Policy enforcement
Evaluate tool access, integration permissions, and action scope before an agent reaches sensitive systems.
AI agent governance
Secure AI agents before they touch tools, secrets, and data.
botYguard is a governance layer for agentic workflows. It sits between hosted or MCP-connected agents and sensitive operations so teams can enforce policies, require approvals, protect secrets, redact private context, and audit what agents actually did.
Use botYguard when secure AI agents need access to tools, Google Workspace data, Discord workflows, provider keys, or internal systems without receiving unrestricted credentials.
Core controls
AI agent governance that runs outside the agent.
Agent prompts and local guardrails are not enough when agents can call tools, access SaaS data, and use credentials. botYguard evaluates sensitive operations at the platform boundary so secure AI agents can work without bypassing organizational controls.
The goal is not to slow every workflow down. Low-risk actions can continue under policy, while higher-risk actions can require approval workflows, stronger secret protection, privacy redaction, or a complete audit trail before and after execution.
Good AI agent governance also gives each team the same language for risk. Developers can describe the tool call they need, security teams can define the allowed scope, and operators can review the evidence after the workflow finishes. That shared boundary matters when secure AI agents move from prototypes into repeatable business processes.
Approval workflows
Route risky or high-impact requests through human or system approvals before side effects occur.
Secret protection
Keep provider keys, OAuth credentials, and private integration context out of agent prompts and instructions.
Audit visibility
Record decisions, approvals, provider access, tool calls, and outcomes for review and compliance.
Where it fits
Built for MCP and hosted agent workflows.
botYguard helps developers keep agent velocity while giving organizations operational control over permissions, approvals, integrations, and audit trails.
MCP gateway governance is useful when an external agent or Anthropic web-agent-style client needs tool access but should not hold raw provider keys. Hosted agents are useful when the team wants botYguard to run the agent in a governed runtime.
- MCP-connected agents that need governed tool access
- Hosted agents that should run under centralized policy
- Google Workspace workflows involving Drive, Gmail, or Calendar
- Discord-mediated approvals, redaction, and team chat workflows
Operational outcomes
What secure AI agent governance should prove.
A governed agent workflow should make it clear which AI agent asked for access, which policy enforcement rule applied, whether approvals were required, which secrets were protected, and what audit visibility was captured. That evidence matters for security review, incident response, and enterprise AI adoption.
botYguard keeps the agent useful while making the surrounding workflow easier to supervise. Teams can start with narrow policies, observe real requests, add approvals where risk increases, and then expand access only after the behavior is understood.
Give security teams a consistent policy enforcement point outside the agent prompt.
Let developers connect useful tools without copying secrets into agent instructions.
Use approval workflows when write, share, delete, or provider actions need review.
Preserve audit trails that show what the agent requested, what policy decided, and what ran.
Clear positioning
botYguard is not a moderation product.
botYguard focuses on AI agent security and governance: controlling what agents can access, when approval is required, how secrets are passed, and what evidence teams can review after execution.
That focus keeps botYguard aligned with platform, security, and operations teams that need secure AI agents to take useful actions without creating unmanaged access paths. It keeps reviews concrete and repeatable.