Protect AI is strong across the AI and ML lifecycle. RAGuard focuses on the narrower but increasingly critical layer where AI agents act, invoke tools, access data, and execute workflows in production.
Focuses on models, datasets, registries, pipelines, and enterprise posture across the broader AI estate.
Controls how deployed agents retrieve context, call tools, and justify their runtime decisions with evidence.
Broad lifecycle AI and ML security, model and AI asset protection, MLSecOps programs, and enterprise-wide AI posture management.
Runtime agent specificity, tool-call governance, instruction provenance, policy-as-code enforcement, and evidence for governed production AI behaviour.
| Question | Protect AI | RAGuard |
|---|---|---|
| Does it cover AI/ML lifecycle security? | Strong | Limited to moderate |
| Does it support model and AI asset security? | Strong | Limited |
| Is runtime governance the primary focus? | Moderate | Strong |
| Does it mediate AI tool execution? | Limited to moderate | Strong |
| Does it focus on MCP workflows? | Emerging | Strong |
| Does it track instruction provenance? | Limited | Strong |
| Does it provide audit-oriented runtime evidence? | Moderate | Strong |
AI security programs need lifecycle controls. Models, datasets, pipelines, notebooks, registries, and AI applications all create security exposure. Protect AI is positioned around this broader MLSecOps need.
RAGuard addresses what happens after deployment. When agents call tools, maintain state, and execute work on behalf of users, enterprises need explicit runtime trust boundaries and policy decisions that can be reviewed later.
You need broad AI and ML lifecycle security, model scanning, asset protection, and support for a formal MLSecOps program.
Your AI systems call tools or APIs, you need runtime policy enforcement, and you want evidence of why an AI action was allowed or blocked in production.