1touch.io uses AI-driven data monitoring and modeling software atop bespoke high-performance compute and storage resources to discover inappropriately exposed and non-compliant data assets across an enterprise’s extended hybrid IT estate.
Many varieties of multi-functional security, identity, authorization and governance platforms exist already, often built upon acquisitions of several niche vendors rolled under one management plane. 1touch.io doesn’t try to replace these tools, instead specializing its engine to automatically map a master data catalog across high volumes of structured and unstructured data, whether in motion or at rest, in public cloud IaaS or private networks.
“Data discovery needs to find the PII data you don’t know about,” says CEO and founder Zak Rubinstein. “If you have to manually map data to systems, it would be like telling an antivirus tool that it needs to know where to look for viruses.”
You might think of this zero-trust approach to identifying and classifying data and documents as forensics for drilling down to the root cause of a breach that already happened. But looking forward, this predictive approach allows DataSec teams to ask policy-based questions of an enterprise data estate, whether observing a massive ML or analytics data set, combing file shares and communications for thousands of employees and business partners, or monitoring a matrix of connected banking or government systems talking via APIs to determine exposures.
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