Compare Liminal vs. Velum Privacy

Liminal gives regulated organizations a single governed environment for AI. Rather than securing models an organization has already deployed, Liminal supplies the access itself, so protection is applied before anything reaches a provider. Employees work with leading models from Anthropic, OpenAI, Google and others through one interface, with policy-based routing and no per-provider contracts. Adding a new model becomes a policy decision rather than an integration project, and teams are never stuck waiting for procurement to catch up with what the market released. Sensitive terms are identified within each prompt and masked, redacted, flagged or blocked according to rules defined by data category rather than by department. Masked values are restored in the response, which keeps output usable while regulated information stays inside the organization. Employees never have to sanitize inputs by hand, which is what makes the governed path the one they actually choose. Administrators see who used which model, what rules were applied and where violations occurred. That record is generated at the time of the interaction, so audit requests are answered from existing evidence instead of reconstruction after the fact. Activity can be fed into existing security tooling and reviewed alongside other events rather than in isolation. The platform connects to internal systems through MCP, so employees bring organizational context into their work without moving data into tools nobody is watching. Access follows permissions that already exist. Deployments run on single-tenant infrastructure with SSO and role-based permissions, serving banking and credit unions, insurance, healthcare, education, state and local government, and life sciences.

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.