- H2O Driverless AI trains classification models on your labeled security events to tell threatening activity from routine noise.
- Anomaly detection and clustering find activity that matches no known signature, even in unlabeled, noisy data.
- Every score comes with reason codes, so your security team, auditors and the board can see why an event was flagged.
- Models run beside the security tools you already have, using their logs and alerts as inputs.
More signals than your analysts can read
Your security team watches thousands of devices, users and applications across data silos that were never designed to talk to each other. Every tool raises its own alerts, and most of them turn out to be routine.
Attackers do not wait for signatures. Phishing, malware and malicious downloads change shape faster than rules are rewritten, so the events that matter hide among the ones that do not. When something does get through, you have to explain what was detected, when, and why your team acted the way it did.
How cyber threat detection works
Your security data science team builds models on the logs, alerts and incident history you already keep. The models score and rank events so analysts spend their time on real threats.
Step 1: join the data you already collect
Start with endpoint, network, identity and email logs, your asset inventory and past incident outcomes.
Step 2: classify threatening and routine events
H2O Driverless AI trains classification models on events your team confirmed as threats or cleared as routine. Automatic feature engineering turns raw logs into signals such as unusual login times and rare destinations, and natural language processing adds features from email and file text.
Step 3: find what no rule describes
Anomaly detection and clustering group events by behavior and surface outliers without labels.
Step 4: rank, explain and route
Each event receives a risk score with reason codes built on Shapley values. Your policy decides how scores route work: the riskiest events go to the front of the queue, and your analysts make every response decision.
Step 5: plan defenses where you are weakest
Models trained on incident history show where and how a breach is most likely, so you can direct budget and hardening to the weak points.
Explaining a flagged event
Auditors, regulators and the board want to know what was detected and why. Reason codes based on Shapley values give security leaders a factor-by-factor answer for every score, not just a number.
Proof
H2O can be used to classify security events, detect anomalies in noisy data, and explain flagged events to leaders and auditors. No customer result for cyber threat detection is published yet.