Gray Swan AI helps organisations understand how AI systems can be attacked, jailbroken, or manipulated. RAGuard helps organisations enforce safe behaviour when AI agents operate in production.
Pressure-tests models and agentic systems to expose the ways they can be manipulated before incidents happen.
Applies policy continuously when deployed agents retrieve context, call tools, and operate inside enterprise workflows.
Research-led adversarial testing, vulnerability discovery, attack realism, frontier model evaluation, and red-team credibility.
Runtime enforcement, governance control, MCP and tool mediation, audit evidence, and always-on operational controls for production AI systems.
| Question | Gray Swan AI | RAGuard |
|---|---|---|
| Is the focus AI red teaming? | Yes | Moderate via research and evaluation |
| Does it discover adversarial weaknesses? | Yes | Yes, through research-led evaluation |
| Is it primarily a runtime governance layer? | No | Yes |
| Does it enforce AI policies in production? | Limited | Yes |
| Does it govern MCP and tool execution? | Moderate | Yes |
| Does it provide governance evidence? | Moderate | Yes |
| Is it designed for continuous production mediation? | Limited to moderate | Yes |
AI red teaming is becoming essential for enterprise adoption. Organisations need to know how their systems behave under adversarial pressure, especially when agents use tools, retrieve data, or operate across workflows. Gray Swan is strong in that discovery space.
RAGuard addresses what comes after discovery. Once an AI system is deployed, enterprises need to enforce what it can do, which tools it can call, which instructions it can trust, and how those decisions are retained for governance.
You need adversarial evaluation, vulnerability discovery, or external validation of how models and agents can be manipulated.
You need runtime enforcement, MCP governance, tenant-aware policy, and production evidence of why agent behaviour was allowed or blocked.