Jozu vs. Enkrypt AI

Jozu serves as an AI-powered platform dedicated to enhancing the security of supply chains by confirming the integrity of artifacts before they are executed, overseeing agent activities in real-time, and keeping a detailed log of all subsequent actions. The Jozu Hub functions as a self-contained repository for models, agents, MCP servers, and skills, guaranteeing that each artifact is integrated with cryptographic signatures, attestations, comprehensive scans, policy compliance, and audit records. This platform's security assessment, specifically designed for AI applications, tackles numerous threats such as hidden executable code within model packages, compromised weights, data poisoning, prompt injection, insecure tools, and licensing breaches. Users can establish policies once, which are then distributed as signed OCI artifacts, with enforcement occurring during the actions of pulling, promoting, admitting, or executing these artifacts. Furthermore, Jozu Agent Guard collaborates seamlessly with workloads across servers, desktops, edge devices, and isolated systems, applying local filtering for prompts and input-output, implementing access controls for tools, requiring approvals, and enforcing policies in real-time. By adopting this all-encompassing strategy, Jozu significantly boosts security while also providing a resilient framework for managing and protecting AI-related artifacts throughout their entire lifecycle. Ultimately, this ensures that users can trust the integrity and compliance of their AI systems.

Enkrypt AI is an agentic AI security and compliance platform that helps enterprises identify, mitigate, monitor, and govern risks associated with AI models, applications, agents, and supporting infrastructure. The platform is designed to cover AI security threats and compliance requirements across different models and modalities while allowing organizations to continuously evaluate AI systems as they evolve. Its dynamic red teaming capabilities generate use-case-specific attacks that adapt in real time to uncover security, safety, compliance, and brand risks beyond those identified through static testing. Enkrypt AI can test for threats including prompt injection, jailbreaking, data leakage, model inversion, adversarial attacks, drip attacks, unsafe actuation, policy violations, and misuse by end users. It also evaluates AI quality and safety risks such as hallucinations, model drift, bias and discrimination, toxic content, deceptive behavior, misleading outputs, lack of explainability, and brand misalignment. Real-time guardrails detect and stop risky interactions as they occur while adapting to new prompts, behaviors, and emerging threats. Continuous monitoring gives security and compliance teams visibility into AI risks, protection effectiveness, and areas requiring action as models and applications change. Enkrypt AI's compliance functionality converts internal policies and external regulatory requirements into automated guardrails and generates evidence that can support audits and compliance programs. The product portfolio includes Agent Red Teaming, Agent Guardrails, an Agent Policy Engine, AI Data Risk Audit, MCP Scanner, and MCP Gateway for protecting AI agents, their data, and their connections to tools and infrastructure. Enkrypt AI supports use cases including customer-facing AI agents, frontier models, AI compliance and auditing, third-party AI risk, secure AI development, data risk assessment, and MCP security.