What is Artificial Intelligence? And How is it Impacting Cybersecurity?

Artificial intelligence isn't just robots taking over the world (though Hollywood loves that narrative). At its core, artificial intelligence refers to computer systems that can perform tasks typically requiring human intelligence—like recognizing patterns, making decisions, and learning from experience.

The AI family tree

Think of AI as the big umbrella term, with some pretty important relatives underneath:

Machine Learning (ML): The cousin that learns from data without being explicitly programmed. Feed it enough examples, and it starts recognizing patterns on its own.

Deep Learning: The overachiever in the family. It uses artificial neural networks with multiple layers to process information—kind of like how our brains work, but digital.

Neural Networks: The building blocks that mimic how neurons connect in our brains. They're what make deep learning possible.

Here's the thing: all deep learning is machine learning, and all machine learning is AI—but not the other way around. It's like saying all squares are rectangles, but not all rectangles are squares.

Where do LLMs fit into the AI tree?

If you’ve been hearing names like GPT, Claude, and Llama thrown around but aren’t quite sure how they fit into the larger artificial intelligence (AI) universe, you’re not alone. They are Large Language Models (LLMs), and they live in this family tree under deep learning. Think of them as a specialized branch of deep learning models designed specifically to process and generate language with human-like reasoning. They are built on neural networks (typically transformer architectures) and trained on massive amounts of text data to learn patterns of language, grammar, knowledge, and reasoning.

So in family terms, you could describe LLMs as:

• A grandchild of AI

• A child of deep learning

• Built on neural networks (transformer architecture-based)

• Specializing in language understanding and generation

A quick history lesson

AI isn't exactly new. The term was coined back in 1956, but the technology has been evolving for decades. What's different now? We've got the computing power and data volumes to actually make AI work at scale. Plus, when ChatGPT was introduced in 2022, its popularity demonstrated a massive amount of consumer interest in generative AI.

How AI is revolutionizing cybersecurity

The AI cybersecurity revolution is happening right now. And yet, the automation AI provides doesn't replace human analysts—it frees them up to focus on the complex stuff that actually needs their expertise.

Let's dive into the specific ways AI is helping people keep the bad guys out.

Threat detection that never sleeps

Let’s be real—endpoint detection and response already provides this, but AI is giving it a boost. AI helps analysts with context enrichment during the threat detection and response process. Specifically, this can help give security analysts the additional contextual information that they need to help determine if a threat is malicious.

Machine learning cybersecurity applications can help spot unusual patterns that might indicate an attack, even if it's something completely new. Think of it as having a security guard who notices when someone's walking differently than usual, even if they're wearing the right uniform.

Nobody wants to manually sift through thousands of security alerts every day. AI and automation can help security analysts:

• Prioritize threats based on severity

• Respond to common attacks

• Coordinate responses across multiple security tools

• Generate detailed incident reports

Behavioral analysis and anomaly detection

Here's where AI gets really clever. By learning what "normal" looks like for your network, users, and applications, AI can help analysts understand when something's off.

For example, if an employee who usually accesses files during business hours suddenly starts downloading massive amounts of data at 3 AM, that's a red flag worth investigating. AI systems can help analysts catch these anomaly detection scenarios in real-time. But it’s important to note that AI is not good at binary decision making (at least not yet), which falls to human analysts.

Phishing and fraud detection

Phishing attacks are getting more sophisticated, but so is AI. Modern AI systems can analyze:

• Email content and structure

• Sender reputation and behavior

• Links and attachments

• Social engineering techniques

They're getting scary good at spotting fake emails that might fool even tech-savvy users.