The document discusses the necessity of integrating artificial intelligence in cybersecurity, highlighting the differences between analysis, analytics, and AI. It identifies the exposure of personal identifiable information (PII) as a critical issue affecting various sectors, including finance and government. Additionally, it outlines the use of machine learning and advanced analytics to enhance data analysis and decision-making in the complex cybersecurity landscape.

1. Ashrith Barthur, PhD
Security Scientist
Is it Time for Security to Use Artificial
Intelligence ?

2. Overview
● Analysis vs. Analytics
● Complexity in Cybersecurity
● Biggest Problem of Cybersecurity
● Examples
● Analytics with ML
● Advanced Analytics

3. Analysis vs. Analytics vs. AI
● Analysis - Study and understand collected data
● Analytics - Data analysis with predictability.
● AI - Analytics with Intelligence

4. Complexity in Cybersecurity
● Complex Protocols
● Massive Amounts of Data
● Attack Asymmetry
● Distributed Attack Surface

5. Biggest Problem of CyberSecurity?
Exposure of PII - Personal, Identifiable
Information
Affects:
● Banks and Financial Institutions
● Corporations
● Governments

6. Examples
● Office of Personnel Management
● Sony
● Home Depot
● Target

7. Analytics with ML
● Random Forest
● Gradient Boosting Machines
● Deep Learning
● Neural Networks

8. Advanced Analytics? Why?
● Multiple Sources of Data
● Dynamic contexts for Analysis
● Extremely low latency in prediction.

9. AI? Why?
● Multiple Vectors of Attacks
● Many 0-day vectors
● Wait time with analysis is a lot

10. What is H2O Doing?
● Leveraging H2O for data analysis and
analytics
● Building Dynamic Contexts to Analyse data
● Providing Intelligence in sourcing, combining
data.

11. Thank You
Questions?