AI and Machine Learning: Do You Know the Difference?


Machine learning and artificial intelligence (AI) are transforming how organizations modernize their approach to cybersecurity. The two inter-related technologies simplify cybersecurity operations, increase efficiency and reduce risk by helping security teams detect known and unknown attacks.  

Given these benefits, it is no wonder that the market for AI in cybersecurity is expected to grow at an annual rate of more than 31% through 2025, reaching more than $34.8 billion. But before your organization can truly maximize the benefits of these technologies—even if you already have some knowledge of what is machine learning and what is artificial intelligence—it is important to understand a couple of things:

  • Machine learning and artificial intelligence are often used as interchangeable terms, but they are not the same thing. They are related in that machine learning is a subset of AI, but each delivers different capabilities. For decision-makers in business, IT and cybersecurity, you can set proper expectations for what each can and can’t accomplish. 
  • Both machine learning and AI can bring huge benefits to your organization—but only if they are fed the right data, which means the data must have complete, relevant and rich context that is structured in a common language. 

In evaluating how machine learning and artificial intelligence can simplify operations and make your organization safer from cybersecurity threats, it is important to understand the capabilities, similarities and differences of the two terms. Here’s what you need to know.  

What Is Machine Learning?

There are different definitions used to describe machine learning, but they fundamentally say the same thing. This is a simple definition courtesy of Stanford University:

Machine learning is the science of getting computers to act without being explicitly programmed.

Machine learning requires a large and rich data set and the use of algorithms to learn from the data. It allows a computer to make predictions about new data it has never seen before, based on patterns it has seen in the past. The viability of any machine learning algorithm is only as strong as the data modeling behind it, according to Giora Engel, vice president of product management at Palo Alto Networks.

“The actual algorithm in use only plays a secondary role,” Engel says. “If the selected data parameters do not contain parameters that can predict the result, you can use fancy algorithms, but the accuracy of the results will be very low.” 

To ensure that the selected data parameters provide the data you need, you should be asking three fundamental questions of your teams:

  1. Can you see everything? You need to be able to see data from everywhere—across cloud, network and endpoints.
  2. Can you analyze it quickly?  Once you’ve established that you can see something, you need to be able to quickly analyze it, which goes well beyond just storing data and applying manual actions or additional tools to analyze it. Analytics must be baked into your processes so they can be done in real time.
  3. Are you leveraging automated response capabilities? Ask your teams how many people and tools it will take to respond and how long it will take to immediately stop an attack. If you don’t have automated response with machine learning, you are facing unnecessary and incremental risk. 

Bottom line: If it requires multiple people and tools to manually make sense of the data and act on it for response and future protection, you are not leveraging your data, you are not maximizing machine learning and you are not simplifying anything. 

What Is Artificial Intelligence?

As with machine learning, there are multiple definitions for AI. Here are a few that are resonant, culled from a simple internet search:

  • Artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. 
  • Artificial intelligence is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.
  • The core problems of artificial intelligence include programming computers for certain traits, such as: Reasoning, perception, and ability to manipulate and move objects.

Artificial intelligence requires the same care and attention to data collection and management as machine learning, so similar questions apply in order to maximize AI to simplify operations and reduce risk. But, as noted, artificial intelligence is a broader concept than machine learning and thus has the potential to deliver different benefits to your organization.

What Are the Differences?

To highlight the differences between machine learning and AI, we turned to Navneet Singh, product marketing director at Palo Alto Networks, who provided guidance as well as direction to several general respected industry reference points. Here is a summary:

  • Artificial intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart.” Machine learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
  • Machine learning is one of the ways we expect to achieve AI. Machine learning relies on working with large data sets, by examining and comparing the data to find common patterns and explore nuances.
  • You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working outwards. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart.

Business leaders may think of machine learning and AI as future technologies that can be deployed sometime down the road. When it comes to cybersecurity, that is not the case, particularly with machine learning. If your cybersecurity teams are not taking advantage of machine learning today, you are exposing your organization to greater risk of being successfully attacked. 

You are also probably spending more time and money on personnel resources doing jobs that can be better done with the help of machines. This doesn’t mean reducing staff; it means making your teams more efficient by having them use modern, effective and smart tools to simplify operations, reduce the time needed to respond and leverage automation to fight machines with machines.

Cybersecurity is emerging as one of the critical use cases for machine learning and AI. If your organization is not using these technologies, be forewarned—the bad actors who would do harm to your business are using them. When it comes to getting smart about cybersecurity, the future is now. 

Al Perlman, co-founder of New Reality Media, is an award-winning technology journalist. For the past dozen years he has focused on the intersection between business and technology, with an emphasis on digital transformation, cloud computing, cybersecurity and IT infrastructure. 

End Points

  • Artificial intelligence and machine learning are often used interchangeably, but they are not the same thing.
  • Machine learning is becoming particularly valuable in helping cybersecurity teams simplify operations and reduce threats.
  • As organizations increase their use of both machine learning and AI, it is essential that the underlying data is complete, rich and actionable.