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Agentic AI Security covers the tools that secure autonomous AI agents, the MCP servers and tools they connect to, and the multi-agent workflows now running inside enterprises. The hard problem is that an agent is a non-human identity that reasons, calls APIs, spends money, and acts on your behalf, often with broad permissions and almost no oversight. This category is for security leaders watching agents reach production faster than anyone can govern them, who need visibility into what those agents can do, controls on what they should do, and a way to catch prompt injection, tool poisoning, and runaway behavior before it becomes an incident.
We cover 125 Agentic AI Security tools, 11 free and 114 commercial.
Accuracy and depth improve over time. Last reviewed Jul 2026. Is something off? Reach out.
MCP governance platform for securing and controlling enterprise AI agents.
AI agent testing platform for security, reliability, and behavior validation.
API gateway for managing, securing, and observing outbound LLM traffic.
Gateway for securing, governing, and auditing AI agent access to MCP servers.
Proxy layer for controlling and monitoring MCP server access in AI apps.
AI control plane for enterprise AI agent security, governance, and observability.
Security & governance platform for evaluating and securing enterprise AI systems.
Agentic AI security platform for inventory, posture mgmt, and threat detection.
Runtime AI trust & security platform for governing agentic AI systems.
Secure gateway platform for governing AI agent MCP server access in enterprises.
Scans and catalogs AI agent skills/plugins for security vulnerabilities.
Open-source CLI tool to map, threat-model, and secure AI agent workflows.
Security gateway for monitoring and protecting MCP-based AI agent tool calls.
Context-aware access control for AI pipelines, LLMs, and multi-agent workflows.
Runtime security platform for protecting AI-powered apps and agentic AI.
Secures Salesforce Agentforce AI workflows via visibility, monitoring & governance.
Privacy-preserving AI agent platform for running LLMs on sensitive data.
Secure, identity-integrated access control platform for GPU and AI infrastructure.
Secures MCP sessions in AI dev environments via proxy, discovery, and policy enforcement.
Secures AI-assisted dev environments from prompt injection, DLP, & shadow AI.
Governs autonomous AI agents with context-aware authz, policy control & audit.
Security audit service for agentic AI systems via threat modeling & red teaming.
Runtime security governance for AI agents operating over MCP environments.
AI agent governance platform securing MCP traffic, prompts, and data access.
Tool roundups, buying guides, and strategic analysis from the CybersecTools resource library.
Common questions about Agentic AI Security tools, selection guides, pricing, and comparisons.
Agentic AI security is the discipline of securing autonomous AI agents and the systems they touch: the MCP servers and tools they call, the data they read, and the actions they take without a human in the loop. It covers agent identity and authorization, runtime guardrails, prompt injection defense, tool and connector validation, and observability across multi-agent workflows. The aim is to keep an agent doing only what it was meant to do.
LLM security mostly worries about the model: prompt injection, jailbreaks, data leakage, and unsafe outputs. Agentic AI security inherits all of that and adds the dangerous part, which is that an agent acts. It holds tools, credentials, and the autonomy to chain decisions and trigger real-world effects. The risk shifts from a bad answer to a bad action: deleting records, moving money, or exfiltrating data through a poisoned MCP tool.
The Model Context Protocol is becoming the default way agents discover and call external tools, which makes MCP servers a fresh, high-value attack surface. Threats include tool poisoning, where a malicious tool description manipulates the agent, plus overbroad scopes, unauthenticated servers, and confused-deputy problems. Tools here inspect MCP traffic, gate which servers and tools an agent may use, and watch for instructions hidden in tool metadata or returned content.
Begin with where your agents actually run: a CrewAI or LangGraph build needs different coverage than a vendor SaaS copilot or an MCP-heavy setup. Decide whether you need governance and discovery, runtime enforcement, or both, since few tools do everything well. Confirm it treats the agent as a first-class non-human identity, intercepts tool calls without killing latency, and feeds your SIEM and IAM stack rather than becoming another silo.
Your existing stack helps but does not cover it. CASB, DLP, and SSPM tools were never built to read agent reasoning, validate MCP tool descriptions, or enforce per-action authorization on a non-human identity that improvises. Some agent security capabilities are arriving as modules inside CNAPP, identity, and data-security platforms, so before buying standalone, check whether a tool you already own is shipping agent coverage that fits your environment.