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What is LLM Capsule?

LLM Capsule enables organizations to harness the power of AI and large language models—like ChatGPT, Claude, and Gemini—while ensuring that sensitive corporate data remains protected from unauthorized access. Unlike traditional methods of data masking or redaction that often strip away essential context and limit the effectiveness of AI, LLM Capsule enhances and encapsulates data to preserve its inherent structure, relationships, and business relevance for accurate AI analysis. This innovative solution not only secures personally identifiable information (PII) but also shields crucial business contexts—such as contracts, project identifiers, clinical terminologies, and source code—through adaptable policies that cater to specific needs. The Capsule technology ensures that only secure, contextually rich information is shared with both internal and external AI systems, while the original data is seamlessly restored upon access. Organizations can confidently comply with stringent regulations, including N2SF and ISO/IEC 27001, all while benefiting from complete audit capabilities and adaptable deployment choices, whether in a cloud environment or on-site. By leveraging LLM Capsule, teams are not just protected; they are equipped to foster innovation through AI, maintaining the essential context that informs effective decision-making processes, ultimately leading to enhanced organizational growth.

What is Informatica Persistent Data Masking?

Ensure the core message, format, and precision remain intact while prioritizing confidentiality. Enhance data security by transforming and concealing sensitive details through the implementation of pseudonymization techniques that comply with privacy regulations and facilitate analytical needs. The transformed data retains its contextual relevance and referential integrity, rendering it appropriate for use in testing, analytics, or support applications. As a highly scalable and efficient data masking solution, Informatica Persistent Data Masking safeguards sensitive information such as credit card numbers, addresses, and phone contacts from unintended disclosure by producing realistic, anonymized datasets that can be securely shared both internally and externally. Moreover, this approach significantly reduces the risk of data breaches in nonproduction environments, improves the quality of test datasets, expedites development workflows, and ensures adherence to various data privacy standards and regulations. By incorporating such comprehensive data masking strategies, organizations not only secure sensitive information but also cultivate an environment of trust and security, which is essential for maintaining stakeholder confidence. Ultimately, the adoption of these advanced techniques plays a crucial role in promoting an organization's overall data governance framework.

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Integrations Supported

Integrations Supported

AWS IoT SiteWise
Accenture Cloud Trade Promotion Management
Amazon Web Services (AWS)
AssetMetrics
CalendarAnything
Cognizant
Databricks
Decimal
Eviden MDR Service
Google Cloud Platform
HCL Accelerate
Hadoop
Microsoft 365
Microsoft Azure
Peromnes
Salesforce
Snowflake
Tableau

API Availability

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

CUBIG LTD

Date Founded

2021

Company Location

United Kingdom

Company Website

cubig.ai

Company Facts

Organization Name

Informatica

Date Founded

1993

Company Location

United States

Company Website

www.informatica.com/products/data-security/data-masking/persistent-data-masking.html

Categories and Features

Data Masking

Not specified

Categories and Features

Data Masking

Not specified

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