Governed AI Core
Zero Data Leakage
One key for everything
The pilot runs on a single API key or service account with broad access. No risk team will sign off on that in production.
Enterprise AI
Most AI pilots die at the security review. We build the ones that pass.
Everyone has an AI proof-of-concept. Almost no one has AI in production doing real work, because the moment it needs access to real systems and data, security says no, and they are usually right to. Open-ended access with no audit trail and no clear owner is not a product. It is a liability.
How it works
Governed enterprise AI: how it works in production.
Autonomous agents never access backend systems directly. Every action is mediated, scoped, human-approved when sensitive, and immutably audited.
Autonomous Agent
AI Agent
Autonomous LLM worker operating under task-specific objectives. Generates tool call requests without direct system credentials.
const req = {
call: "crm:sync_account",
id: "cust_8921"
};
Zero Static Keys Stored
Enforcement Core
Governed Gateway
v2.4 Zero-TrustCentral policy firewall mediating every agent interaction. Verifies identity, scopes privileges, intercepts injection attacks, and routes approved payloads.
Least-Privilege Scoping
Ephemeral dynamic token generation
Prompt Injection & DLP Guard
Real-time boundary & PII sanitization
NIST CSF & CCPA/CPRA Compliance Engine
Strict local residency & policy enforcement
Security & Governance Controls
Scoped Access
Fine-grained, short-lived tokens restricted to the exact record and action requested.
Just-in-Time RBACHuman Approval
Human-in-the-LoopSensitive or financial transactions are paused in real-time until authorized by designated personnel.
Threshold: USD 50k+
Audit Log
WORM tamper-evident telemetry tracking prompt, model output, identity, and execution timestamps.
Immutable Telemetry
Enterprise Production Systems
CRM
Salesforce / Dynamics 365 / Hub
Scoped Read/Write
ERP
SAP S/4HANA / Oracle Cloud ERP
Approval Gated
Internal APIs
Private Microservices & Data Mesh
mTLS 1.3 · VPC Peered
How it fits together
From a single agent to a governed, multi-cloud AI platform.
The real blocker
The model is rarely the problem. Access is.
Most AI pilots work fine in a demo. They stall when someone asks what the AI can reach, who approved it, and how you would know if it did something wrong.
No audit trail
If an agent changes a record or sends a message, nobody can say who triggered it, what it saw or why it acted.
Sensitive data exposure
AI surfaces whatever it can reach. If permissions are messy, it will show people files and data they should never see.
No clear owner
IT built it, the business uses it and security wasn't involved. When it breaks, nobody owns the fix.
Business outcomes
From stuck pilot to governed production.
We design AI that earns its way into production. Agents get least-privilege access to specific tools, every action is logged, and higher-risk actions sit behind human approval. Copilot and Azure OpenAI integrations connect to your data under real identity controls, not a blanket API key.
Adopt AI safely
Move from pilot to production with a security model your risk team will actually approve. Least-privilege access, audit logging and human approval on sensitive actions are built in from day one, not added later as an afterthought.
Control tool access
Every tool an agent can touch gets its own scoped credential and approval rule. If it only needs to read a calendar, it can't send email or delete a record. Access expands as the agent earns trust.
Connect AI to business workflows
Wire AI into the systems you already run, such as CRM platforms, ticketing tools and internal APIs, through governed connectors so it does real work instead of sitting in a demo. Nothing is open-ended by default.
Monitor AI operations
Every agent action is logged with who, what and when. When something breaks at 2am, you can trace exactly what happened and roll it back instead of guessing.
Aligned with US AI and data governance
Clear ownership, audit trails and controlled access to sensitive data, designed to support alignment with the NIST AI Risk Management Framework, the CCPA/CPRA and NIST cybersecurity controls.
Keep AI costs predictable
Usage budgets per team and per agent, cost monitoring and the right model for each task. AI spend grows with value, not by surprise.
Security by default
The controls your risk team will ask about, already in place.
Every AI deployment we deliver ships with these controls from day one, so the security review becomes a walkthrough instead of a wall.
Least-privilege tool access
Each tool gets its own scoped credential. Read-only stays read-only.
Human-in-the-loop approvals
Higher-risk actions such as sending, deleting or paying wait for a person to approve.
Full audit logging
Every prompt, tool call and action is recorded with who, what and when.
Prompt-injection defense
Inputs and tool outputs are treated as untrusted and tested against manipulation.
Data boundaries
Sensitivity labels, DLP and permission clean-up so AI only sees what the user is allowed to see.
Private networking
Model endpoints stay inside your network boundary, not on the open internet.
Identity-based access
AI acts under real user and workload identities, not shared keys.
Monitoring and rollback
Alerts on unusual behavior, and a clear path to trace and undo actions.
Related services
Capabilities connected to this solution.
These services can be delivered independently or combined into a larger roadmap.
Agentic AI
Enterprise agentic AI development: Model Context Protocol (MCP), tool-call authorization, human-in-the-loop gates, and CCPA/CPRA-compliant execution.
