Compare Jozu vs. Liminal

Jozu functions as an AI-driven platform focused on securing supply chains by validating artifacts prior to their execution, managing agent activities in real-time, and maintaining a record of all actions taken afterward. The Jozu Hub acts as a self-hosted repository for models, agents, MCP servers, and skills, ensuring that each artifact is consolidated with cryptographic signatures, attestations, thorough scanning, policy regulations, and audit trails. This platform's security analysis, tailored specifically for AI, addresses various threats including concealed executable code within model packages, compromised weights, data poisoning, prompt injection, insecure tools, and violations of licensing. Users can create policies once, which are then distributed as signed OCI artifacts, and these policies are enforced during the processes of pulling, promoting, admitting, or executing artifacts. Additionally, Jozu Agent Guard operates in conjunction with workloads across servers, desktops, edge devices, and isolated systems, implementing local filtering for prompts and input-output, access controls for tools, requirement for approvals, and enforcement of policies in real-time. Through this comprehensive approach, Jozu not only enhances security but also ensures a robust framework for managing and safeguarding AI-related artifacts throughout their lifecycle.

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.