Platform Capabilities

Data Protection Capabilities for AI, Analytics, and Cloud

Protect sensitive data at the data layer so teams can use it safely across AI, analytics, development, sharing, cloud, and business workflows. Protegrity brings together modular capabilities for finding, governing, protecting, and using sensitive data across modern enterprise environments.

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AI and Advanced Analytics

Use sensitive data safely across AI and analytics workflows.

AI systems need useful data to generate reliable insights, but sensitive information must remain controlled. Protegrity capabilities help teams protect data across natural-language analytics, AI pipelines, model development, retrieval, inference, and response generation.

Semantic Guardrails

Apply context-aware controls to AI applications, assistants, and agents. Semantic Guardrails help reduce unsafe prompts, off-policy responses, prompt injection risk, and sensitive data exposure in AI interactions.

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Synthetic Data

Generate realistic, statistically representative datasets for AI training, model testing, analytics, development, and data sharing. Synthetic data helps teams reduce reliance on regulated production data while preserving useful patterns for approved use cases.

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Find, Protect, and Control Sensitive Data

Know where sensitive data exists and control how it is used.

Sensitive data appears across structured systems, unstructured content, cloud platforms, applications, and AI workflows. Protegrity helps teams identify sensitive data, apply protection, and control how data appears based on user, role, policy, and context.

Find & Protect

Detect and protect sensitive data across AI pipelines, uploaded files, prompts, retrieved content, and generated outputs. Find & Protect helps teams apply the right controls where sensitive data appears, including redaction, masking, tokenization, and policy-driven protection.

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Dynamic Data Masking

Control how sensitive data appears at query time based on role, policy, and access privileges. Dynamic Data Masking helps users work with data while limiting unnecessary exposure of raw values.

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Unstructured Data Protection

Protect sensitive information in files, documents, images, PDFs, messages, and free-text content. Unstructured Data Protection extends data-centric controls beyond rows and columns so sensitive data can be managed across more enterprise workflows.

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Foundational Protection Methods

Protect the data itself with methods matched to each use case.

Different workflows require different protection methods. Protegrity supports persistent, data-level protection that helps teams reduce exposure while preserving the usability needed for analytics, applications, AI, compliance, and business operations.

Vaultless Tokenization

Replace sensitive values with protected tokens while preserving data utility for approved workflows. Vaultless tokenization helps reduce cleartext exposure without requiring a traditional token vault.

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Format-Preserving Encryption

Encrypt sensitive data while maintaining the format downstream systems and applications expect. Format-preserving encryption helps teams protect data while supporting compatibility across legacy and modern environments.

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Data Anonymization

Transform sensitive data for AI, analytics, research, testing, retention, or sharing with reduced identity exposure. Anonymization supports use cases where teams need privacy-preserving data rather than access to original values.

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Data Pseudonymization

Replace direct identifiers with pseudonyms so data can remain useful for approved processing, analysis, and longitudinal workflows while reducing exposure of identifiable values. Pseudonymization can support privacy-preserving use cases where controlled re-identification is required.

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