Security Data Workshop

How to achieve success

A Security data workshop is focused on defining all the objectives of the customer regarding detection, ML models and response frameworks with a detailed focus on what data is required to achieve those objectives.

What questions do you want to ask

We are loading in log data to ask a specific set of questions. These questions define what data is required, and what Field and Values are required in that data.

Compliance Directives

Many customers are required, or have chosen one or more Compliance directives examples like:

  • Mitre
  • Lockheed
  • NIST
  • GDPR
  • EU
  • ANZ

Mapping

Mapping the data requirements, log data, network data, and app data to a given set of techniques in any given compliance directive is the focus of the Security Data Workshop.

Outcome based

An Outcome based, data driven, and prioritized methodology helps guide customers into an achievable outcome based security model, starting with Data.

Training the Customer

Many times the overall program, with  all the data possible is overwhelming. CyberQ Workshop helps build a team at the customer to focus all Security Analytics initiatives based upon data and outcomes.

Deliverables

CyberQ Data Workshop delivers a detailed list of compliance objectives mapped to individual data sources, and field/value pairs required from those data sources. 

AI and Analytics Test

Validation is a key step in the methodology that proves to the business that a given custom model is complete, and has the required outcome to make decisions.

Engaging CyberQ

CyberQ AI Models program can be purchased as a complete program to design, implement, and rollout into production a mature customized ML solution using COTS SW that can be integrated into the main SOC workflow.  The program is focused on achieving results from the customers platform.

Deliverables

The backbone of quality ML models is quality Data. CyberQ will work with the customer to audit and modify data ingestion to ensure quality outcomes from any Custom ML model.