AgentGuard Research
Practical guidance, independent reviews, and clear comparisons for teams building and securing AI agents.
AgentGuard and Lakera both address risks created when AI systems call tools, access data, and act across connected systems. The useful comparison is not a feature checklist. It is whether the control point you need is a developer's local runtime and component intake, an organization-wide agent discovery and governance layer, or a combination of both.
By Agent Guard Team6 min read
Read articleAn agent can make a bad security decision before it ever calls a tool: it can accept a package, plugin, skill, tool description, or MCP server that is not what its owner believes it to be.
By Agent Guard Team4 min read
Read articleAn LLM agent becomes a security problem when untrusted input can change a decision that reaches a tool, credential, data store, or external destination. The useful question is not which framework name applies. It is where that influence crosses a trust boundary.
By Agent Guard Team6 min read
Read articleZenity may be in the conversation because its platform is positioned around securing enterprise AI, low-code, and agentic applications. An alternative only makes sense when the buyer can name the workflow, control point, evidence, and residual risk they need.
By Agent Guard Team6 min read
Read articlePrompt injection lets untrusted text override an AI system’s intended instructions. It may come directly from users or indirectly through webpages, documents, emails, and tools. Defenses should separate data from authority, restrict permissions, validate actions externally, require approval for sensitive operations, and log decisions. Simple phrase filtering is insufficient protection.
By Agent Guard Team6 min read
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