Leverage Big Data & Machine Learning to Win in the Age of Cybersecurity

Hackers are more sophisticated and attacks are more frequent and connected with each other. The systems on how you detect and how you monitor these environments need to change. Most organizations rely on SIEM for many years. SIEM basically takes our network logs and events to advise you on your network and potential intrusion detection and monitoring. SIEM only looks at small amount of data, typically few months and aggregates events together. This may not prevent the types of breaches that you see today. You need to detect advanced threats faster via machine learning and artificial intelligence to change the economies of cybersecurity with an open source platform that supports multiple LOB workloads.

Challenges with traditional SIEMs:

Scale: Depending on the SIEM, you have indexing & retention policy that is built into the SIEM provider. At some point, accessing your data that resides in SIEM won’t be possible because of the querying capabilities built into the traditional SIEM applications.

Enterprise Visibility: There are certain data streams that are not natural fit for your SIEM environment due to the fact that it will blow your cost in terms of what you want to pay for the SIEM environment. If you have to start capturing larger volumes of network data, unstructured information, they don’t fit into the SIEM systems because of the volume and type of data.

Analytics: SIEM is good at correlation based detection rules, but as the attackers become more advanced, they start taking advantage for the limitation of the SIEM system. Attackers will be lot more sophisticated in their attack techniques. Being able to apply large scale machine learning across multiple years’ worth of information and having that enriched information that has deeper context in improving the threat detection capabilities would help us in the age of cybersecurity.

How Big Data Hub will help you win in the age of cybersecurity

Data Flexibility: Big Data is made of components that allows you to store, process and analyse data. You can analyse the data using search, interactive SQL, machine learning. When you bring data to SIEM system, it is usually stored in proprietary data storage that is optimized specially for SIEM applications. However, when you bring the data to the Big Data Hub, it resides in an open source system so there is no vendor lock in and, it is optimized for a community defined data model, where We as customers have the ability to optimize the data model for our needs.  

Scale: Big Data Hub can scale linearly. You can deploy on premise or any cloud provider so that you can bring the TCO of storing the data down significantly.

Analytics: You can extend the analytics capability. With Big Data Hub, you not only get standard SQL engines, search engines. You can also leverage open source machine learning ecosystems in R, Python, Scala and apply it to your data.