Delivered from our Chennai and Bengaluru studios — to clients across India and overseas.
// The problem
Your security team said no, and they were right to.
The business case for connecting an AI assistant to real systems is obvious the moment someone tries it. So is the objection: what exactly can it access, who can see what, what is logged, and what happens if it is manipulated through a document it reads.
Those are good questions. The usual answers — an API key in a config file, or a copy of the data somewhere else — do not survive them, which is why so many promising internal AI projects stall at review.
Does any of this sound familiar?
Security review blocked the projectNo defensible answer to what the assistant could access.
Broad credentials as the only optionAn API key granting far more than any assistant should hold.
No record of AI accessAn audit question with no answer.
Every tool needing its own integrationRepeated work with inconsistent security each time.
What it costs to leave this alone
Either the assistant stays generic and largely unused, or somebody eventually connects it insecurely because the value was obvious and the governance was not. The second outcome is considerably more expensive than doing it properly.
// How we fix it
Our approach to Claude Connector Development
We build connectors that answer the security questions before they are asked. Narrow validated operations rather than broad access. Permission mapping so the assistant sees exactly what the requesting person could see. Complete audit logging. Approval gates on anything irreversible.
Critically, we design assuming the model will eventually be manipulated — so that even a fully compromised assistant cannot exfiltrate, delete or spend. That containment, rather than filtering, is what makes the position defensible.
An API key grants everything that key can do, with no per-user scoping and no record of what was accessed. A connector exposes specific validated operations, applies the requesting user's permissions, logs every call, and can be revoked instantly.
Anything with an API or database — CRMs, ERPs, helpdesks, e-commerce platforms, internal databases. Legacy systems without clean APIs need an adapter layer built first, which is usually the larger part of that project.
Yes, with confirmation on anything consequential. Reading is automatic; creating tickets, sending messages or updating records pauses for human approval, calibrated to your risk tolerance.
What it can access, what happens if compromised, how it is logged, whether it can act unattended, and how to turn it off. We build so those answers exist before the review rather than during it.
// Read before you commit
Guides that answer the next question
Honest, detailed writing on costs, trade-offs and how to choose — including when not to hire us.
Scoped access, permission mapping, audit trails and prompt-injection defence — the architecture that makes AI access to internal systems defensible to your security team.
The specific attack classes MCP servers face, and the design decisions — least-privilege tools, input validation, untrusted-content handling — that defend against them.