Machine learning to tune
We leverage machine learning to remove false positive alerts. Focus on the real threat while retaining control of data.
Suggested tuning rules
The ML/AI algorithm constantly surveys your threat landscape. If new alert similarities arise, you are recommended new tuning rules.
Automatic or guided
Tune out alerts automatic or guided. If alerts match your tuning rules, tune them automatically or manually with a simple click.
No more alert fatigue
Remove false positives with AI/ML
Remove 40 – 80% of false positives and save 32 minutes per false positive alert which is the average handling time.
As we are not using static, fixed rules, the algorithm is agnostic to changed data schemes. If you you fixed rules or playbooks, you would have to change them every time
Adding enrichment to alert improves the efficiency of the ML/AI tuning algorithm because it adds more context
You algorithm is improved as we use anonymized data from other clients to tweak the algorithm. Then launched attacks across different sectors or customers are more easy discovered
Minimize false positive alerts
Automatically tune alert rules
Tuning removes false positives and provides the means to stop critical incidents in their tracks. Seculyze will automatically tune-out irrelevant alerts using state-of-the-art machine learning, but the software provides the flexibility to manually override the algorithm, should you desire more control in the analysis process.
Spend less time analyzing false positives and save resources for the critical alerts that require full attention.
Read how we tuneSimple asssembly
Control is yours! Cost is down!
Choose to set up your alert tuning rules to run automatic or by a click by you, so you are in control. This includes a time frame for running the rule and also changing the severity. In that way input to your cost can be less – either internally in your MDR or as input to a supplier.
Read about control functions