file and activity-based risk. Risk Distance uniquely uses advanced deep learning technology – a type of artificial intelligence (AI) – to find at-risk data by comparing each file to its peers. These peer file comparisons give security professionals for the first time the highly targeted and actionable guidance they need to finally secure their business-critical unstructured data without hard-to-maintain rules or complex configurations.
Recent data security incidents at a major bank and drug manufacturer highlight how even large, well-funded organizations cannot fully secure their unstructured data. Protecting millions of disparate files stored across a sprawl of data storage options is a daunting task for IT professionals. Available tools are inaccurate and labor-intensive, failing to reliably identify business-critical files, assess risk or provide insight into urgency. Concentric’s Semantic Intelligence Data Access Governance solution addresses these unmet unstructured data security needs using the latest innovations in AI technology to apply the industry’s first zero trust protection for unstructured data – without needing additional staff or end-user involvement.
“We trust Concentric’s solution to effectively secure unstructured data,” said Sebastian Goodwin, VP of Cybersecurity at Nutanix. “With Risk Distance analysis, we can see the policies our users have adopted, understand which files are at risk compared to their peers, and enforce zero trust practices in ways that don’t constrain the business.”
Concentric emerged from stealth mode earlier this year with $7.5M in funding and a team of expert AI and data security technologists. This summer, the company added Concentric MIND, a deep-learning-as-a-service infrastructure that aggregates and curates models from across customer installations for faster, more accurate data categorization and adaptation to industry-specific data types. Now, with the addition of AI-based Risk Distance analysis, the solution can identify specific risk factors and assess the urgency to act by comparing each file to how its peers are configured and used – without upfront work, rule development and maintenance or end-user classification programs.
Semantic Intelligence is the only data access governance solution capable of giving security professionals the concise, actionable information they need by identifying the intersection of risk, business criticality and urgency. Offering more than 175 thematic data categories, Semantic Intelligence offers the industry’s best autonomous data categorization capability, which is the key to effective risk management. Offered as an easy-to-deploy SaaS platform, Semantic Intelligence needs no agents, upfront configuration or rules development, and delivers value in days, not weeks or months as seen with traditional solutions.
“Prior to today’s launch, the industry had no way to find file-based and activity-based risk without writing complex rules or implementing cumbersome end-user classification programs,” said Karthik Krishnan, Concentric CEO. “Now, important progress in deep learning research – commercialized by Concentric – has created a third way. We’ve built the first comprehensive solution to the problem of access management for large bodies of unstructured data and apply zero trust protection.”
With Risk Distance’s pioneering ability to identity business criticality, unusual security configurations (e.g. file sharing or classification) and anomalous activities (e.g. file movement or management), security professionals benefit from accurate, actionable unstructured data security at scale. They can focus on only urgent data security issues without having to assess thousands of unimportant files; can discover all types of data risk, from permission errors to misclassification to activity to location; and can eliminate complex, rule-based tools with an autonomous system that taps into the collective wisdom of file owners.
