In this installment of our MCP blog series, our CPO Dana Ritter explores how the Unique MCP Hub is engineered with a security-first mindset, delivering enterprise-grade protection through integrated data loss prevention, proactive anonymization, and immutable audit capabilities. See more product-related content from Dana on his blog.
In the first two parts of this series, we explored the vision for the Unique Multi-Connector Platform (MCP) Hub and how it brings order to the chaos of enterprise AI through orchestration and governance. But in the world of financial services, none of that matters without a foundation of uncompromising security. When you're dealing with sensitive client data, market-moving information, and strict regulatory oversight, security isn't just a feature, it's the bedrock of trust.
That’s why we’re designing the MCP Hub with a security-first principle, engineering it to be the Fort Knox for our customers' AI operations.
Our internal PRD specifies a multi-layered security architecture designed to protect data at every stage of the AI workflow. Two of the most critical layers are Data Loss Prevention (DLP) and Proactive Anonymization.
For our customers, preventing the inadvertent leakage of sensitive information is a non-negotiable requirement. The MCP Hub addresses this head-on by integrating directly with enterprise-grade DLP solutions like Microsoft Purview.
Every request that passes through the Hub, before it ever reaches an external model or tool, is subject to DLP scanning. This isn't an optional add-on; it's a core part of the data flow. Our goal is clear:
Achieve >=95% of outbound requests covered by DLP scanning; 0 critical DLP incidents post-GA.
This means that if a user accidentally includes a client's account number in a prompt or tries to send a confidential document to an unauthorized external tool, the Hub will block the request before it leaves the secure perimeter. It’s a critical safeguard that moves our customers from a reactive to a proactive security posture.
Beyond just blocking sensitive data, we're building intelligent systems to redact it. The Hub will include a Prompt Anonymizer that uses a combination of pattern matching and machine learning to identify and scrub personally identifiable information (PII) and other secrets from prompts before they are processed.
Furthermore, every single action taken through the Hub is recorded in immutable audit logs. These aren't simple text files; they are cryptographically signed, write-once-read-many (WORM) records that provide a complete, tamper-proof history of AI usage. This level of auditability is essential for compliance reviews, security investigations, and demonstrating regulatory adherence.
By combining deep DLP integration, intelligent data anonymization, and comprehensive auditing, the MCP Hub provides the robust security framework that financial institutions need to innovate with AI safely. In the next post, we'll shift our focus to the other side of the equation: ensuring that this secure and well-governed platform delivers a seamless and powerful experience for the end-user.