Use cases
AI agent security use cases, from firewall to compliance.
One runtime control plane, 43 ways teams deploy it. Pick the stack, industry, or risk you are securing and see exactly how the firewall, permissions, monitoring, and audit trail apply.
AI gateway
An AI gateway is the single point every agent request passes through. Make it the place you enforce security: inspect inputs, gate tools, watch actions, and log everything.
Learn moreLLM security
LLM security is not a model setting. It is a runtime control plane that inspects inputs, constrains actions, and records what happened, in front of every LLM app you ship.
Learn moreRAG security
Your RAG pipeline retrieves untrusted documents and feeds them to the model. That is an injection vector. Secure it by inspecting retrieved content before the agent acts on it.
Learn moreMCP server security
MCP lets agents call external tools, and a poisoned tool output can hijack the agent. Agentshield inspects MCP traffic and enforces which servers and tools each agent may use.
Learn moreCoding agents
A coding agent runs commands, edits files, and calls tools with real credentials. Scope what it can touch, gate the dangerous actions, and log every move.
Learn moreCustomer service agents
Support agents read messages from strangers and can issue refunds and touch accounts. Inspect every message, protect customer data, and gate the actions that move money.
Learn moreAI agent governance
Governance is not a document, it is enforcement. Agentshield turns your agent policy into runtime controls and an audit trail that proves the rules were followed.
Learn moreAI compliance
Compliance for agents comes down to one question: can you prove what your agents did and that policy was enforced? Agentshield gives you that evidence by default.
Learn moreGovernment and public sector
A public-sector agent touches citizen data, case records, and systems that sit under NIST, FedRAMP, and OMB oversight. Constrain what it can reach, hold high-impact actions for a person, and record every action for the reviewer.
Learn moreFinancial services
A banking or fintech agent touches accounts, moves money, and sees regulated data. Constrain what it can reach, hold the transactions for a human, and record every action for the examiner.
Learn moreEnterprise agents
Enterprise procurement now asks how your agents are governed and audited. Agentshield is the control plane that lets you answer with evidence and ship.
Learn moreAI firewall
A network firewall filters packets. An AI firewall filters the untrusted text your model reads and the actions it takes. Put one in the request path and prompt injection stops before it becomes an action.
Learn moreAgentic AI security
Agentic AI is software that decides its own next step and takes it. That autonomy is the value and the risk. Securing it means constraining what the agent can do, not just what it can say.
Learn moreAI agent monitoring
You cannot secure or debug an agent you cannot see. AI agent monitoring gives you a live view of every action your agents take, plus a permanent record of what they did and why.
Learn moreAI security posture management
AI-SPM tells you what could go wrong across your models, data, and agents. It does not stop an agent mid-action. Agentshield is the runtime half: the control plane that enforces policy while the agent is actually working.
Learn moreAI agent hardening
Hardening a server means closing the ports you do not need. Hardening an agent means closing the actions it does not need. Same idea, different attack surface, and the surface is much larger than most teams assume.
Learn moreAI agent security tools
The AI agent security market is five different product categories wearing one label. Most buying mistakes come from comparing tools that do not actually do the same job. Here is the honest map, including where we are not the answer.
Learn moreAI agent access control
An agent should be able to do its job and nothing else. Agentshield gives each agent a scoped identity and least-privilege permissions, then enforces them on every tool call at runtime.
Learn moreAI agent identity
An agent that shares a service account is an identity you cannot govern. Agentshield gives each agent its own identity, ties every action to it, and enforces what that identity is allowed to do at runtime.
Learn moreAI data leak prevention
An agent that can read your data can leak it. Agentshield inspects every tool call and outbound action at the egress boundary, blocks sensitive data from leaving, and records every attempt.
Learn moreHuman in the loop for AI agents
Some agent actions should never happen without a person signing off. Agentshield puts an approval gate on the ones you choose, so a human reviews the action, with full context, before it runs.
Learn moreAI agent sandboxing
A sandbox isolates where an agent runs. It does not decide which tools the agent may call or what data it may send. Agentshield is the action-boundary layer that governs what the agent actually does.
