AI-SOCAcross the whole stack

AI Security Operations

Agents work the hours nobody is watching. People still decide.

Every product in the stack produces signal. The limit has never been the tooling — it is that there are more alerts than there are people awake to read them. We put AI agents on that gap: they watch, correlate and prepare the fix overnight, then stop and wait for a human to approve anything that would actually change your systems.

0changes made without a human approving them

Talk to an Engineer See the Stack

How we keep AI safe to point at production

Autonomy is the part that should worry you. These four rules are what make agents usable on a live environment.

Asks before it acts

No agent writes to your systems on its own. Isolating a device, rotating a key, pushing a config — each one arrives as a request with its reasoning attached, and waits for a person to approve or reject it.

You keep the keys

Credentials and context stay in infrastructure you control. Models are rented and replaceable; the operational knowledge your environment builds up is the asset, and it remains yours.

Honest about what it knows

Findings come back with evidence and a confidence level. A blank result means not assessed — it never silently means fine.

Graded, not assumed

Agent work is scored against defined criteria before it counts as done, so you can see what was checked, what was found, and what it cost.

What the agents actually do

Overnight triage of alerts from EDR, SIEM, RMM and email security

Correlation across products that would otherwise be read in isolation

Draft remediation prepared and queued for your approval

Plain-English incident narratives instead of raw alert dumps

Durable transcripts of every agent action, for audit

Model-agnostic — no lock-in to a single AI provider

Technology partner

Built on ZilverNet

The AI layer runs on ZilverNet, an operations platform for infrastructure and AI. Its Intelligence family supplies the context store, the agent host and the grading engine; its approval model is what makes agents safe to point at production.

NeuroHive

Holds the context and memory. Any model — hosted, open-source or self-run.

NeuroBots

Runs the agents that do the work, with a permission card before anything real.

NeuroScope

Grades what comes back against criteria, with evidence and cost.

Visit ZilverNet

See it run against your own alerts

We will point the agents at a read-only slice of your environment and show you what they would have caught last month.

Request a Walkthrough