AI-powered threat defense
Your analysts can't read every alert.
Machine learning can sort and correlate alerts as they arrive and flag behavior that doesn't match a user's normal pattern. Your analysts start with the alerts most likely to be real, instead of working the queue top to bottom.
What we deploy.
AI-assisted SOC
Alert triage and correlation that cut the queue your analysts work through by hand.
Behavioral analytics
User and entity behavior analytics (UEBA) to detect anomalies and insider threats.
Automated threat hunting
Machine-generated hypotheses and pattern recognition to find threats before they trigger an alert.
Deepfake detection
Detection of synthetic media used in social engineering, fraud and disinformation campaigns.
Phishing and BEC detection
ML models that detect well-crafted phishing, AI-written lures and business email compromise.
Adaptive detection
Models that keep learning from new data instead of relying only on fixed rules.
Where it helps
AI-assisted SOC
Machine learning for alert triage, correlation and investigation support alongside your existing analysts.
Behavioral analytics
Anomaly and insider threat detection from models trained on your own environment.
Automated threat hunting
Hunting for threats that haven't raised an alert, using machine-generated hypotheses and pattern recognition.
Deepfake detection
Spotting AI-generated audio, video and images used in social engineering and fraud attempts.
Phishing detection
Models that detect well-crafted phishing, including emails written by AI.
Ongoing tuning
Detection models retuned as new attack patterns appear in your environment.
How we deploy it
We start from the security tools you already run and tune against your own environment.
Environment assessment
Review your current security stack and where machine learning would help.
Use case prioritization
Choose the use cases worth doing first, given the threats you face and the staff you have.
Solution design
Design detection that feeds into the tools your team already uses.
Implementation
Deploy, configure and integrate the detection tooling.
Model training
Train and calibrate models on your own environment so they learn what normal looks like there.
Optimization
Keep tuning based on how detection performs and what your analysts report back.
Use cases.
SOC augmentation
Fewer alerts to wade through and faster triage, through machine-learning prioritization and correlation.
Insider threat detection
Identify anomalous user behavior and potential insider threats through behavioral analytics.
Fraud prevention
Detect deepfakes, synthetic identities and AI-generated fraud attempts as they happen.
Email security
Catch phishing and BEC attacks with models trained on recent attack samples.
Start with where the alerts pile up.
Tell us what your SOC looks like today and which tools feed it. We'll tell you where machine learning would help and where it wouldn't.