CISO Challenge #4

I need better threat detection than static IOCs

Static IOCs only detect already known malware. A bit more advance attackers actively avoid detection by IOCs and malware signatures by frequently changing their malware and command&control infrastructure. Thus, IOC-based detection is always one step behind.

Exeon’s approach

ExeonTrace uses supervised and unsupervised machine learning models to detect suspicious behaviours. These behaviours typically stay the same, even when attackers change their malware or command&control infrastructure
Typical detection pattern includes Internal reconnaissance, C&C channels, lateral movement, and data leakage
IOCs can be correlated with the network data as well

Benefits for CISOs and security teams

Avoid always being “one step behind attackers” thanks to machine learning-based detection that is much harder to avoid by attackers
Higher threat detection accuracy

Future-Proof NDR

Combining the best from traditional NDRs and SIEMs

ExeonTrace works with light-weight log data as SIEMs do, while traditional NDRs rely on traffic mirroring. For the data analysis, ExeonTrace provides specialised detection algorithms for network log data - like traditional NDRs.

Other challenges you might have:

Are you facing other challenges that we didn't cover yet?

We are very happy to discuss them with you personally. Just book a live demonstration of ExeonTrace.

Main benefits of ExeonTrace

Comprehensive Visibility

Visibility into your IT network to identify weaknesses before they are exploited by attackers (exposed services, shadow IT, insecure and risky communication etc.)

No traffic mirroring

Algorithms are analysing light-weight network log data

Reduced SOC workload

Ready-made use cases and ML models, automated cross-data correlation and intuitive visualisations make the SOC work more effective and efficient

Not affected by encryption

Metadata analysis is unaffected by network data encryption