Your Network Has a NOC. Where’s Your AI’s? Here’s How to Build One.

Most teams can’t support their own AI 24x7. This Cybersecurity Awareness Month, here’s how to change that before 2027 does it for you.

“In 2027, most enterprises will learn they have a network operations center for their network and nothing for their AI. The companies that build one will scale agents. The rest will spend the year explaining incidents and invoices.”
— Ron Williams, CEO, Kindo

Ask a security leader how big their IT footprint is and they can show you a report down to the asset. Ask how many AI agents are running in the company, what each one can touch, where its data goes, who owns it, whether it's working, and what it costs, and the answer is often incomplete, or a guess.

A commercial bank that has agents monitoring key processes can’t stay in compliance when their agents break at 2 a.m. and no one knows. 

That gap is why we’re here. Cybersecurity Awareness Month exists to put the basics back in front of everyone: know what you own, support what is running, be able to show what happened, and track cost against return on investment. For decades, the network operations center (NOC) has done a lot of that for enterprise networks. For AI, most organizations have no equivalent. What they’re missing is a view of their AI estate: every agent, model, tool, credential, data connection, and token the company has spent. Much of it is undocumented, and much of its cost is hard to trace back to a team, let alone a business outcome.

Why this is getting worse, fast

Several things are happening at once:

• Product design: Current agent creation and deployment platforms have been created with retail consumers in mind rather than enterprise employees. They ship with limited, if any, centralized security, compliance, support, and cost controls. That leaves every employee to build secure agents, fix them at 2 a.m., stay compliant, and navigate budget policy on their own.
• Speed: Business teams can build and deploy an agent with natural language in an afternoon. Security reviews can take weeks, so agents often ship before anyone reviews them. IT finds out the agent is supporting a critical business process only when it breaks, often late at night. Finance gets an exploding bill at the end of the month. Compliance can’t explain what happened during the audit six months later.
• Access: To get pilots working, teams gave agents root-level service accounts and standing credentials, and those shortcuts made it into production. Accountability back to the human who set up the agent is missing. No one can remember why the agent is running after the staff turns over. Agent identities are expected to outnumber human ones 10 to 1 in 2028, and standing credentials are exactly what attackers look for.
• Data: If you don’t know an agent is running, you don’t know where your data is going. Agents pull from internal systems, pass information to models and tools, and can send it beyond your walls, often with no record of what left, who let it happen, where it went, what the AI did vs. the human, or why. What starts as an inventory gap becomes a data governance problem, with real privacy and compliance consequences. The world just witnessed thousands of unmonitored and ungoverned AI lab agents attack hundreds of companies, leak data, and steal credentials. 
• Consequences: The risk is moving from a chatbot saying something wrong to an agent doing something wrong in production. The risk rises with sprawling tool connections, 24x7 autonomous agents, and too much access. When it happens, the first questions will be who was accountable, where the record is, and what needs to be fixed.
• Cost visibility: Teams are working on this, and a growing set of tools can count tokens and estimate spend. Accurate attribution and forecasting are still hard and the ability to take action is missing. Models, pricing, and usage keep changing, agents call other agents and tools, and spend spreads across teams and vendors. So “what did AI cost us this quarter, by team?” often gets an estimate rather than an answer. Ramp recently shared the numbers: the top 1% of employees is spending $7,500 a month on AI tokens, the top 10% is spending almost $700 a month and the median is spending $12. The trend is clear for 2027: AI tokens will be a surprisingly large part of the IT budget to most companies.
• Runaway spend: An agent stuck in a loop can burn six figures of tokens overnight, which is why budgets and hard stops are a security control as much as a finance one. Where is the ROI is another frequent question as Tokenmaxing teams throw AI at everything.

How to get in front of it

Banning agents won’t work. What made networks manageable and reliable enough to build the business on was seeing everything first, then controlling it, then being able to account for it, including where the data went and what it cost. AI needs the same order. A NOC doesn’t prevent every outage. It lets you know what’s running, detect when it’s broken, reach in and fix it, and then explain what happened. You can start creating your own AI Operations Center today with a short checklist.

The checklist: seven steps to start now

1. Inventory every agent. Build a register of every agent in use, including agents built outside your approved tools and the ones teams built themselves. Record what each one does, which model and tools it uses, and where it runs. This list is your AI estate; you can’t govern what you can’t list.
2. Assign an owner to each one. Every agent needs a named person who answers for it, because an agent with no owner has no one to notice when it misbehaves or to shut it down. 
3. Map what each agent can touch, and where its data goes. For each agent, document the systems it can reach, the data it can read, and where that data can be sent, including other models and outside tools. This is the step that turns an inventory into data governance.
4. Cut access down to the operator’s permissions. An agent should have its operator’s permissions and nothing more. Replace shared service accounts and standing credentials with access tied to the person accountable for the agent, so a compromised agent can’t do more than that person could. 
5. Attribute cost, and set hard stops. Use the token and cost tools that exist to tie spend to an agent, an owner, and a team, and expect early numbers to be estimates. Then set budgets with hard limits, so a loop or a mistake stops at a ceiling instead of showing up as a surprise invoice.
6. Keep a record of every action. Log what each agent was asked to do, what it did, and what it touched. When something goes wrong, “who was accountable, and where’s the record?” should have a quick answer.
7. Retire what you’re not using. Decommission agents when a project ends or an owner leaves. Orphaned agents with live access are the AI version of forgotten cloud accounts.

Where it all comes together: an AI Operations Center

Agents are built daily and models keep changing, so a checklist done once goes stale within a quarter. What keeps it current is something your other critical systems already have: a place where someone is always watching.

Enterprises run a NOC to keep the network up and a security operations center (SOC) to spot and respond to threats. An AI Operations Center does these jobs for your agents, and it’s where the checklist operates day to day:

• See: a live view of every agent, its owner, its access, its status, and its data connections, wherever the agent was built.
• Detect: alerts when an agent behaves unexpectedly, hits a spending limit, or reaches for something it shouldn’t.
• Respond: pause or shut down an agent quickly, optimize a misconfigured one, without waiting for a weeks-long review.
• Prove: a complete record of what happened, ready for an auditor, a regulator, or a board.
• Control cost, understand impact: spend attributed by team, ROI calculated, with budget limits that hold.

Better agent security, uptime, compliance, and ROI is only part of the payoff. The bigger reward is being able to say yes to more agents because you can see and control them. Keep AI transformation on track so the organization leverages the benefits of AI faster.

Putting the enterprise in control

This is the gap Kindo was built to close. Kindo is an AI agent management platform that puts the enterprise fully in control. Build agents in Kindo or other tools, manage the agents your teams built, and set the rules for how every agent uses your data, models, tools, and dollars. Kindo’s AI Operations Center is the live view inside the platform, and together they make up what we call an Enterprise AI Control Center: one place to see, support, govern, and account for every agent in your company.

The question AI-forward buyers ask is shifting from “which model?” to “what’s our control layer?”

The number of agents inside enterprises will explode in 2027. Industry analysts predict the average enterprise could have 150,000 in 2028. Get ahead of it now and you’ll meet it with relief, because you’ll already know what’s running, what it can touch, how to support it, and what it costs.