AI Control Room
See, secure, and steer AI across your business.
AI is already in your environment. The problem is not whether AI is here. The problem is whether anyone can actually see it, govern it, and scale it without creating risk the CEO ends up owning.
AI Control Room gives CIOs, CISOs, and CTOs a practical way to move from scattered pilots to managed AI operations -- visibility, policy guardrails, auditability, rollout controls, and executive reporting in one operating layer.
The Problem
AI adoption is moving faster than control.
Most companies do not have an AI strategy problem. They have a control problem. AI shows up first in productivity tools, departmental software, and isolated pilots, then spreads into customer-facing processes, internal workflows, and decision support long before governance catches up.
That creates three executive problems at once. The CIO loses architectural clarity. The CISO inherits a new attack and exposure surface. The CTO is asked to scale systems that were never designed with monitoring, approvals, or policy enforcement in mind. Meanwhile, the CEO is left asking the simplest question of all: where is AI helping the business, and what is it putting at risk?
Most teams can answer pieces of that question, but not the whole thing. Very few organizations can show, in one place, what AI is doing across the business, what controls are in place, and which use cases are safe to scale next.
It does not have to work that way.
What AI Control Room Does
One layer for visibility, guardrails, and managed rollout.
AI Control Room is the operating layer that sits between AI experimentation and enterprise-scale adoption. It helps you see where AI is being used, determine what is allowed, monitor what matters, and standardize how new AI workflows move into production. This is not generic change management, and it is not another dashboard for its own sake. It is the control structure that lets technical leadership say yes to AI without losing the plot.
We start with Microsoft-centric environments because that is where AI risk becomes real fastest: identity, permissions, data access, Copilot exposure, workflow automation, and agents all converge there. From that base, we extend controls and observability into the broader AI surface area across tools, vendors, and internal workflows.
Control Surface
Four capabilities. One executive answer.
AI Visibility
Get a unified view of where AI is in use across teams, workflows, and systems. We map sanctioned and unsanctioned AI activity, identify where AI touches sensitive data, and establish a working inventory of tools, agents, automations, and business processes influenced by AI.
Governance & Guardrails
Define what AI is allowed to do, with what data, under whose authority. We implement policy templates, approval logic, role-based controls, human-in-the-loop escalation paths, and workflow-level risk thresholds so AI usage is bounded by design instead of left to interpretation.
Monitoring & Auditability
See what AI accessed, what actions it triggered, where exceptions occurred, and when something starts drifting. We combine logging, alerting, and traceability so security teams can investigate, IT can validate, and executives can review without relying on anecdotes.
Rollout & Change Control
Move from isolated wins to controlled scale. We create standard rollout playbooks for new AI use cases, including risk classification, implementation sequencing, exception handling, user communication, and executive review.
Who This Is For
Technical leaders carrying both the upside and the downside.
AI Control Room is built for organizations where technical leadership is expected to enable AI adoption without opening governance gaps.
What You Get
A practical operating model for AI.
By the end of the engagement, leadership gets a working view of the company's AI surface area, an explicit control model for high-risk and medium-risk use cases, a prioritized set of remediation actions, and a defined path for approving and scaling new AI workflows. Teams get clarity on what is allowed, when to escalate, and how to introduce new AI capabilities without creating invisible risk.
The CEO gets something just as important: reporting that ties AI activity back to business relevance. Instead of hearing that “teams are using AI,” leadership can review where AI is improving speed, where it is reducing manual effort, where exceptions are occurring, and where the company should or should not expand next.
Packages
Assess. Implement. Monitor.
Fixed-fee entry points for assessment and implementation, plus a recurring managed layer priced by machines covered.
Control Room Assessment
You know AI is entering the business, but you do not yet have a clear view of the surface area, control gaps, or rollout risk.
Control Room Implementation
You need to stand up the control layer: visibility, guardrails, logging, approval logic, and executive reporting.
Control Room SaaS
This is the ongoing layer that keeps AI visible, governed, and monitored as usage expands.
Pricing
Simple pricing based on machines covered.
AI Control Room is priced like a SaaS product, with coverage tied to the number of business machines enrolled in the control layer. Implementation and major remediation projects are scoped separately.
Foundation
100-249 machines
$2,500 monthly minimum
For organizations formalizing their first AI governance and monitoring layer.
Growth
250-999 machines
$4,000 monthly minimum
For organizations rolling out AI across multiple teams and workflows.
Everything in Foundation, plus:
Enterprise
1,000+ machines
$8,000 monthly minimum
For organizations treating AI as a governed enterprise capability.
Everything in Growth, plus:
How It Works
From scattered AI activity to managed AI operations.
Assess
Weeks 1-3We identify where AI is being used, where it can reach, and where control gaps already exist. That includes Microsoft-specific posture, sanctioned and unsanctioned tools, workflow exposure, and the executive questions that need clear answers first.
Implement
Weeks 4-8We stand up the control model: guardrails, visibility, logging, approvals, and reporting. This is where policy gets translated into operational reality and where the first approved AI workflows move into a properly governed environment.
Monitor
OngoingWe review usage, tune controls, flag anomalies, and support the expansion of AI into new workflows without losing discipline. An ongoing AI operations cadence designed for CIO, CISO, and CTO stakeholders -- not just point-in-time hardening.
Microsoft Aligned
Built for the Microsoft stack. Not bolted on.
Most AI governance discussions get abstract fast. We keep this grounded in the systems your business already runs on.
Identity through Entra ID
All access controls and governance policies work through your existing identity provider. No third-party identity layer required.
Compliance through Purview
Data classification, audit logging, DLP, and sensitivity labels configured through the compliance stack you already operate.
Enforcement through Conditional Access
Policy enforcement for AI services, agents, and workflows using the same Conditional Access framework that governs the rest of your environment.
No new control plane
CIOs and CISOs avoid creating another disconnected governance layer. CTOs get lower implementation friction. The CEO gets improved control without a new bureaucracy.
FAQ
Common questions.
How is this different from AI Security & Governance?
AI Security & Governance is the foundation. AI Control Room expands that work into an ongoing operating layer for visibility, policy enforcement, rollout control, and executive reporting. Security remains a core pillar, but the offer is broader: it helps you run AI safely across the business, not just harden one layer of the stack.
Why price this per machine covered?
Machine count is a clean proxy for operational footprint. It gives IT and finance a practical budgeting mechanism, and it lets the company scale coverage in a way that is easier to forecast than open-ended consulting retainers.
Does this only work for Microsoft environments?
No, but Microsoft environments are the right starting point because that is where identity, data access, Copilot exposure, and workflow controls are most tightly connected. Starting there keeps the offering credible and concrete. We extend controls into the broader AI surface area from that base.
Is this a software product or a managed service?
It is best understood as a SaaS-style managed platform. The recurring fee covers the ongoing control layer and oversight motion, while assessment, implementation, and major remediation remain scoped services.
What does the CEO actually get from this?
The CEO gets a way to govern AI as a business capability rather than a collection of experiments. That means visibility into where AI is helping, where it is creating risk, and where the company should scale next -- tied to business relevance, not technical abstractions.
AI is already moving. Put controls around it.
Book a call and we will show you what an AI control layer looks like for your environment -- where the risk is, where the opportunity is, and what it takes to scale AI without losing executive control.