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.
- 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
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
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