The Rise of AI in Cybersecurity: A Primer on Machine Learning, Neural Networks, and Deep Learning - ThreatWarrior

The Rise of AI in Cybersecurity: A Primer on Machine Learning, Neural Networks, and Deep Learning


Artificial Intelligence (AI) is poised to transform the field of cybersecurity by enabling machines to work and learn like humans. However, many security leaders and practitioners are at the beginning phases of understanding how the AI in cybersecurity transformation will take place – and how to equip themselves for smart decision-making circa their own defense-in-depth strategies and implementations.

To that end, ThreatWarrior is rolling out a three-blog series that we hope will shed light on different forms of AI, and ultimately how it can make us far more effective at finding and rooting out suspicious and malicious activity from the very lifeblood of any business – its digital network environment. We’ll hit on topics like these:

Blog 1: An AI Primer (this blog):

  • What is artificial intelligence?
  • What is intelligence, even?
  • What is machine learning?
  • How many types of machine learning are there, anyway?
  • Clear me up on how to think about AI, ML, neural networks, and deep learning
  • From a cybersecurity point of view, why should I care?

Blog 2: Unsupervised Neural Networks: A big step forward from today’s AI implementations

  • Applying AI to Cybersecurity
  • Traditional AI Approaches
  • Supervised and Unsupervised ML
  • Unsupervised Neural Networks
  • Benefits of Unsupervised Neural Networks in Cybersecurity

Blog 3: ThreatWarrior’s Use of Unsupervised Neural Networks and Deep Learning

  • Core design principles
  • How these principles lead to an effective NDR solution 

In this first blog, we’ll start with some foundational definitions.


Artificial Intelligence In a Nutshell

Artificial Intelligence (AI) is the simulation of human intelligence processes by machines. The goal of AI is to create intelligent machines that can work and learn like humans. In a world where computers permeate every aspect of our lives, there is no limit to the potential benefits of AI. With its ability to take over repetitive or dangerous tasks, humans can more freely do work we are better equipped for – tasks that involve creativity, long-term visionary efforts, social interactions, etc.

With respect to AI in cybersecurity, the opportunity  to reshape defense-in-depth effectiveness is enormous given its ability to process large amounts of data in ways that humans cannot, and at a much faster rate. This allows it to discover patterns in data that would go unnoticed by humans.

Our white paper covers the field of intelligence and learning models in greater depth.


Machine Learning in Cybersecurity: Enabling Machines to Learn from Data

Machine Learning (ML) is a subset of AI that focuses on enabling machines to learn from data without being explicitly programmed. ML algorithms can learn and improve from experience by using statistical models and algorithms to analyze and identify patterns in data. ML can be supervised, unsupervised, or semi-supervised, depending on the type of learning approach used.

Unquestionably, machine learning security force multiplies the effectiveness of security analysts and threat hunters in a dramatic manner.

Our white paper discusses six different types of machine learning.


Neural Networks: The Artificial Brain for Analyzing Data

Neural Networks are a type of ML algorithm that is modeled after the structure of the human brain. Neural networks can process large amounts of data and identify patterns and relationships that may not be immediately apparent to humans. Neural networks consist of layers of interconnected nodes that process and analyze data. Each layer processes information and sends it to the next layer, allowing the network to learn and improve its performance over time.


Deep Learning: The Future of Machine Intelligence

Deep learning is a subfield of ML that uses neural networks with multiple layers to process and analyze data. Deep learning algorithms can process vast amounts of data and identify patterns and relationships that are less identifiable through traditional programming methods. Deep learning is used in a wide range of applications, including image and speech recognition, natural language processing and autonomous vehicles.


AI, ML, Neural Networks and Deep Learning: Enhancing Cybersecurity Defenses

AI cybersecurity solutions have enormous potential for the field of cybersecurity. AI and ML are already in broad use to enhance security defenses and improve threat detection capabilities, e.g., anomaly detection. Anomaly detection is the process of identifying unusual or unexpected activity on a network or system. AI and ML algorithms can analyze network traffic, user behavior, and other data to identify anomalies that may indicate a potential security threat.

A second area where AI and ML are being used is in threat intelligence. Threat intelligence involves collecting and analyzing information about potential security threats. AI and ML algorithms can be used to analyze large volumes of data and identify patterns and relationships that may be indicative of a specific type of threat or attack.

While much newer in practice, deep learning and neural networks can be used to substantially improve the scope and accuracy of threat detection. With their ability to recognize specific patterns in network traffic or user behavior – through unsupervised learning capabilities – neural networks can move well past ML to find more advanced threat activity, and much earlier in the kill chain. 



To summarize the relationships between these technologies, here are some simple takeaways:

  1. AI is a broad concept that enables machines to simulate human intelligence and perform human tasks.
  2. Machine learning (ML) is a subset of AI focused on how machines learn patterns and insights from data without being explicitly programmed.
  3. Neural networking is a subset of ML that is modeled after the structure and function of the human brain.
  4. Deep learning uses neural networks with many layers to learn complex patterns and representations in data. 

Why should you care?

Global cybercrime will be a $10.5 trillion USD business by 2025, up from $3 trillion USD in 2015, according to Cybersecurity Ventures. And its impact does not stop there. The costs associated with damage and destruction of data, lost productivity, post-attack business disruption, forensic investigations, restoration and deletion of hacked data and systems, and reputational harm are enormous.

At the same time, everyone in the cybersecurity business knows we are abysmally behind in the required human talent to fight the good fight. While the global cybersecurity workforce grew to 4.7 million people in 2022, we remain 3.4 million security professionals short.

Given these realities, a well-designed intersection between artificial intelligence and cybersecurity can change the game – specifically with respect to faster detection of network breaches and incidents that underpin top of mind security concerns – like ransomware, phishing, data leaks and more. But in order to do so, we have to move well beyond where we are today with relatively mundane supervised ML. And we don’t have forever. The adversaries are aware of where things are headed too.

Can’t wait for blogs 2 and 3? Go ahead and grab our full white paper here. No reg wall. We are sharing freely just to help educate. Of course if you have any questions about what we have shared, feel free to reach out. We love to talk about AI and how ThreatWarrior plans to advance its impact on cybersecurity.

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