In the scenario where the AI system integrates external data sources, Klavis AI builds a protection system through a four-fold security design: OAuth 2.0 standard protocol is used for authentication to achieve secure authorization of third-party services; HTTPS encryption is mandatory for the transport layer, and platform tests show that it can withstand 99.6% man-in-the-middle attacks; the access control implements the principle of least privilege, and each MCP instance Access control implements the principle of least privilege, and each MCP instance needs to be configured with a separate access token; audit trail function records all API call logs.
Take Resend mail service integration as an example: users first generate exclusive API key in Klavis AI console, which is encrypted and stored by SHA-256; the system automatically injects time-sensitive temporary tokens with a validity period of 15 minutes during the actual invocation; sensitive operations such as sending emails in bulk require secondary bio-authentication. The platform security test report shows that the architecture has passed the OWASP Top 10 protection validation, and the risk of key leakage of mail service is reduced by 87%.
Distinguished from the traditional risk model of direct exposure of API keys, the mechanism supports enterprise AD/LDAP docking and allows the setting of 19 fine-grained policies such as IP whitelisting and call frequency restriction. The practice of hospitals, financial institutions and other highly sensitive scenarios has proven that its annual incidence of data leakage events maintains a 0 record.
This answer comes from the articleKlavis AI: Model Context Protocol (MCP) Integration Tool for AI ApplicationsThe
