Agent Security
Discover, test, and protect AI agents, models, prompts, and connected tools.
Agent security products address the risks created when models read external inputs and act through connected tools. The scope includes discovery, adversarial testing, prompt and data protections, and runtime controls. Compare where a product sits in the architecture and which threats it covers. Monitoring a trace is useful evidence, but does not necessarily block an unsafe action. Evaluate detection and enforcement separately, using representative attack cases and the permissions your agents actually receive.
Compare by workflow
- Threat coverage
- Permission controls
- Testing and reporting
- Deployment options
Shortlist by use case
| Tool | Consider it for |
|---|---|
| HiddenLayer | AI security testing and runtime protection |
| Lakera | Agent discovery and runtime security |
| Noma Security | AI and agent security posture and controls |
| Mindgard | AI security testing and risk assessment |
| Straiker | AI application and agent protections |
| Fiddler AI | Agent monitoring and guardrails |
Explore all Agent Security
How to choose
- Map the agents, models, data sources, and tool connections that need protection.
- Separate pre-deployment red teaming from runtime detection and blocking.
- Evaluate false positives, incident evidence, and the operational effect of an unavailable security service.
Frequently asked questions
- Does observability alone provide agent security?
- Observability records behavior. Security can also test attacks, enforce permissions, and block or remediate unsafe behavior.
- Do all products protect MCP connections?
- Coverage varies. Check the specific discovery and runtime controls offered for your MCP servers and connected tools.
Last reviewed:
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