Data security 2023-03-13
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Credal

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Safeguarding sensitive data in secure utilization.
Generated by ChatGPT

Credal.ai is an AI tool designed to protect sensitive data while leveraging AI applications within an enterprise. It provides a secure solution for utilizing popular AI apps such as ChatGPT, ensuring that business secrets and protected data like PII remain secure.

The tool offers APIs, a secure chat UI, and a Slack bot that integrates seamlessly with your data sources such as Google Drive, Confluence, and Slack.

It automatically enforces access policies, masks sensitive data, and ensures acceptable use policies are followed. Credal allows enterprises to define, enforce, and audit access to AI tools, whether internally built or externally procured, all from a single place.

It enables developers to build custom applications on secure APIs while respecting source permissions, generating automatic audit logs, and masking sensitive data.

For increased security, Credal can be deployed fully on-premise, including the large language models themselves, ensuring data never leaves the network and leveraging existing investments in platforms like Azure OpenAI and AWS Bedrock.

The tool provides granular audit logs that track data shared with AI providers, offering transparency into who is sharing what data and the applicable terms and agreements.

It also offers automatic redaction of sensitive keywords and phrases before data leaves the organization and syncs permissions with source systems like Google documents and Confluence pages.

Credal is popular among developers as it offers drop-in replacements for OpenAI and Anthropic APIs and allows them to build apps on top of enterprise data while maintaining data security.

Trusted by leading companies, Credal ensures the secure adoption of AI within an enterprise, providing the necessary security controls and features to protect sensitive data while leveraging the benefits of AI applications.

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Credal was manually vetted by our editorial team and was first featured on July 6th 2023.
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2 alternatives to Credal for Data security

Pros and Cons

Pros

API offerings
Includes secure chat UI
Comes with Slack bot integration
Pre-integrated with various data sources
Automatic enforcement of access policies
Sensitive data masker
Supports custom application building
Automatic audit log creation
Data security with on-premise deployment
Large language model support
Detailed audit logs for data transparency
Redaction of sensitive keywords or phrases
Syncs permissions with source systems
Protects enterprise data security while building apps
Data never leaves your network
Supported by leading companies
Granular audit logs
Automatic redaction
Permissions syncing
Integration with Google Drive, Confluence, Slack
Enterprise data sources integration
Ease-of-use with secure chat UI
Secure APIs for custom apps
Option for full on-premise deployment
Protection against data training on your models
Removal of all data from LLM servers
Prevent sending data to the LLM
Trust from proven industry leaders
Transparency into data sharing
Relevant context provision
Inclusion of source system permissions
Python libraries support

Cons

No option for cloud deployment
Lacks multi-language support
Dependent on Azure, AWS
No self-service troubleshooting features
Limited data source integrations
Specific to enterprise usage
No individual user tier
High reliance on APIs
High learning curve for non-tech users

Q&A

What is Credal.ai?
How does Credal ensure data security?
What types of AI applications can be securely utilized using Credal?
What are the key features offered by Credal to protect sensitive data?
Can Credal be fully deployed on-premise?
What are some popular AI applications that can be securely used with Credal?
How does Credal support developers in maintaining data security?
What integrations does Credal offer with external data sources?
How does Credal automate enforcement of access policies?
What types of data can Credal mask?
What audit logging capabilities does Credal provide?
Is it possible to use Credal with internally built AI tools?
How does Credal aid in enforcing acceptable use policies?
Does Credal redact sensitive data before it leaves the organization?
Can Credal sync permissions with source systems?
How can enterprises control usage of AI apps with Credal?
Can companies build custom applications on secure APIs using Credal?
How can developers benefit from using Credal?
Does Credal negotiate MSAs with AI providers?
What do existing users have to say about Credal?

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