Onspring vs Protegrity

About Protegrity

Protegrity sells data protection that can be applied wherever sensitive information lives or flows. Protegrity protects sensitive data at rest, in motion and in use, and controls how AI agents see, share or act on it.^[4][11]

Protegrity semantic encryption creates protected equivalents designed to preserve useful meaning and relationships while reducing exposure of readable sensitive data. Dual-principal trust in Protegrity verifies both the agent and the person directing it before any data is accessed or action taken. Protegrity makes agents registered, verifiable principals, and tool calls can be permission-checked and logged with the identity and policy context behind each action. Protegrity can apply governance to model outputs and to inputs, checking responses before information reaches an agent or user. Protegrity inference controls prevent sensitive information exposure via user prompts and model responses.^[4]

Protegrity's governance layer beneath all three pillars is used to discover sensitive data, agents and tools, then define how data is protected, what agents can access and which tools they can use. Protegrity protects each data element individually as data moves from databases to prompts to agents. Protegrity's native protectors, gateways and an agent control plane plug into existing systems without moving data or rebuilding applications. Protegrity lets teams schedule recurring scans and use the results to inform policy.^[4][10]

Protegrity provides SDKs for finding and protecting sensitive data in prompts, logs and unstructured text. Protegrity offers anonymization that irreversibly de-identifies sensitive data while preserving value for analytics and model development. Protegrity governs how agents retrieve data, reason over enterprise knowledge and generate responses.

Protegrity's ML-powered discovery tools identify and classify sensitive data (PII, PHI, PCI, IP) across structured and unstructured sources. Protegrity Discovery integrates into applications, scripts and workflows through a REST API and Python SDK. Protegrity Discovery can be deployed within a customer's own infrastructure using Docker containers or managed Kubernetes support such as AWS EKS. Protegrity Discovery outputs identify the type of sensitive data along with a confidence score and location within the source text string. Documents can be scanned and scrubbed of sensitive information before vectorization to prevent PII leakage into Retrieval-Augmented Generation (RAG) pipelines and LLM prompts. Discovery results can feed Protegrity Governance to inform policy creation, refine protection rules and validate security posture. Protegrity's Anon solution anonymizes sensitive data while preserving analytical value and can be deployed across cloud or on-prem environments. Anonymization removes or generalizes identifiers using techniques that support privacy models such as k-anonymity, l-diversity and t-closeness.^[1][5][6]

Protectors can intercept data in transit via gateways or proxies, such as DSG for JDBC, without app changes. Protectors can also embed protection logic directly into custom or third-party applications via APIs/SDKs. Protegrity can tokenize or mask sensitive customer data (PII, PCI) stored in platforms such as Snowflake, BigQuery or Redshift, while preserving usability for reporting, AI and analytics. Sensitive fields such as payment info and account numbers can be dynamically masked or redacted based on user role or session context within internal applications or BI tools. Protegrity applies region-specific tokenization or other protection methods to meet data localization requirements like GDPR. Protegrity's vaultless tokenization replaces sensitive data with anonymized values at the source. PVT uses small, static token tables to create unique, random token values without a traditional vaulted token lookup table.^[1][7][8][9]

Protegrity Enterprise Security Administrator is an interface for centralized, visual administration of data security policies, key management, auditing and reporting of sensitive data assets across the enterprise. The Enterprise Security Administrator (ESA) serves as the central hub where security teams define and manage data security policies. Protegrity Data Security Gateway uses encryption and tokenization to protect sensitive data as it moves across the network. The Protegrity Application Protector for Java provides APIs for performing security operations in a client's applications. Protegrity supports any cloud or on-prem deployment of modern applications through an API with options to enhance security and performance. Protegrity partners with data storage and data analytics software providers including Amazon AWS, Microsoft Azure, Google Cloud and Snowflake. Protegrity gateways, integrations and solutions are regularly updated so its products evolve with Nacha, PCI-DSS and GDPR regulations. JustGiving chose Protegrity's data privacy solution to protect sensitive donor information and payment data and to comply with PCI DSS requirements.^{[1][4][6][8][9][10][11][12]}