<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=325921436538399&amp;ev=PageView&amp;noscript=1">
Skip to the main content.

Create an account on our custom learning platform, Foundry™, to access our free and premium content.

Create Free Account


New call-to-action

Applied Data Science

For Artificial Intelligence & Cybersecurity


Waitlist Registration

The Why Behind the Course

We made this course to teach cybersecurity professionals how to use AI/ML to defend their organizations.  Additionally, we want cybersecurity professionals to understand how to attack artificial intelligence applications and what are the associated risks.

In Development!

This course is being developed and will release late May to early June 2024. Get on the waitlist until then to know more and be informed of giveaways and early bird rates!

Course Description

This interactive course will teach security professionals how to use data science techniques to quickly manipulate and analyze network and security data and ultimately uncover valuable insights from this data. The course will cover the entire data science process from data preparation, feature engineering, and selection, exploratory data analysis, data visualization, machine learning, model evaluation and optimization and finally, implementing at scale—all with a focus on security-related problems.

Participants will learn how to read in data in a variety of common formats and then write scripts to analyze and visualize that data. A non-exhaustive list of what will be covered include:

  • Using machine learning and AI to detect network attacks within your organization

  • Hunting anomalous indicators of compromise and reducing false positives

  • Quickly and efficiently parsing executables, log files, PCAP and extracting artifacts from them

  • Writing scripts to efficiently read and manipulate CSV, XML, and JSON files

  • Using the Pandas library to quickly manipulate tabular data

  • Preprocessing raw security data for machine learning and feature engineering

  • Building, applying, and evaluating machine learning algorithms to identify potential threats

  • Automating the process of tuning and optimizing machine learning models

  • Using supervised learning algorithms such as Random Forests, Naive Bayes, K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) to classify malicious URLs and identify SQL Injection

  • Applying unsupervised learning algorithms such as K-Means Clustering to detect anomalous behavior

  • Rapidly and effectively visualizing data using Python

  • Attacking and exploiting machine learning models using adversarial techniques

  • Use NLP to detect spam and social engineering attacks

  • Using the latest LLM models for security analysis

  • Attacking and defending LLM-based applications against prompt injection

Learning Outcomes

Anyone who wishes to incorporate automated data analysis, artificial intelligence, machine learning and data science into their cybersecurity work should take this course and expect the following outcomes:

  • Use the Python data science ecosystem to rapidly prepare, explore and visualize cybersecurity data

  • Build and evaluate common machine learning models (both supervised and unsupervised) and apply these techniques to cybersecurity use cases

  • Develop unsupervised models to uncover anomalies and other patterns in cybersecurity data








Course Author

Name: Charles Givre (LinkedIn)

Currently: Head of Artificial Intelligence, Stealth Startup

Bio: Charles is the CEO and founder of DataDistillr, which is dedicated to making the world's data easy to use and query. Prior to founding DataDistillr, Charles worked as a data scientist in cyber for JP Morgan and Deutsche Bank. Mr. Givre has taught (and is teaching) security data science courses at Blackhat and is a sought-after instructor. Mr. Givre co-authored the O'Reilly book Learning Apache Drill and is the PMC Chair for the Apache Drill project.

Charles Givre

Course Author

Name: Curtis Lambert (LinkedIn)

Currently: Senior Data Scientist, Raytheon

Bio: Curtis has more than 15 years experience supporting cyber security missions for the U.S. DOD specializing in application of data science techniques to national security challenges across cyberspace. He holds multiple SANS certifications in cyber security and loves taking on challenges others say can't be solved. Curtis started his career journey as a heavy equipment mechanic in central California working on agricultural equipment. He spent 6 years in the U.S. Army as a linguist and data analyst before becoming a consultant with BAH where he spent 9 more years supporting a variety of national security missions. Curtis is a CISSP and holds multiple SANS certifications. He is a relentless pursuer of knowledge and constantly engages in self-education through books, videos, and courses.


Curtis Lambert-1

Course Features


Data Analysis

Gain hands-on experience with vectorized computing, data frame management, and creating both static and interactive visualizations, essential for data interpretation and presentation.

Machine Learning in Cybersecurity

Tailored for cybersecurity applications, including practical training on classifiers, clustering, anomaly detection, and deep learning, all framed within security contexts. Address the challenges of hacking machine learning models, equipping students with knowledge to protect AI systems.

AI Model

Security Risks

Focus on the practical implications for cybersecurity and AI model hacking. Students explore neural networks, including CNNs and RNNs, learning to apply these to security tasks and understand how to safeguard against vulnerabilities in AI technologies.

Explore the Curriculum

Learning Modules

Note - this content is not finalized and may be subject to change prior to release.

Course Cost

The price will be released closer to the release date! Expect it to be within a range of $400 - $800


Frequently Asked Questions