Mobula · Self-Learning SOC
A SOC that gets
smarter
every shift.
Every verdict your analysts confirm or correct feeds Mobula's AI back into itself. The platform improves from your real decisions - not a vendor's generic model update six months later.
Every analyst decision is a training signal.
When an analyst confirms an escalation, closes a false positive, or updates a verdict, that decision feeds directly back into the detection model. No data leaves your tenant. The model learning from your environment is yours alone.
Detection, escalation, playbooks - all of it learns.
The feedback loop touches every layer of the platform. Detection weights shift to match what your analysts confirm. Escalation scoring tightens around what your team actually wakes up for. Playbook suggestions improve based on what worked in your past incidents.
Your environment. Your model. Not a shared baseline.
Every vendor claims their AI gets better over time. Most mean: they retrain a shared model on aggregated data every few months and push an update. Mobula's model is trained on your tenant alone - continuously, in real time, from your analysts' actual decisions.
Full visibility into how your model is evolving.
Mobula surfaces a model health dashboard showing accuracy trends, which patterns improved, which are still noisy, and where more analyst feedback would help. You always know exactly where the model stands.
Continuous learning · your model · real-time feedback
The longer it runs,
the better it gets.
Mobula compounds. Every shift adds signal. Every confirmed verdict sharpens detection. At 90 days, you have a platform that knows your environment better than any out-of-the-box model ever could.