Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Anoni assists professionals in leveraging AI technology while safeguarding the confidentiality of sensitive documents.
Various sectors including legal, human resources, healthcare, accounting, consulting, and public administration frequently face challenges when attempting to utilize AI tools due to the presence of personal and sensitive information in contracts, resumes, case files, patient records, and client documents. The traditional approach of manually redacting such information is often tedious, prone to mistakes, and may compromise the usability of the document.
To address these issues, Anoni operates by processing documents directly on the user's device, identifying personal and confidential data, and then either anonymizing, pseudonymizing, or redacting it before any sharing, analysis, or AI utilization occurs. Notably, documents remain securely on the user's machine and are not sent to any cloud-based processing service.
Developed in France, Anoni is designed for those who prioritize privacy in AI applications, document assessment, teamwork, and data preparation processes. Its features include local processing capabilities, customizable detection settings, realistic pseudonymization techniques, and the ability to generate reports without leaving the user's environment. This ensures that sensitive information is handled with the utmost care while still enabling efficient workflow integration.
Description
Azure Confidential Computing enhances the privacy and security of data by safeguarding it during processing, rather than merely when it is stored or transmitted. It achieves this by encrypting data in memory through hardware-based trusted execution environments, enabling computations to occur only after the cloud platform has authenticated the environment. This method effectively blocks access from cloud service providers, administrators, and other privileged users. Additionally, it facilitates scenarios like multi-party analytics, where various organizations can collaboratively use encrypted datasets for joint machine learning efforts without disclosing their respective data. Users maintain complete control over their data and code, dictating which hardware and software can access them, and they can transition existing workloads using familiar tools, SDKs, and cloud infrastructures. Ultimately, this approach not only fosters collaboration but also significantly bolsters trust in cloud computing environments.
API Access
Has API
API Access
Has API
Screenshots View All
No images available
Integrations
Azure Data Explorer
Azure Databricks
Azure Key Vault
Azure Virtual Desktop
Kubernetes
Microsoft Azure
NVIDIA Confidential Computing
Integrations
Azure Data Explorer
Azure Databricks
Azure Key Vault
Azure Virtual Desktop
Kubernetes
Microsoft Azure
NVIDIA Confidential Computing
Pricing Details
$0
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Anoni
Founded
2026
Country
France
Website
anoni.dev/
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
azure.microsoft.com/en-us/solutions/confidential-compute
Product Features
Product Features
Virtual Machine
Backup Management
Graphical User Interface
Remote Control
VDI
Virtual Machine Encryption
Virtual Machine Migration
Virtual Machine Monitoring
Virtual Server