What is the difference between SOAR and an AI SOC?
SOAR, which stands for security orchestration, automation and response, runs playbooks: fixed sequences of steps someone wrote in advance, such as “when a phishing report arrives, extract the links, look them up, and open a ticket”. An AI SOC uses an AI agent that reads an alert, decides what to ask next, queries the data to answer, and keeps going until it can give a verdict. SOAR follows a path. An AI SOC chooses one.
That difference decides where each fits. SOAR is good at repeatable actions with known inputs: enrich, notify, open a ticket, run an approved change. An AI analyst is good at open-ended investigation, where the next useful question depends on what the last answer showed. Most mature teams end up wanting both, and the useful question is which work goes where.
How SOAR playbooks work
A SOAR platform connects to your security tools through their interfaces and runs playbooks when something triggers them. A playbook is a flow chart: take these fields from the alert, call this lookup, if the result is bad do this, otherwise do that. The steps are written by an engineer, tested, and then run the same way each time.
That predictability is the point. A playbook for a phishing report will always extract the sender, the links and the attachments, check each against threat intelligence, search the mail system for the same message in other inboxes, and open a ticket with the results. An auditor can read the playbook and know exactly what happens. Nothing is improvised.
The limit is the same predictability. A playbook can only handle the cases its author foresaw. When an alert does not match the expected shape, such as a field that is empty, a new kind of attack, or a question no branch covers, the playbook either stops or hands the case to a person.
- Trigger: an alert, a report or a schedule
- Enrichment: lookups in threat intelligence, asset inventory, identity systems
- Branches: fixed if-then rules written in advance
- Actions: open tickets, notify people, run approved changes on other systems
- Record: a log of which steps ran and what they returned
How an AI SOC analyst decides its next step
An AI SOC analyst starts from the alert and asks itself questions, the way a person would. Which process raised this? What started that process? Has this account signed in anywhere unusual? Are there other alerts on the machine? Each question becomes a query against your SIEM, endpoint data, identity logs or threat intelligence, and each answer shapes the next question.
Take an alert for a burst of reconnaissance commands on a Linux server. A playbook could enrich it and file it. An AI analyst can ask what launched the commands, find an unfamiliar process, check whether that process talks to the internet on a schedule, look up the destination and decide the activity is a foothold. None of those steps needed to be written in advance; they followed from the evidence.
Because the path is not fixed, the record matters more. A good AI analyst keeps each question, query and result, and cites the records behind its verdict, so a person can check the reasoning rather than trust it.
When to use SOAR and when to use an AI analyst
Use SOAR where the work is the same each time and the right action is agreed in advance. Use an AI analyst where the work is a judgement that depends on what the data shows. The test is simple: could you write the steps down completely before seeing the alert? If yes, a playbook will do it reliably. If no, you need something that can reason.
Many tasks split across the line. Enriching an alert with asset owner and location is playbook work. Deciding whether the alert is a real attack, given that enrichment and everything else on the machine, is investigation work. Sending the verdict to the right channel and opening a ticket is playbook work again.
- SOAR fits: enrichment, ticket creation, notifications, routing, approved changes, evidence collection with fixed steps
- AI analyst fits: deciding whether an alert is real, following unexpected leads, joining related alerts, explaining a verdict in plain words
- Either can fit: phishing triage, with SOAR for collection and an AI analyst for the judgement on unusual cases
What it takes to keep each one working
SOAR’s hidden cost is upkeep. Each playbook depends on the interfaces of the tools it calls, the field names in your alerts and the way your processes work today. When a tool is upgraded, a field is renamed or a team changes how it handles a case, playbooks break or quietly do the wrong thing. Teams with many playbooks often need an engineer whose main job is keeping them running.
An AI analyst has a different kind of upkeep. It needs good connections to your data, accurate business context, such as what each machine does and who owns it, and people reviewing its verdicts so mistakes are found and corrected. It copes better with a renamed field or a new alert type, because it reads what is there rather than following a fixed path. But it still needs checking, and its quality should be measured by how often reviewers agree with it.
Neither is maintenance-free. The question is whether you would rather maintain logic you wrote or review judgements the system made.
The risks of automated response compared with recommendations
SOAR is often used to take response actions automatically: disabling an account, blocking an address at the firewall, isolating a laptop. For well-understood cases with a tested playbook, that can save time. The risk is the case the playbook did not foresee. A playbook that disables accounts on a certain pattern will also disable the finance director’s account during the year-end close if the pattern matches.
Letting an AI agent take those actions on its own adds a further risk, because its path is not fixed in advance. Most organisations are not ready to let software that reasons for itself switch off production systems, and auditors tend to ask who approved each change. A safer pattern is for the AI analyst to recommend and a person to decide. The person can then trigger a tested SOAR playbook, or act by hand.
This pattern keeps the strengths of both. The AI does the open-ended thinking and explains it. A person who knows the business makes the call. The action itself runs through a playbook that has been tested and approved, with an audit trail of who decided what.
- Automatic actions: fastest, but wrong actions hit production systems with no one checking
- Recommendations with approval: slower by the time it takes a person to read, but every action has an owner
- Middle ground: automatic actions only for narrow, tested cases with an easy undo
How to use SOAR and an AI SOC together
A practical design puts each where it is strongest. Playbooks collect and enrich at the start, the AI analyst investigates and gives a verdict, a person reviews it, and playbooks carry out whatever the person approves and close the loop with tickets and notifications.
If you already run SOAR, start by moving the judgement out of playbooks that have grown too many branches. Those are usually the ones that break most often. Keep the playbooks that do simple, repeatable things well. If you do not run SOAR, an AI analyst with good notification and ticketing connections may cover what you need for now, and you can add playbooks for actions later.
How armor360 approaches it
armor360 is an AI SOC analyst, not a SOAR platform. It investigates alerts from your existing tools by asking questions and querying data, keeps each record it collects, and gives a verdict with the evidence cited and recommended next steps. It takes no response actions: armor360 recommends and people act.
Findings go where your team works, including Slack, Microsoft Teams, PagerDuty, email, Jira, ServiceNow and webhooks, so a person can review the verdict and, if they choose, start an approved playbook in the tool they already use.