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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
No
API Access
Has API
No
Screenshots View All
No images available
Integrations
Azure Data Explorer
No
Azure Databricks
No
Azure Key Vault
No
Azure Virtual Desktop
No
Kubernetes
No
Microsoft Azure
No
NVIDIA Confidential Computing
No
Integrations
Azure Data Explorer
Yes
Azure Databricks
Yes
Azure Key Vault
Yes
Azure Virtual Desktop
Yes
Kubernetes
Yes
Microsoft Azure
Yes
NVIDIA Confidential Computing
Yes
Pricing Details
$0
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
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
No
Graphical User Interface
No
Remote Control
No
VDI
No
Virtual Machine Encryption
No
Virtual Machine Migration
No
Virtual Machine Monitoring
No
Virtual Server
No