What is an AI SOC?

What a security operations centre does

A security operations centre, or SOC, is the team and the tools that watch a company’s systems for signs of attack. Security tools such as a SIEM, which gathers logs from across the company, or EDR, which watches activity on each computer, raise an alert when something matches a pattern worth checking. It might be a sign-in from an unusual country, a burst of commands on a server, or a program starting another program it normally never starts.

An alert is a question, not an answer. Someone has to find out whether it matters: which machine raised it, who uses that machine, what else happened around the same time, and whether anything it connected to is known to be bad. That checking is the daily work of a SOC, and it is where most of the hours go.

Once a person decides an alert is real, the work moves on to understanding how far the activity has got and deciding what to do about it. Those later steps depend on the first one being done well and done quickly.

What changes when AI takes the first look

An AI SOC gives that first look to an AI agent. When a new alert arrives, the agent works it the way an analyst would. It asks itself questions, queries the data to answer each one, and keeps every record it reads. It ends with a verdict, such as real attack, suspicious or harmless, together with how sure it is and the reasons.

The difference from older automation is that the steps are not fixed in advance. A scripted playbook runs the same lookups for each alert of a given type. An agent decides what to check next from what it has just found. If a process looks odd, it can look at the process that started it. If an address looks unfamiliar, it can check threat intelligence and look for other machines that talked to it.

The result reaches a person as a finished first read rather than a raw alert. The analyst starts from a conclusion and its evidence, and spends their time checking and deciding instead of gathering. Done well, the agent’s part of the work looks like this:

• Collects new alerts as they arrive, day and night.
• Puts likely real attacks at the front of the queue.
• Investigates each alert with questions and data lookups.
• Gives a verdict, a confidence and the evidence behind it.
• Joins related alerts into one picture of an attack.

What the AI leaves to people

A good AI SOC is clear about where its job ends. Reading data and forming a view is low risk: if the agent is wrong, a person catches it at review and nothing in the company has changed. Acting on systems is different. Shutting a server down, disabling an account or cutting a network connection can interrupt the business, and the person who owns that decision should be the one to make it.

For that reason many teams want the AI to recommend and people to act. The agent can say which machine looks compromised, which account was used and what it would do next, but the step itself is taken by someone with the authority and the context to take it.

Nor does the AI do away with the need for people who know the company. It does not know that a server is due to be retired next week, or that the finance team runs an unusual job at the end of every quarter, unless someone tells it. The better systems are built to be told.

How people stay in charge

Staying in charge is a matter of design, not good intentions. Three things make the difference. Every verdict should be reviewable, with the evidence shown, so a person can see what was checked and agree or disagree. A person’s correction should be kept and used, so the same mistake does not come back next week. And every approval should be recorded, with who made it and when.

Confidence matters here too. A verdict that says harmless at low confidence should be treated differently from one that says harmless at high confidence. Showing the number lets the team decide how much checking each verdict needs, and lets them see over time whether the agent’s confidence matches how often it is right.

Finally, changes that reduce what the SOC can see, such as switching a detection rule off, deserve more care than changes that add to it. Asking for a second person to approve them is a simple guard against one mistake, or one bad actor, creating a blind spot.

What to look for when choosing one

It is worth running a trial on real alerts from your own environment, including a few you already know the answer to. A system that gets the known cases right and shows its working on the rest is easier to trust than one that only looks good on prepared examples.

Demonstrations tend to show the best case. The questions below get closer to how a system will behave on your alerts, with your data:

• Can you see every question the agent asked and every record it read, for any verdict?
• Does it show how sure it is, and is it honest when it is unsure?
• Where does your alert data go, and can the AI run inside your own environment?
• Does it work with the security tools you already run, without a migration?
• What does it do on its own, and what waits for a person’s approval?
• Does it learn from your team’s corrections, and can you see what it has learned?

How armor360 approaches it

armor360 is an AI SOC analyst. It collects alerts from the security tools you already run, starting with Elastic Security, whose logs it also searches, and also Splunk, Microsoft Sentinel, CrowdStrike Falcon and Microsoft Defender. It investigates each one and gives a verdict of real attack, suspicious, harmless or not sure, with a confidence and the evidence behind it, every record cited where it is used.

It takes no action on your systems. It recommends, and your people decide and act. A person agrees with each verdict or changes it, corrections are remembered for later investigations, and the audit trail keeps who approved what and when. armor360 runs on its own AI model, fine-tuned by our team, and can run fully inside your environment, the AI included, so alert data need not leave it.