autobotAI is the cybersecurity agent factory. Security teams use it to build their own swarm of agents that work every risk until it's closed: they investigate, find the owner, prepare the fix, get it approved, follow up and prove it held. People approve the calls that matter.
Detection is largely solved. Most security tools now find more risk than teams can close, and the bottleneck has moved to people and process: who owns a finding, who approves the fix, who follows up, and where the evidence lives. autobotAI is built for that part of the work.
What you can do with it
- Investigate and operate now. Ask Optimus to triage an alert, investigate an identity or collect audit evidence, using your own tools, before anything is automated.
- Build agents by describing the job. Describe a workflow to AI Builder or a goal to Optimus, start from 300+ ready-made templates in the library, or build node by node on the canvas.
- Run them across every security team. SOC, GRC, identity, AppSec, cloud, IT and network teams use the same factory, integrations and guardrails.
- Reach every tool you run. 1,000s of integrations, federated: agents query each tool where the data lives, alongside your SIEM rather than instead of it.
- Keep people in charge. Autonomy is set per action by risk. Changes that matter wait for a named approver in Slack, Microsoft Teams, email or a pull request.
- Prove the work. Every execution, decision and approval is recorded, fixes are re-tested, and a live risk register shows where every risk stands.
How it's different
| Instead of | autobotAI gives you |
|---|---|
| A script or playbook per tool, maintained by whoever wrote it | One factory where every agent shares integrations, memory and guardrails |
| A finding in a report that waits for someone to act | A fix that arrives ready to approve: a pull request, a WAF rule, a firewall or cloud-posture change |
| Copying every log into one place before anything can act | Agents that query the source at the moment of action |
| One model chosen for you | Any model, chosen per agent, from the provider you already trust |
| A SaaS-only black box | SaaS, or fully self-hosted in your own AWS account, where no workflow data, credentials or logs leave it. See Architecture and security |
Who uses it
- Security leaders use it to close more risk with the team they have, with audit evidence produced as the work happens.
- Analysts and engineers build and tune agents instead of chasing tickets by hand.
- Platform and cloud teams get fixes as reviewable changes, in the tools they already use, with an owner and an SLA.
- MSSPs and MDR providers run autobotAI as a multi-tenant, white-label service for their customers.
Start here
- How autobotAI works: the parts of the factory and how a risk gets closed.
- Before you begin: what to have ready.
- Quickstart: set up autobotAI and run your first workflow.
- Choosing a workflow or a harness: pick the right shape for each job.