Microsoft AI Foundry
Build, evaluate and deploy enterprise AI on Microsoft AI Foundry with the governance, evaluation and monitoring production requires.
Azure OpenAI
Deploy enterprise Azure OpenAI with Private Link, Entra ID authentication, content filtering, and hybrid RAG architecture aligned with US regulations.
Microsoft Copilot
Copilot readiness done right: clean up permissions first, so Copilot surfaces what people should see and nothing they shouldn't.
MCP Development
Custom MCP servers that connect AI agents to your APIs and data through governed, auditable, least-privilege interfaces.
AI Automation
Automate real business workflows with AI: safely scoped, monitored, and with humans in the loop where it matters.
Industry fit
Built for industries where AI mistakes are expensive.
Each industry brings different data, regulation and risk. We map AI use cases to the controls each one requires.
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Government & Public Sector
Citizen-service assistants and internal knowledge agents with strict data boundaries, role-based access and full accountability for every action.
Financial Services
Document review, KYC support and analyst copilots with human approval on decisions, complete audit trails and controls built for regulatory scrutiny.
Healthcare
Administrative and operational AI that keeps patient data protected, access tightly scoped and every interaction traceable.
Energy & Industrial
Maintenance, operations and field-support agents that connect to operational systems without opening uncontrolled access to critical infrastructure.
SaaS & Technology
AI features and internal agents shipped with evaluation, monitoring and least-privilege integrations, so customers can trust what the product does.
Professional Services
Microsoft Copilot readiness, secure knowledge search and document automation that respects client confidentiality and matter-level permissions.
Delivery path
From first use case to governed production.
Each phase has a clear output, so you know what you get before moving to the next.
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Prioritize AI use cases by value and risk, and assess data readiness, existing permissions and the systems AI will need to reach.
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Design the agent-to-tool access model, approval flows, data boundaries, identity integration and monitoring before any build begins.
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Build governed connectors, MCP servers and integrations using reviewed, repeatable engineering and infrastructure as code.
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Test with real scenarios, output evaluations, prompt-injection and red-team testing, and a security review before production sign-off.
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Hand over audit dashboards, cost tracking, runbooks and escalation paths, with optional managed operations after launch.
Enterprise delivery model
5 Phases
Milestone-Gated Delivery Model
Every phase ends with a documented deliverable and a go/no-go decision, so nothing reaches production without sign-off from your business, IT and security owners.
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Documented Deliverable & Formal Go/No-Go Decision Gate
No code or infrastructure moves to production without passing automated compliance checks and client review.
Zero Vendor Lock-In
Delivered into client Git & cloud accounts
Infrastructure-as-Code
Terraform / Bicep repeatable blueprints
Operational Runbooks
Knowledge transfer & team enablement
Ways to start
Start small, prove value, then scale.
AI Readiness Assessment
2-3 weeksIdentify high-value use cases, assess data and permission risks, and leave with a prioritized roadmap and a target architecture.
Best for: teams starting out or with scattered pilotsPilot to Production
8-12 weeksTake one use case from pilot to governed production, with access controls, approvals, audit logging and monitoring in place.
Best for: a pilot that is stuck in security reviewManaged AI Operations
OngoingMonitoring, cost control, evaluation, access reviews and continuous improvement for AI systems already running in production.
Best for: teams scaling AI across departments
Technology
Platforms we build on.
Azure OpenAI
Microsoft AI Foundry
Microsoft 365 Copilot
Copilot Studio
Microsoft Entra ID
Microsoft Purview
Model Context Protocol (MCP)
Google Cloud
BigQuery
Power Automate
Related proof
Representative delivery pattern.
Case studies are shared where details can be published without exposing confidential client information.
AI agents needed real tools. The security team needed to sleep at night.
AI agents got only the tools they need, through one gateway, with every call logged.
Read the story
FAQ
Questions we hear from risk and IT teams.
Short answers to common planning questions for AI Transformation.
Is our data used to train the AI models?
No. With enterprise services such as Azure OpenAI and Microsoft 365 Copilot, your prompts and data are not used to train the underlying foundation models. We also configure access so AI only works with data the user is already allowed to see.
Can AI run with data hosted in the United States?
We design for US-region hosting where the required services are available, and we document exactly where data is stored and processed so your compliance team can review it.
How do you stop an agent from doing something it shouldn't?
Agents only get scoped access to specific tools, higher-risk actions require human approval, and every action is logged. If an agent only needs to read, it cannot write.
Do we need Microsoft 365 E5 for Copilot?
No. Microsoft 365 Copilot is available as an add-on to most business and enterprise plans. E5 adds stronger data protection and governance tooling, which we recommend reviewing as part of Copilot readiness.
How long until our first use case is in production?
It depends on scope and readiness, but a single well-defined use case typically moves from pilot to governed production in 8-12 weeks.
Will our team be able to run it after handover?
Yes. Delivery includes documentation, runbooks, audit dashboards and training. We can also provide managed operations if you prefer ongoing support.
Free first call
Book a Consultation
Got an AI pilot stuck in security review?
Tell us what you're trying to build. We'll show you how to get it to production with controls your risk team will approve.