AI Governance Gateway for Regulated Data
100%
SEC 17a-4 Audit Readiness
The Challenge
A high-growth financial services firm faced a critical security dilemma: third-party AI automation vendors were aggressively seeking direct integration with the firm's CRM. While these tools promised to revolutionize sales workflows, they created unacceptable risks for a regulated institution:
Uncontrolled PII Exposure
Direct API access would allow external AI models to ingest sensitive client contact info and private financial notes without oversight.
Regulatory Audit Gaps
Standard third-party integrations lacked the "paper trail" required by financial regulators regarding who accessed what data and for what purpose.
Data Sovereignty Risks
The firm had no way to intercept, redact, or block specific data queries in real-time before they reached external cloud environments.
How We Solved It
Fern Strategy was retained for a 1.5-month engagement to build a defensive "AI Gateway." We implemented the gateway as an MCP server, using Anthropic's Model Context Protocol—an open standard designed for universal AI-to-system communication. Any AI agent must now communicate through this standardized, auditable channel rather than raw API access or, worse, direct database access.
By extending their existing Django-based system with this gateway, we moved the firm from "direct access" to "governed access." A single point of truth where every data request was inspected, filtered, and logged before being fulfilled.
The approach followed a security-first posture: eliminate shadow AI by gaining full visibility into all AI-related assets, enforce least privilege by scoping credentials per function, and layer guardrails against PII leakage. Critically, we prioritized interoperability from day one. Standardizing on MCP—an open protocol with broad industry adoption—means any MCP-compatible AI agent can integrate without custom API work. The firm isn't locked into a single vendor's ecosystem.
The gateway abstracts the underlying AI vendor's integration model entirely. Whether the firm adopts Claude, GPT, Gemini, or specialized enterprise AI tools, all data access routes through the same governed channel. New AI capabilities can be onboarded in hours rather than weeks, without re-engineering the security layer.
Engagement Timeline
Discovery
Week 1
Security Audit & Requirements
Development
Weeks 2-4
MCP Gateway Build
Deployment
Weeks 5-6
Integration & Migration
What We Built
The solution was deployed as a resilient, enterprise-grade architecture on AWS, utilizing Kubernetes for orchestration and Celery for high-volume background processing.
MCP Security Gateway
A custom-built server implementing the Model Context Protocol—an open standard that enables any compliant AI agent to connect without vendor-specific integration work. The gateway abstracts the CRM's data schema, ensuring third-party tools only interact with "sanitized" versions of client records while maintaining universal compatibility.
Universal Interoperability Layer
Built on open standards rather than proprietary APIs, enabling seamless integration with any AI platform—current or future. The architecture supports Claude, GPT, Gemini, and specialized enterprise tools through a single, consistent interface. New AI vendors can be onboarded without modifying the core security infrastructure.
Extended Audit Logging
We extended the firm's existing immutable audit log to capture AI interactions: the service principal or user identity, the data categories accessed, and the timestamp. All entries flow into their existing compliance infrastructure.
Asynchronous Redaction Engine
A redaction layer built on open-source PII detection that scans outbound data, masking sensitive information before it reaches external AI agents.
Kubernetes-Managed Infrastructure
A scalable container environment on AWS with horizontal pod autoscaling, allowing the gateway to elastically handle traffic spikes without impacting CRM performance.
Least Privilege Enforcement
Credentials follow two models: for user-triggered workflows, the agent inherits the employee's session and RBAC profile. For automated pipelines (scheduled scans, background enrichment), dedicated service accounts are scoped to the minimum data access required. Both paths are logged to the firm's existing audit infrastructure.
Gateway Architecture
Secure bridge between CRM and AI automation tools
Internal Environment
AI Agent
Orchestration & Logic
Secure Gateway
MCP
MCP Client
Interface Layer
MCP Server
Enterprise Data
Internal Environment
AI Agent
Secure Gateway
MCP Client
MCP Server
Enterprise Data
Technology Stack
Measurable Results
0
Direct Vendor Connections
100%
SEC 17a-4 Audit Readiness
Any
AI Platform Compatible
Eliminated Direct Vendor Risk
All third-party AI automations now route through the MCP gateway. Zero direct API or database connections remain. Every data request is inspected, filtered, and logged before fulfillment.
SEC 17a-4 Audit Readiness
Established a permanent, searchable audit trail of all AI-to-CRM data exchanges with immutable logging. Meets SEC Rule 17a-4 requirements for electronic record retention and is ready for regulatory review.
Automated Data Protection
Implemented automated PII redaction that prevents sensitive client data from ever leaving the firm's controlled perimeter.
True Interoperability
By building on the open Model Context Protocol standard, the firm achieved genuine vendor independence. Any MCP-compatible AI agent—whether from Anthropic, OpenAI, Google, or emerging startups—integrates through the same governed channel. The firm can evaluate new AI capabilities, switch providers, or run multiple platforms simultaneously without re-engineering their security posture.
Future-Proof Architecture
The open-standards approach means the gateway will remain compatible as the AI landscape evolves. New AI platforms adopting MCP can be onboarded in hours, not weeks. The firm's investment in governed AI access pays dividends with every new tool they evaluate.
Ready to secure your AI integrations?
Let's discuss how Fern Strategy can help you implement governed AI access without sacrificing automation benefits.