- Data
- CMS
- AWS
- Web app
Enterprise MCP Gateway for secure operations
A large enterprise company needed a secure way to connect AI assistants, agents, and developer tools with internal systems while maintaining full control over data access, governance, and compliance requirements. We designed an MCP-based integration architecture that enables controlled AI access to SDLC workflows, enterprise knowledge, and operational systems through a centralized security layer.
Industry
FinTech
Client
Under NDA
Region
USA
Main challenges
Business Challenge: AI adoption lacked centralized governance and control
The company wanted to expand AI usage across development and operational workflows without introducing security, compliance, or data management risks.
- AI access policies were difficult to enforce
- Internal knowledge remained fragmented across systems
- Teams lacked visibility into AI-driven activities
Technical Challenge: Internal systems were not AI-ready by design
Enterprise tools contained valuable context but lacked a standardized mechanism for secure AI access, authorization, and orchestration.
- AI tools required separate integration approaches
- Permissions and access control were difficult to maintain
- Sensitive data required advanced filtering and protection
Delivery Challenge: Rollout required sequencing to avoid operational risk
New AI capabilities had to be introduced across active teams gradually, without disrupting ongoing development workflows.
- Any sequencing error risked breaking active development processes
- Teams had different levels of readiness for AI tooling
- Each system required independent validation before broader rollout
What we did
We started by mapping the organization's AI exposure across systems — identifying where access was uncontrolled, where sensitive data flowed without filtering, and where knowledge remained siloed. This audit shaped the gateway architecture: a layered approach where security, authorization, and observability were built in from the start rather than added later.
From there, we prioritized integrations by risk and business value, deploying read-only access first and introducing write operations only after approval workflows were validated. Each system was onboarded independently, tested against security policies, and connected to a shared knowledge graph that gave AI assistants reliable context without bypassing existing permission structures.
100%
AI interactions governed through centralized access policies
80%
Reduction in time spent searching across engineering systems
20+
Enterprise systems connected through approved MCP tools
1
Unified layer for AI access, authorization, and auditing
Enterprise MCP gateway
A centralized control layer for secure AI access across enterprise systems.
- Centralized tool registry and governance controls
- Role-based authorization across projects and teams
- Secure AI access to approved internal systems
- Complete audit trail for AI interactions
- Human approval for sensitive operations
AI security framework
Security controls designed specifically for enterprise AI adoption.
- Data classification before AI context delivery
- DLP filtering for sensitive information
- Secrets and credential redaction mechanisms
- Source-system permissions remain preserved
- Full security event monitoring and reporting
Knowledge graph infrastructure
A connected enterprise knowledge layer supporting AI reasoning and impact analysis.
- Unified relationship mapping across systems
- Traceability between requirements and implementation
- Ownership discovery across teams and repositories
- Context retrieval for engineering workflows
- Semantic and graph-based information access
AI agents for SDLC automation
Governed agents supporting repetitive engineering activities.
- Automated investigation of development issues
- Test generation and documentation assistance
- Vulnerability triage and risk identification
- CI/CD failure analysis and recommendations
- Controlled escalation for complex scenarios
GitHub Copilot enablement
Governed rollout of GitHub Copilot across development teams with shared practices and data protection controls.
- Shared skills library for BA, QA, development, and code review
- Content exclusions configured for sensitive repositories and folders
- Data obfuscation and anonymization before code enters Copilot context
- Pre-commit and CI checks for secrets and sensitive data patterns
- Team-level playbook defining approved and prohibited Copilot use cases
Key results and business value
Fragmented AI access replaced by centralized governance
Disconnected systems transformed into a unified AI-accessible ecosystem
Uncontrolled AI experimentation evolved into enterprise-scale adoption
Manual knowledge discovery replaced by contextual retrieval
Features Delivered
Key capabilities of the MCP Gateway:
- Centralized MCP gateway for AI tool access
- Role-based authorization and policy enforcement
- Knowledge graph for contextual data retrieval
- Enterprise audit logging and monitoring
- Governed AI agents for SDLC workflows
- Human approval controls for sensitive actions
MCP SDK
OAuth 2.1
OpenTelemetry
Grafana
OPA
Vault
Neo4j
GitHub Copilot
Technical Highlights
Enterprise MCP gateway
Centralized AI access layer managing authentication, authorization, governance policies, and tool orchestration
Policy-based authorization
Role, project, and repository permissions determine which types of tools and data AI can access
Data protection controls
DLP filtering, secret redaction, and classification policies protect sensitive enterprise information
Knowledge graph architecture
Entity relationships across Jira, GitLab, and Confluence are stored in Neo4j graph traversal for impact analysis
Human-in-the-loop agents
AI agents operate within approval workflows to reduce risk while increasing system productivity
Full audit infrastructure
Comprehensive logging and monitoring provide traceability, compliance reporting, and security visibility
Client Feedback
"The practical value is that we no longer have to solve the same security and access problems for every new AI tool. Once the gateway was in place, connecting assistants, agents, and internal systems became a controlled process instead of a custom integration effort. It gave us a foundation for scaling AI adoption without losing visibility into how company data is being used."
Head of Technology
Client
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