Let AI work with your internal systems without breaking your security model
We design and build Model Context Protocol (MCP) integrations that give AI assistants controlled access to your internal systems—so they can read, write, and orchestrate data under strict governance, observability, and least-privilege access.
Works with your real systems
CRMs, ERPs, ticketing, HR, finance, custom line-of-business apps—exposed via tools, not open database access.
Least-privilege by design
Each tool exposes a narrow, audited operation with clear inputs, outputs, and policy checks.
Full observability
Every AI-initiated action is traceable: who requested it, which tool was used, and what data was touched.
AI wants access to your systems. Security wants clear boundaries. Both can be right.
Enterprises want AI to do real work: look up tickets, change records, triage incidents. But giving models direct access to production systems without a security model is a non-starter.
What most teams try (and why it worries security)
- Direct DB connections from AI playgrounds.
- Unbounded “admin” API keys wired into prompts.
- Agents that can call everything, without audit logs or scopes.
- Shadow scripts and bots bypassing official change management.
What we do instead with MCP
- Define narrow, well-described MCP tools that call your existing APIs.
- Enforce least-privilege scopes and user-based access checks at the tool level.
- Route all AI access through an auditable, observable MCP server.
- Align with your existing identity provider, logging, and compliance controls.
A security-first approach to AI access, built around MCP
We treat AI as a new class of client to your systems—with all the same expectations you'd have for a human admin or a mission-critical service, not a toy script.
Identity & access modeling
We map which internal personas (and AI agents acting on their behalf) can access which systems and operations, then encode those rules into MCP tools.
Least-privilege tool design
Instead of exposing raw tables or unfiltered APIs, we design narrow tools like “get_employee_summary” or “open_ticket_with_template” with strict parameter schemas.
Audit, logging & approvals
Sensitive actions can require human approval, dual control, or routing through existing workflows (e.g. ITSM, change management) instead of bypassing them.
Reference architecture: AI ↔ MCP ↔ your internal systems
Every environment is different, but the core pattern stays the same: AI agents call tools on an MCP server that enforces your security decisions and calls your systems on their terms.
Control plane
Central policy, tool definitions, rate limits, and routing rules live in the MCP server. AI never sees connection strings or raw credentials.
Data plane
Requests are translated into calls to your existing internal APIs and services, respecting your current security and network boundaries.
Observability
Logs for every tool call and response integrate with your SIEM / logging stack for monitoring, alerting, and incident response.
Internal use cases where secure AI access actually pays off
These are not speculative. These are real patterns we see in IT, operations, HR, finance, and support teams.
IT service desk copilot
AI copilots that can look up assets, open tickets with correct templates, check incident history, and propose next actions—without direct DB access to ITSM.
HR & employee self-service
Assistants that answer “How much PTO do I have left?” or “What's our parental leave policy?” via tools that query HRIS and document systems with strict access checks.
Finance & approvals
AI that drafts spend summaries, looks up vendor data, and prepares approval tickets—while actual approval clicks stay with humans.
What's the incident history for VPN access issues in the last 30 days?
MCP tool plan
1) Call incident_query with category="VPN" and date_range=30d
2) Aggregate by root cause
3) Return summary + top recurring issues
Tools used: incident_query, incident_aggregate
There were 17 VPN-related incidents in the last 30 days. 10 were due to outdated clients, 5 due to expired certificates, and 2 due to regional outages. I've attached a CSV with full details.
Built to fit into your governance, not fight it
We don't promise certifications we don't own. Instead, we design MCP integrations that align with your existing GDPR, SOC 2, ISO 27001, or internal policy frameworks.
Data handling clarity
We document what data each tool can access, where it flows, and retention expectations so your security and legal teams can review with confidence.
Separation of duties
Sensitive operations can be split between what AI can do automatically vs. what always requires human approval, mapped to your existing roles.
Review & sign-off
We present architectures, data flows, and tool definitions in a format security, compliance, and platform teams can review and sign off on.
Strategic, high-skill work—priced accordingly
This is not a “connect a chatbot” project. It touches core systems, identity, and risk. We price based on complexity, sensitivity, and expected business value.
Secure internal AI access
Expose your internal systems to AI (securely)
Investment: premium, by proposal
Typically for mid-size and enterprise environments with meaningful operational or support volume.
Typically includes
- • System & use case discovery workshops
- • MCP tool & access model design
- • MCP server implementation & deployment
- • Logging, monitoring & audit integration
Optional extensions
- • Multi-environment & multi-region setups
- • Ongoing AI ops & incident review cadence
- • Additional tools & systems onboarding
- • Deeper policy automation & approvals
If you're being asked to “let AI talk to our systems” and your first instinct is to say “not unless we do it properly”, this offering is designed for you.
Request a secure AI access planReady to let AI help—without giving up control?
Bring your security concerns, your internal systems, and your highest-value use cases. We'll design an MCP-based integration that lets AI do real work while respecting your risk and governance profile.