Data Science

Andradge excels at providing analysis to enhance organizations security posture and to mitigate risk. We employ all tenets of Data Science including Machine Learning, Artificial Intelligence, and Data Analytics to achieve vulnerability remediation prioritization and to build action-oriented insights that move the organization to a true SecDevOps.

Data Visualization

Data-driven stories with interactive visualizations allow audiences to explore and understand your findings. We provide training and consultancy in designing effective and accessible data Visualization.

Security Engineering

At Andradge, we develop requirements for SecDevOps services to design solutions that rapidly integrate security into the client pipeline. We implement and operate SecDevOps solutions at varying levels of maturity from commercial tool integration through custom program builds. Our experts act as SecDevOps security champions for our clients through clear communication of internal and external technical reports, project read-outs and presentations.

Andradge Security & Privacy Prioritization Experts Blog

Inspiring weekly blog covering Offensive Security Testing & Posture Assessments, Vulnerability Management, Data Protection and Digital Privacy Analysis, Industry & Regulatory Compliance, Security & Privacy Awareness Training, Security Technical Design Engineering & Architecture, Engineering SecDevOps. Learn more about us here.

Blogs

on September 20, 2016

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Motor Trend Regression Models

By Domtria Simba M on September 20, 2016

In this project we perform regression analysis of Motor Trend dataset.

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vFeed Correlated Vulnerability and Threat Database

By Domtria Simba M on July 2, 2016

This Shiny App is for searching and visualizing vFeed fully aggregated, cross-linked and standardized Vulnerability CVE Database that contains detective and preventive security information repository used for gathering vulnerability and mitigation data on the internet.

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Red Wine Quality Exploration

By Domtria Simba M on February 10, 2016

This was to explore the variables, structure, patterns, oddities and underlying relationships of a data set of my choice. I chose the Red Wine dataset

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