GuardRail decides what your AI agents are allowed to do — before they do it. Every action is allowed, held for a person, or denied, and every decision is signed into evidence your auditors can verify. It runs privately, inside your environment.
From the first action an agent takes to the evidence your auditors ask for.
Works with the platforms your agents already run on.
Most AI governance watches agents and reports on them. GuardRail decides — and is built so its decisions can't be quietly worked around.
Agents built on Claude, OpenAI, Azure AI Foundry, Amazon Bedrock and Google Gemini stay where they are while GuardRail decides their tool calls, and Copilot Studio agents call their tools through GuardRail. Claude agents connect with just an API key. No rebuild.
If an action is approved anywhere other than GuardRail, GuardRail detects it and contains the agent within seconds — and alerts your team.
For the tools you route through GuardRail, GuardRail holds the credentials. The agent can only reach them through GuardRail — enforcement, not just monitoring.
GuardRail reads what a connected agent can do and applies rules from your compliance frameworks automatically. Your own rules can tighten them — never loosen them.
Once an agent has read outside content — a web page, an inbound email — anything it tries to send out waits for a person. Sensitive data is detected and masked.
Every decision is chained, signed and kept in write-once storage — verifiable independently of GuardRail, and exportable to your SIEM and GRC tools.
Policies, approvals and evidence for every agent you run — managed by your people, on your terms.
Rules for every agent or one agent: allow, hold or deny by action, target and conditions such as amount or destination. Test before you publish, with full version history, rollback and optional second-admin review.
Held actions reach the right person with the full context of what the agent was doing. Route to the agent's owner or a team, require two different approvers where it matters, approve from Slack.
Halt one agent or every agent at once. Nothing new runs until you release it — no code deploy, no waiting.
Session-level protection against prompt injection, sensitive-data detection and masking, and hourly rate limits per agent — they only ever make a decision stricter.
Every agent gets a risk score and a map of what it can reach. Quarantine an agent, or let GuardRail do it after repeated blocked actions; owners are alerted directly.
Find the agents already running in your cloud and Microsoft 365 tenant, with their tools and owners. New tools and changed agents are flagged and kept in each agent's history.
Single sign-on with your identity provider, two-step sign-in, roles, and teams that see only their own agents. Every agent has its own identity and key.
Auditor-ready reports, retention with legal hold, and export to Splunk, Microsoft Sentinel, syslog, Vanta and Drata. Alerts to Slack, Teams, Jira and PagerDuty.
GuardRail sits between your agents and the actions they take.
Agents connect through a gateway, an SDK, their vendor's platform or a single API call — without rebuilding them.
Each action — what the agent wants to do, on what — is checked against your rules and frameworks before it runs.
High-risk actions wait for the right person. The agent proceeds only if they approve.
Every decision and approval is written to a signed, tamper-evident record.
If GuardRail can't be reached, consequential actions are held or denied — never silently allowed.
Observability tools tell you what an agent did. GuardRail decides what it is allowed to do, at the moment it tries — and keeps the proof. That is the difference between observing AI and governing it.
We'll show you exactly where GuardRail would allow, hold or deny its actions — and the evidence it produces. Then we connect the rest.
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