Agent Security is a comprehensive knowledge platform designed to help teams secure AI agents and generative AI systems. It brings together practical frameworks, best-practice playbooks, and real-world insights to make AI security approachable and actionable.
The platform includes a rich library of threat models, vulnerability analyses, and security benchmarks that allow organizations to assess their AI systems’ resilience and compliance readiness. Users can explore policy templates, governance resources, and case studies that translate complex security challenges into clear operational steps.
By combining expert guidance with continuously updated research, Agent Security empowers companies to deploy AI safely, reduce compliance risks, and strengthen trust across their AI ecosystems.
Agent Security is a knowledge platform that helps teams secure autonomous AI agents and generative AI systems.
- for
- Enterprises building or operating autonomous AI agents and generative AI applications.
- pricing
- contact sales
Key features
6 features of Agent Security
- Threat model library — Provides a catalog of AI agent threat models, vulnerability analyses, and security benchmarks.
- Governance controls — Offers technical and policy controls for tool execution, memory protection, and permission management.
- Runtime monitoring — Continuously monitors autonomous decision chains and tool usage to detect attacks.
- Regulatory alignment — Maps AI agent security to emerging governance standards and compliance frameworks.
- Research hub — Curates academic papers, case studies, and incident reports on AI agent security.
- Policy templates — Provides ready-to-use security policies, guardrails, and governance resources.
Use cases
- Prevent prompt injection chains that cause agents to execute unintended actions.
- Protect long-term memory from poisoning that alters future decisions.
- Guard against privilege escalation where agents access higher-permission resources.
- Detect and block tool abuse that invokes unauthorized APIs or system functions.
- Monitor autonomous drift to keep agents aligned with original objectives.
Agent Security vs alternatives
 Agent Security |  LangChain |  LlamaIndex |  LangGraph |  CrewAI | |
|---|---|---|---|---|---|
| Best for | Comprehensive AI agent security framework | LLM app development framework | Data-centric LLM applications | Stateful AI agent workflows | Multi-agent AI platform |
| Pricing | Contact sales | Free | Free | Free | Subscription |
| DevHunt upvotes | 0 | 0 | 0 | 0 | 0 |
| Launched | Jan 2026 | — | — | — | — |
- Agent Security vs LangChain: LangChain is a developer framework for building LLM apps, not a security knowledge hub.
- Agent Security vs LlamaIndex: LlamaIndex focuses on data indexing for LLMs, whereas Agent Security focuses on security controls.
- Agent Security vs LangGraph: LangGraph provides stateful agent orchestration, not dedicated security guidance.
- Agent Security vs CrewAI: CrewAI is a platform for multi-agent systems, lacking the comprehensive security frameworks of Agent Security.
Agent Security FAQ
Does Agent Security support on-premises deployment?+
Yes, it offers on-premises, private cloud, and hybrid deployment options.
How does Agent Security ensure data security?+
It uses end-to-end encryption, role-based access controls, audit logging, and complies with SOC 2, GDPR, and ISO 27001.
What is the cost of using Agent Security?+
Pricing is tiered based on usage and features; contact the sales team for a customized quote.
What applications can be secured with Agent Security?+
Any AI-powered application such as chatbots, autonomous agents, RAG pipelines, multi-agent systems, and LLM-based APIs.
What’s the difference between infrastructure-level security and a guardrail?+
Infrastructure security protects network, compute, and data layers, while guardrails are runtime policies that constrain agent behavior.
Summarized by DevHunt from agentsecurity.com · Oct 8, 2026. Details may change; check the official site.
