Shawn Riley, Chief Data Officer & Chief Information Security Officer, DarkLight Inc. on Artificial Intelligence in Cybersecurity. Shawn provides a formal definition of artificial intelligence, describes the two primary fields of artificial intelligence being applied in the cyber defense ecosystem, Data Science derived AI such as machine learning and deep learning & Knowledge Engineering derived AI such as expert systems. Shawn then looks at topics such as explainability, reproducibility, and use of AI in zero-trust. Slides from the 12 minute YouTube video https://youtu.be/Ubq8lTUey7Q

1. SHAWN RILEY ON A.I.
Chief Data Officer & Chief Information Security Officer
DarkLight, Inc.

2. WHAT IS ARTIFICIAL INTELLIGENCE
In computer science, artificial intelligence, sometimes called
machine intelligence, is intelligence demonstrated by
machines, in contrast to the natural intelligence displayed by
humans and other animals. Intelligence is the ability to
acquire and apply knowledge and skills. Artificial Intelligence
uses inference which is a conclusion reached on the basis of
evidence and reasoning. Data Science derived AI uses
inductive inference and probabilistic reasoning. Knowledge
Engineering derived AI uses deductive inference and
contextual reasoning.

3. KNOWLEDGE
ENGINEERING
Sensor
Human
Skills
DATA
SCIENCE
Focus on mimicking human
domain experts experience
in applying the knowledge
Focus on patterns in the
data set / data silo for
classification, clustering,
and making predictions
Logical
Top-Down Methods
Statistical
Bottom-Up Methods

4. KNOWLEDGE
ENGINEERING
Sensor
Human
Skills
DATA
SCIENCE
The conclusion of reasoning
is facts that are fully
transparent and explainable
The conclusion of reasoning
is probable (conjecture) and
not explainable (yet)
Shareable W3C Ontologies and
cognitive (skill) playbooks
support both peer review &
reproducibility of results
In Zero-Trust Architecture
the probable results from
data science derived AI need
to be verified - Forrester
“Machine-learning techniques used by
thousands of scientists to analyze data
are producing results that are misleading
and often completely wrong.” - BBC