# What elite SOCs have in common, and why we built OrbitSOC around it URL: https://avispa.ai/blog/what-elite-socs-have-in-common/ [Blog](https://avispa.ai/blog/) The best security operations teams share a few habits. We built Avispa OrbitSOC so every SOC can work that way. ![Ross Mathews, Co-Founder](https://avispa.ai/assets/people/ross-144-ccac8b7871.avif) By Ross Mathews · 5 minute read I've spent nearly two decades in security operations, threat detection and response, and cyber strategy, building and leading security teams across federal government, defense, financial services and hospitality. Over that time I've seen what separates an elite SOC from the rest. It's rarely the biggest budget or the newest tool. It's how the team works. We built Avispa OrbitSOC around those habits. Here's what they are, and where each one shows up in the product. ## Every part of the SOC makes the others better In an elite SOC, nothing is a dead end. Intel feeds hunts. Hunts feed detections. Investigations feed lessons, and lessons sharpen the next triage. The team gets better after every investigation and every action, because what one part learns, the rest put to work. That's the loop OrbitSOC runs. When the intel pipeline finds a fresh article with an indicator you can query, it plans a hunt from it. When your coverage has a gap, the detection engineer agent proposes a rule to close it. When an analyst teaches a lesson or the AI proposes a fact, a SOC lead approves or rejects it, and approved knowledge reaches only the agents it names. Impact receipts show which investigations it changed. The product draws all of this as a memory map: eight layers over your organization, so every signal is remembered. ## They challenge each other's work Elite teams don't take a conclusion on faith, even a good one. Someone always asks what was missed. So OrbitSOC has a red team agent whose job is to attack every investigation's conclusion before a person sees it, looking for blind spots. A critical gap buys the investigation one more targeted round. Then a SOC manager agent checks the case independently. It approves the case, sends it back once with the evidence it wants named, or flags it for escalation. Confidence comes from evidence, not from the model grading itself. It's scored from what the investigation's own searches returned, and stated confidence never goes above 85%. Thin evidence reads as low confidence, never as a benign close. ## An investigation doesn't stop when you find something In a strong SOC, finding the adversary is when the real work starts. You keep watching, because what you found is rarely all there is. That's why OrbitSOC has what I call IR mode. When a security event is escalated, the affected hosts, accounts and IPs go on a watch list. Agents keep re-querying your SIEM and EDR for new adversary activity: every 15 minutes while it's hot, then hourly, then every 4 hours, then every 12 hours, for up to seven days. If activity resurfaces, the watch can be extended, and everything the agents find lands on the alert. After resolution, a lessons-learned agent proposes lessons from the case, and what the case missed becomes a proposed rule that goes live only when a SOC lead deploys it. ## AI multiplies what you can already see Here's the part that's easy to get wrong. AI is a force multiplier for a SOC with good telemetry and good content. With poor visibility, it's wasted spend. An agent can't investigate what your logs never captured, and it can't hunt for behavior your data doesn't record. So we built OrbitSOC to grow a newer SOC into a strong one, not only to run on top of one that's already mature. - **Discovery.** A scheduled assessment of what works here, from integration and alert intake to searching, hunting and detection. Every fix is tagged as your step or our step. - **Health.** Estate health shows log sources, connector health and EDR coverage, so you know whether OrbitSOC has usable telemetry. Unknown is never treated as clean. - **Content.** A curated library of Sigma rules, mapped to ATT&CK, that compiles to Splunk SPL and exports for Microsoft Sentinel, Falcon LogScale and Google SecOps. - **Rules.** The detection engineer agent proposes rules from coverage gaps, each with why it was proposed. None deploys until a SOC lead approves it. With that foundation in place, investigations and hunts pay off. You can describe a hunt in your own words, and OrbitSOC drafts the whole thing: the hypothesis, the telemetry it needs, the steps, the results you asked for and a publish checklist. It never invents an indicator, and every step says whether it can prove an absence. An administrator publishes it, and it runs every day. Each run publishes an assurance receipt that says what it established and what it couldn't look at, so "we looked and it was clean" is something you can check. ## People stay in charge None of this works if people lose the decisions. In OrbitSOC, the AI recommends and a person declares an incident, and that's enforced in the product, not just written in a policy. Response is guidance mapped to your playbook steps, plus guarded actions: contain a host or disable an account, each once a SOC lead confirms. For AI agents, OrbitSOC pauses an agent on its own when your rules say so and keeps its work for a person to review; a SOC lead can also pause a team or stop an instance. Nothing the AI learns or drafts goes live without approval. No detection deploys itself, no knowledge goes live unapproved, and learning never rewrites a closed investigation. That's the SOC I'd want to run: every part making the others better, every conclusion challenged, every investigation followed through, and every decision held by a person. ## Come and see it The best way to judge a SOC is to watch it work a case. Book a demo and bring an alert you know well, or watch a live investigation replayed from start to finish. ## Read next - [Avispa OrbitSOC](https://avispa.ai/orbitsoc/) - [How OrbitSOC works a case](https://avispa.ai/orbitsoc/investigations/) - [See a live investigation](https://www.orbitsoc.com/investigation-storyline?view=storyline) - [Book a demo](https://avispa.ai/book-a-demo/) [All posts](https://avispa.ai/blog/) · [Next: How to contain prompt injection hidden in the documents your agents read](https://avispa.ai/blog/contain-prompt-injection-in-documents/) ## See it on your own use case. [Book a demo](https://avispa.ai/book-a-demo/) · [Watch it work](https://avispa.ai/#watch)