Blocks destructive file actions
Stops delete or overwrite requests before protected files are touched.
Local-first AI action control
Control AI actions before they touch files, systems, or data.
Guardian Gate is a local-first control layer that allows, blocks, or escalates sensitive AI actions before execution.
Built for teams deploying action-taking AI into real workflows, where file changes, commands, and outbound calls can cause operational damage if they run unchecked.
Action decision layer
Action
Write updated payroll summary to
/reports/march.csvPolicy
Allow writes only inside approved workspace paths
Decision
Allowed
Action
Delete customer export archive
Decision
Blocked
Action
Call external billing API with account data
Decision
Approval required
Requires approval for sensitive operations
Routes higher-risk actions to a human decision instead of executing on trust.
Gates risky outbound requests
Checks destinations before agents send data to external domains or endpoints.
Keeps protected paths off-limits
Restricts reads, writes, and commands to approved workspace boundaries.
Product flow
One layer between AI intent and execution.
Every sensitive request is checked before it runs.
01
The AI requests an action
A model or agent requests a file, command, or network operation.
02
Guardian Gate checks it
It checks policy, path, destination, and approval requirements before execution.
03
A decision is enforced
The action is allowed, blocked, or escalated before the environment is touched.
When Guardian Gate Becomes Necessary
When AI stops being just chat and starts operating in real workflows.
Guardian Gate becomes necessary when agents can change files, run commands, call services, or cross workflow boundaries in environments where a wrong action creates operational consequences.
Destructive file changes
When an agent can overwrite reports, delete shared files, or modify workspace data inside a live workflow.
Unsafe command execution
When a copilot or agent can run shell commands that reach the wrong host, path, or environment.
Outbound calls to the wrong destination
When AI can send data to internal services, third-party APIs, or unapproved external domains.
Agents operating outside scope
When workflow assistants can act beyond intended boundaries without the right approval or review checkpoint.
What actions it controls
Built for the actions that create operational risk.
For action-taking AI deployed into workflows where file changes, command execution, and outbound requests need control before they run.
Read files
Restrict which directories or file types an AI system can inspect.
Write files
Allow updates only in approved locations and workflow-specific scopes.
Delete files
Block destructive operations or require explicit human approval.
Run commands
Check shell or system execution before it reaches the host environment.
Call websites
Limit outbound requests for copilots and agents to trusted destinations.
Call HTTP endpoints
Apply destination checks before agents send data to internal or external services.
Why a control layer matters
Why buyers ask for action controls.
These examples show what can happen when AI executes without checks on files, systems, or destinations.
Hundreds of emails affected
An AI agent reportedly deleted or archived hundreds of emails.
Even familiar business systems can be affected quickly when autonomous actions are allowed to proceed without policy checks.
2.5 years of data lost
A coding agent reportedly wiped years of stored work.
Write and delete permissions need strong boundaries before agents are allowed to touch valuable project data.
Live database deleted
An AI coding agent reportedly removed a production database.
High-impact actions need an approval or policy checkpoint before an agent can reach live systems.
Unsafe agent behavior
Research has highlighted manipulative and unsafe autonomous actions.
The risk is broader than a single workflow. Teams need controls between AI intent and execution across files, tools, and endpoints.
Isolation was advised
Some agent systems were serious enough that isolated environments were recommended.
Isolation reduces exposure, but teams still benefit from a dedicated decision layer around what actions are allowed at all.
Private data at risk
Reports have described autonomous systems leaking sensitive details or deleting files.
Outbound requests and file operations both need explicit controls when AI is connected to internal or customer data.
Who it is for
Built first for teams deploying action-taking AI into real workflows.
That is the primary fit. The audiences below are common versions of that same deployment problem.
Primary fit: workflow deployment teams
Teams putting AI into live workflows where it can touch files, trigger actions, call services, or operate inside business and technical environments.
Internal automation teams
Teams deploying assistants that update records, handle files, and trigger downstream systems inside the business.
Companies shipping agent products
Products that give AI live access to customer workspaces, tools, or connected endpoints.
Engineering and operations teams
Teams that need command checks, scope boundaries, and approval gates before AI affects production systems or data.
Contact us
Show us the actions you need to control.
Share your workflow, environment, and risk points. We will tailor the demo around the files, commands, tools, or services your AI can reach in production-like workflows.
Useful examples: workflow automations, coding agents, tool-using copilots, approval boundaries, or endpoint restrictions.
Watch the demo
See Guardian Gate in the risky part of AI deployment.
See how it blocks destructive actions, checks commands and destinations, and adds review boundaries before execution.