“AIOps” has been around for a decade. It describes AI that makes sense of the flood of events coming out of your monitoring tools. A newer kind of AI now does something else: it reads your live systems, investigates the root cause and drafts the change.
Both get sold as “AI for IT operations”, and they are easy to mix up. They do different jobs and carry very different risks. The second needs zero-trust AI operations, an open specification: zero trust applied to the AI itself.
What is AIOps?
AIOps stands for artificial intelligence for IT operations. Wikipedia describes it as “the use of artificial intelligence, machine learning, and big data analytics to automate and enhance data center management”, and notes that Gartner first defined it in 2016 to automate “processes such as event correlation, anomaly detection, and causality determination”.
In practice an AIOps platform takes in events, metrics and logs from many monitoring tools. It removes duplicates, groups related alerts into one incident, flags unusual patterns and suggests a probable cause. The result is fewer, better-grouped alerts for your on-call engineer. Where an AIOps platform acts, it usually runs automation that people set up in advance.
What is zero-trust AI operations?
Zero-trust AI operations is zero trust applied to the AI that runs your IT operations, and it is an open specification. The AI reads the current state of your systems through each vendor’s own API, investigates what is going on and why, and drafts the change. It is treated as an untrusted actor. It never holds a credential, the code it writes runs without network access, and every change it asks for waits for a person to approve it. Each decision is recorded.
The difference in one line: AIOps tells you something is wrong. The second kind of AI helps you find out why and change it, under controls built for an actor you do not fully trust.
How do they compare?
| AIOps | Zero-trust AI operations | |
|---|---|---|
| Main input | Events, metrics and logs from monitoring tools | Live state read from the systems themselves, plus alerts |
| Main output | Grouped alerts, anomalies, a probable cause | An investigation with its evidence, and proposed changes |
| Changes to production | Usually none, or automation people set up in advance | Proposes changes; a person approves each one |
| What goes wrong | Missed or wrongly grouped alerts, noise | An AI misled into a harmful change, or a leaked credential |
| The control that matters | Tuning and feedback on correlation | No credentials for the AI, approval enforced on the server, a record of every decision |
| Best fit | High alert volume across many monitoring tools | Teams whose time goes on investigating and changing mixed infrastructure |
Why does AI that changes systems need zero trust?
The risk follows the reach. A correlation engine reads telemetry. Its worst day is a missed or misgrouped alert. That hurts, but the AI itself changed nothing. An AI that can change systems has a worse worst day: an outage it caused, or a credential it leaked to whoever planted instructions in a log line.
The security standards say the same. The OWASP Top 10 for LLM applications names “Excessive Agency” as a risk in its own right, and notes that its impact can span “the confidentiality, integrity and availability spectrum” depending on which systems the AI can reach. NIST SP 800-207 warns that an attacker may “induce or coerce” a software agent “to perform some task that the attacker is not privileged to perform”.
So the controls have to grow with the reach. For an AI that only reads monitoring data, tuning is enough. For an AI that can propose changes to production, you need credentials it cannot touch, changes it cannot approve, and a record of every decision. We walk through that design in letting an AI near production without trusting it.
Which one do you need?
Start from where your time goes.
- Alert noise is the problem. You get more alerts than anyone can read, from many monitoring tools, and the real incident hides among duplicates. An AIOps platform built for event correlation is the right first buy.
- The work after the alert is the problem. Each incident means opening console after console, working out what changed, writing the change and finding the one person with the admin account to apply it. That is the job an investigating AI does, and it needs the zero-trust controls above.
- Both. Plenty of teams have both problems. The two fit together: a correlated alert is a good starting point for an investigation.
If you are evaluating tools in the second group, the credentials question is the first one to ask. See should AI agents hold production credentials?
How GAEZLA does it
GAEZLA is built for the second job. The AI reads your systems through each vendor’s own API, inside the scope of the operator who asked, keeps the evidence in a case and drafts the change. It never holds a credential, the code it writes runs with no network, and every change it asks for is drafted as a proposal that waits for an operator and is fingerprinted at approval. Each decision records who approved it, over which channel, and what ran.
GAEZLA also handles part of the first job. On the Complete plan, a machine-learning classifier routes alerts that arrive from any system over a webhook, records its outcome, confidence and reasons for each one, and sends the ones it is not sure about to an operator for review. If you need correlation across many monitoring tools, pair GAEZLA with an AIOps platform.