MCP Gateway Comparison for Security Leaders

By Eito Miyamura and Ilia Manolov

Overview

Model Context Protocol (MCP) gateways are centralized repositories that standardize and synchronize configurations across organizations. While they solve operational challenges in enterprise AI deployment, security leaders must understand their protective capabilities and limitations.

Key Security Gaps

The authors identify critical vulnerabilities in current MCP gateway offerings from Docker and IBM:

Observability Challenges Without proper monitoring, organizations might not detect issues like denial-of-service conditions caused by infinite MCP call loops for weeks. Real-time detection is essential for rapid incident response.

Data Exfiltration Risks When large language models connect to enterprise tools through MCP, they create potential vectors for data leaks. The authors reference their previous research showing these systems can be exploited to leak company data or escalate privileges, threats traditional security controls weren't designed to address.

Multi-Tenancy Deficiencies Poor isolation between users and teams sharing infrastructure can lead to credential hijacking. For example, a shared OAuth server could expose one user's credentials to another.

Essential MCP Gateway Features

Security leaders should evaluate gateways on these dimensions:

Real-time observability beyond basic logging
Runtime security approvals at the gateway level for sensitive operations
Proper multi-tenancy with credential isolation
Self-hosting and on-premises options for data residency requirements
Role-based access controls (RBAC) enabling granular tool restrictions
Integration with existing security platforms like Splunk

A runtime security-approval notification flagging a Claude agent trying to send email to an external address, with deny and approve actions.

Runtime security approval as a notification: the gateway resolves policy before the call reaches your data and prompts only on the dangerous actions.

Business Value

Properly implemented MCP gateways deliver:

Faster incident response through improved visibility
Audit-ready logging and action tracing
Reduced IT overhead via automated low-risk approvals
Alignment between security and business teams, transforming security from bottleneck to enabler
Pathway to production scale, addressing the fact that "95% of generative AI pilots fail to reach production"

AI clients call tools through the SealGate gateway, which allows or blocks each call and forwards every decision as a structured log event to a Splunk or SIEM node.

Every tool call, whether allowed or blocked, is forwarded to your SIEM as a structured, audit-ready log event.

Conclusion

MCP gateway selection is foundational to enterprise AI deployment. Organizations prioritizing security governance and observability will successfully scale AI initiatives where others stall.