Last updated: July 2026
VRIFY Technology Inc takes the confidentiality and integrity of its customer data very seriously and we strive to ensure data is protected from unauthorized access and is available when needed.
We're proud to share that we've achieved SOC 2 Type II compliance. As of January 2026, this certification covers all five SOC 2 Type II Trust Services Criteria: security, availability, processing integrity, confidentiality, and privacy. This means our systems and operational controls are independently audited against rigorous, industry-recognized standards.
Our SOC 2 compliance reflects our ongoing commitment to building trust with you. We continuously evaluate and improve our security controls to ensure their effectiveness over time.
What is SOC2 Compliance?
Created by the American Institute of Certified Public Accountants (AICPA), SOC 2 provides criteria for handling customer data based on the Trust Services Criteria (TSC): security, availability, processing integrity, confidentiality, and privacy.
Service organizations determine which TSCs apply to their business, then design and implement SOC 2 controls to meet them. Independent auditors provide a report attesting that those criteria have been met.
Data Storage at VRIFY
We store client data in cloud environments, including databases and Amazon S3 buckets, that can only be accessed through secure channels. Your data is never stored locally.
Data stored in our cloud infrastructure is encrypted at rest using AES-256 encryption through AWS-native encryption mechanisms. This applies to storage services including databases, object storage, and backups.
Within the VRIFY cloud environment, access controls are tightly restricted to authorized personnel only. All VRIFY employees complete regular security training and device compliance checks to ensure standards are upheld.
*Note that this is not a contractual agreement. Our Terms of Service is the authoritative document that outlines the data storage agreement.
Data Anonymization
When training our deep learning models on geospatial data, we anonymize data to protect privacy while retaining its usefulness for model training.
This process includes:
Data aggregation. Raw geospatial data is stripped of identifying information such as company names and project specifications. The data is renamed and stored using only data types. At this stage, data remains within each company's individual data environment.
Unique asset identification. Each asset is assigned a numerical identifier in place of its original name. Coordinate Reference System information, raster names, and other potentially identifying metadata are removed. This prevents any asset from being traced back to its original source.
Coordinate data masking. Spatial coordinates are transformed into a normalized index range. This prevents datasets from being repositioned to their original coordinates or coordinate systems.
File format transformation. For image-based deep learning models, data is converted to an image format and stored as a numerical matrix, which does not retain coordinates, data type, or project-specific information. Data values are normalized to a 0 to 1 range for the data loader.
These techniques let us build robust, accurate models while protecting individual client privacy.
Are Other Companies Benefiting From My Data?
In machine learning, the performance and accuracy of a model improves as more data is used. Rest assured, your individual data remains private and inaccessible to third parties.
Aggregated, anonymized data is used to train VRIFY Predict's AI models, allowing them to continuously learn and improve, and ultimately deliver more accurate insights. Our anonymization process ensures your geospatial data can't be traced back to its source.
As stated in VRIFY's Terms and Conditions, this process lets our software continuously improve, providing a better experience for all users.
Protecting your privacy and ensuring data security are our top priorities.
Still Have Questions?
Reach out to your CSM or email support@vrify.com for more information.