Safeguard vs. Griffin

Safeguard is an AI-powered application and software supply chain security platform designed to move security teams from vulnerability detection to automated remediation. The platform continuously inventories repositories, containers, software packages, dependencies, and other assets to create a live view of an organization's software environment. Griffin AI combines SBOM data, abstract syntax trees, call graphs, reachability analysis, exploit information, and business impact data to identify which vulnerabilities represent meaningful risk. Instead of generating a ticket for every finding, Safeguard can suppress unreachable issues and focus remediation efforts on vulnerabilities that can actually affect production systems. For actionable problems, its agents can author code changes or dependency upgrades, run automated testing, verify compatibility, and submit or merge pull requests under organization-defined policies. Safeguard also uses its Eagle adversarial model to search for zero-day vulnerabilities and test the validity of findings and proposed patches. Compliance capabilities automatically connect security activity and evidence to hundreds of frameworks while supporting SBOM formats such as CycloneDX and SPDX and recording SLSA provenance. The broader platform includes runtime defenses for AI systems, secure coding assistance, hardened zero-CVE packages and container images, Trust Centers, third-party risk management, and continuous security monitoring. Developers can work with Safeguard through IDEs, CLIs, source-control systems, CI/CD pipelines, cloud integrations, and more than 25 tenant-scoped MCP tools available to AI agents. Every automated action is designed to be logged, attributable, auditable, and governed by the same policies used throughout the platform.

Griffin is recognized as Tavus’s first Human Interaction Model, an advanced full-duplex video-to-video system designed to interpret and generate real-time, face-to-face human engagements. Unlike traditional conversational systems that address speech recognition, language modeling, and speech synthesis in a step-by-step process, Griffin functions by constantly processing audio and video inputs, making prompt decisions for responses, and simultaneously providing expressive verbal and visual feedback. It pays close attention not only to the verbal content but also to the nuances of tone, eye contact, facial expressions, gestures, pauses, interruptions, and the overall visual context. This model demonstrates functionality such as backchanneling, yielding or taking conversational turns, effectively managing interruptions, staying engaged during silences, and modifying its emotional tone and nonverbal signals as the conversation progresses. Additionally, Griffin can create a rich visual scene based on a reference image, encompassing aspects like facial features, body movements, hand gestures, chair positioning, shadows, and background details, which enables it to offer a more immersive experience beyond merely presenting a talking head. The integration of these capabilities significantly enriches the quality and realism of human-like interactions, making conversations feel more genuine and engaging.