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Description
Knox serves as a secret management platform designed for the secure storage and rotation of sensitive information such as secrets, keys, and passwords utilized by various services. Within Pinterest, a multitude of keys and secrets are employed for diverse functions, including signing cookies, encrypting sensitive data, securing the network through TLS, accessing AWS machines, and facilitating communication with third-party services, among others. The risk of these keys being compromised posed significant challenges, as the process of rotation typically required a deployment and often necessitated changes to the codebase. Previously, keys and secrets at Pinterest were stored in Git repositories, leading to their replication across the company's infrastructure and presence on numerous employee laptops, which made tracking access and auditing who had permission to use these keys virtually impossible. To address these issues, Knox was developed with the intention of simplifying the process for developers to securely access and utilize confidential secrets, keys, and credentials. It also ensures the confidentiality of these sensitive elements while providing robust mechanisms for key rotation in the event of a security breach, thereby enhancing overall security practices. By implementing Knox, Pinterest aims to streamline secret management processes while fortifying its defenses against potential vulnerabilities.
Description
Enhancing patient care can be achieved through the secure and confidential sharing of data among healthcare professionals, ensuring the protection of privacy. Additionally, it is important to facilitate secure financial data analysis to effectively manage risks and detect fraud, while keeping client information encrypted and safeguarded. In the evolving landscape of digital marketing, creating targeted advertising and campaign insights without compromising user privacy can be accomplished through encrypted data analysis, especially in a post-cookie world. Furthermore, fostering data collaboration between various agencies is crucial, allowing them to work together efficiently without disclosing sensitive information to each other, thus bolstering both efficiency and data security. Moreover, developing applications for user authentication that maintain individuals' anonymity is essential in preserving privacy. Lastly, empowering governments to digitize their services independently of cloud providers can enhance operational trust and security. This approach ensures that the integrity of sensitive information is upheld across all sectors involved.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
Docker
Yes
Pinterest
Yes
Pradeo
Yes
Python
No
Swarm
Yes
Yaguara
Yes
Integrations
Docker
No
Pinterest
No
Pradeo
No
Python
Yes
Swarm
No
Yaguara
No
Pricing Details
No price information available.
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
No
Mac
No
Linux
No
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
No
Live Rep (24/7)
No
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
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Founded
2009
Country
United States
Website
github.com/pinterest/knox
Vendor Details
Company Name
Zama
Founded
2020
Country
France
Website
www.zama.ai
Product Features
Privileged Access Management
Application Access Control
No
Behavioral Analytics
No
Credential Management
No
Endpoint Management
No
For MSPs
No
Granular Access Controls
No
Least Privilege
No
Multifactor Authentication
No
Password Management
No
Policy Management
No
Remote Access Management
No
Threat Intelligence
No
User Activity Monitoring
No
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No
