Which MCP Security Tool Fits Your Stack? 7 Options Compared
Compare seven MCP security tools by control point, operating boundary, and proof requirements, from scanners to gateways and runtime controls.
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Security thinking for the agentic era.
Practical guidance, independent reviews, and clear explanations for teams building AI agents that can take real action.
AI Runtime Security Tools: Choose by What They Can Actually Control
Compare seven AI runtime security tools by enforcement point, documented scope, best fit, and the POC needed to verify agent action control.
Read articleBest AI Security Companies for Agent Control
Compare six AI security companies by the control point they document: discovery, inspection, action enforcement, and investigation evidence.
Read articleWhat is Prompt Injection & How it Works
Prompt 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.
Read articleChoosing a Zenity Alternative for AI Agent Security
Zenity 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.
Read articleHow LLM Agents Are Exploited: Attack Paths and Defenses
An 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.
Read articleAgent Dependency Pollution: A Working Definition
An 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.
Read articleAgentGuard vs Lakera: What Each Product Actually Covers
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.
Read articleCan You Trust an MCP Response? Four Failure Paths to Test
Prevent MCP response spoofing by separating endpoint identity, message integrity, prompt manipulation, and compromised-server behavior.
Read articleHow Enterprises Can Secure AI Agents in Production
Learn how to secure enterprise AI agents across identity, tools, runtime actions, memory, monitoring, and governance with a practical rollout checklist.
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