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Secure and monitor AI agent interactions via MCP to prevent data leaks.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "open-edison" yet — see the docs or source repo.
Design a data firewall policy for an AI agent accessing an internal database via MCP: allow only masked customer data, block exports of phone numbers and national ID numbers, and log all high-risk requests.
A practical access-control and audit policy with clear allow, block, and monitoring rules.
Create an MCP tool-usage monitoring plan to track when an AI agent accesses the file system, calls external APIs, or executes scripts, and flag anomalous behavior.
A monitoring plan covering key events, risk indicators, alert conditions, and audit logging recommendations.
Using the principle of least privilege, define permission boundaries for an AI agent that can access documents, databases, and internal services via MCP, and explain how to reduce data leakage risk.
A layered permission model with risk-mitigation recommendations for securely deploying AI agents.
Protect AI agents with scans for injections, leaks, risky URLs, and passwords.
Detect prompt injections and jailbreaks to secure LLM applications and workflows.
Secure AI agents locally with cost controls, injection blocking, and action approvals.
Simulate adversarial attacks to test AI agents and MCP server security.
Provide signed, verifiable risk verdicts for every AI-driven payment action.
Connect AI assistants to Opendock Neutron API for dock operations management.