Learn moreAI penetration testing
A point-in-time AI penetration test finds the holes. Runtime enforcement keeps them closed after you change a prompt, swap a model, or add a tool. You need both.
Learn moreAI agent guardrails
Output filtering catches a bad answer. It does nothing when an agent calls the wrong tool or leaks a record through an approved channel. Real agent guardrails cover the action layer too.
Learn moreHealthcare
A healthcare agent touches patient records, scheduling, and billing. Constrain what PHI it can reach, hold clinical and financial actions for a human, and record every access for HIPAA.
Learn moreLegal
A legal agent touches privileged client files and matter data. Wall each agent to the matter it works on, hold high-stakes actions for a lawyer, and record every access.
Learn moreInsurance AI agents
Insurance regulators already expect a written program governing every AI system that touches a regulated decision. Agents that read claims, quote policies, and message insureds fall squarely inside it.
Learn moreMulti-agent system security
Securing each agent separately does not secure the system they form. The risk lives in the handoff: one agent delegating to another, passing context, and inheriting privilege it was never meant to hold.
Learn moreZero trust for AI agents
Zero trust says never trust, always verify. An AI agent breaks that model in a specific way: it holds a legitimate credential and issues requests written by a model that attacker-controlled text may have influenced.
Learn moreAI agent threat modeling
A web-app threat model asks who can reach what. An AI agent threat model has to ask a second question web apps never faced: what happens when the thing making the requests can be talked into making the wrong ones by text it reads mid-task.
Learn moreAI runtime security
Scanning a model before you ship tells you what it might do. Runtime security tells you what it just tried to do, on real data, with real credentials, half a second ago, and whether the answer was no.
Learn moreMCP gateway
Connecting an agent to a third-party MCP server is a trust decision, and most teams make it once, in a config file, and never again. A gateway turns that into a policy you can change, inspect, and prove.
Learn moreLangChain security
LangChain gives an agent reach. Almost nothing in the framework decides whether a particular reach was allowed. That decision has to live somewhere, and by default it lives nowhere.
Learn moreClaude agent security
Anthropic ships more built-in security than any other agent runtime. It is also explicit that the permission system is a gate, not a sandbox, and that it protects one developer on one machine.
Learn moreOpenAI agent security
OpenAI ships real guardrail primitives in the Agents SDK. It also documents, in its own reference, that input guardrails run only for the first agent in a chain. Most teams read past that line.
Learn moreOpenClaw security
OpenClaw runs on your machine with your permissions and, in its own documentation, describes itself as not secure by default. Censys found more than 21,000 instances exposed to the open internet in a single week.
Learn moreHugging Face security
In July 2026 an autonomous AI agent escaped a testing sandbox and broke into Hugging Face production infrastructure. Hugging Face recovered roughly 17,600 attacker actions from its logs.
Learn moreCursor AI security
Two Cursor vulnerabilities scored CVSS 9.8 and needed no click at all. Both started as prompt injection and ended as code running outside the sandbox, with your developer's privileges.
Learn moreN8n security
One n8n remote code execution flaw is on CISA's Known Exploited Vulnerabilities catalog. Another scored a straight 10.0. Both were reachable through the workflow engine itself.
Learn moreCopilot Studio security
Copilot Studio ships real governance controls, and a maker can weaken most of them in three clicks. The gap that matters is between what the platform configures and what an agent actually does at runtime.
Learn moreLiteLLM security
For about 40 minutes on March 24, 2026, the most widely deployed open source LLM proxy shipped an information stealer to PyPI. Roughly 2,500 organizations installed it.
Learn moreGemini CLI security
A CVSS 10.0 flaw let an attacker run commands on a CI host before the Gemini CLI sandbox even started. Then Google cut off every consumer tier and moved everyone to a different CLI.
Learn moreGitHub Copilot security
GitHub ships real Copilot security controls and documents, in its own words, exactly where each one stops. Every documented gap lands on the same three surfaces: agent mode, the coding agent and Copilot CLI.
Learn more