Compare IRI DarkShield vs. Velum Privacy in 2026

Velum Privacy

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Description

IRI DarkShield uses several search techniques to find, and multiple data masking functions to de-identify, sensitive data in semi- and unstructured data sources enterprise-wide. You can use the search results to provide, remove, or fix PII simultaneously or separately to comply with GDPR data portability and erasure provisions. DarkShield jobs are configured, logged, and run from IRI Workbench or a restful RPC (web services) API to encrypt, redact, blur, etc., the PII it discovers in: * NoSQL & RDBs * PDFs * Parquet * JSON, XML & CSV * Excel & Word * BMP, DICOM, GIF, JPG & TIFF using pattern or dictionary matches, fuzzy search, named entity recognition, path filters, or image area bounding boxes. DarkShield search data can display in its own interactive dashboard, or in SIEM software analytic and visualization platforms like Datadog or Splunk ES. A Splunk Adaptive Response Framework or Phantom Playbook can also act on it. IRI DarkShield is a breakthrough in unstructured data hiding technology, speed, usability and affordability. DarkShield consolidates, multi-threads, the search, extraction and remediation of PII in multiple formats and folders on your network and in the cloud, on Windows, Linux, and macOS.

Description

Velum Privacy is designed to protect personal data before it interacts with an AI assistant, subsequently restoring the actual values in the response, all while operating solely on your machine without the need for servers, accounts, or any telemetry. The system is inherently reversible; it employs one-way redaction to secure the data, although this would typically compromise the response, yet a local mapping ensures that the returned reply remains intelligible. This solution comprises three distinct interfaces integrated into a single engine: a browser extension compatible with Chrome, Brave, Edge, and Vivaldi that offers text masking without requiring an account; a macOS desktop application that manages documents, folders, and coding tasks through MCP, with a Windows version currently under development; and an application for Jira and Confluence Cloud that operates within your own Atlassian environment. Detection is achieved through a combination of pattern recognition, checksum validation specifically for IBANs and national IDs, and a local named entity recognition model that supports nine language variations, with published accuracy metrics highlighting a remarkable 99.8% must-mask F1 score at the highest level, including failure rates. The commitment to transparency is evident, as the extension specifies zero host permissions, while the desktop application meticulously lists every outbound connection and ensures masking occurs even when the network is offline, reinforcing the integrity of the system. Such comprehensive measures ensure that users can maintain control over their sensitive information while enjoying the benefits of AI technology.