AI for Humans: A Beginner’s Field Guide

As always, security at SURGe is a team effort. Credit to authors and collaborators: Tamara Chacon, Vandita Anand, and Audra Streetman.

Introduction: Why AI Can Feel Overwhelming

Drowning in AI buzzwords? You’re not alone—AI isn’t just trending anymore; it’s all over your home screen and headlines, and yes, even your threat models. It’s here to stay and evolving faster than the list of vulnerabilities you swore you patched last week.

Trying to make sense of AI these days can feel like someone dumped a 10,000-piece jigsaw puzzle on your desk—but with no picture on the box, pieces flung everywhere, and half of them labeled “generative AI,” “prompt injection,” or “LLMs.” You’re left squinting at the scattered bits, wondering where to even start. Is this a model? A tool? A vibe? Or just a hallucination?

That’s exactly why we launched this blog series: to cut through the noise, dodge the buzzword bingo, and guide you towards tools and concepts that won’t require a PhD… or a séance. We’ll help you put those puzzle pieces into place by simplifying key concepts and sharing curated resources that are worth your time.

Whether you’re a long-time techie or just tired of pretending you know what everyone’s talking about when it comes to AI, this blog is your no-nonsense beginner’s guide to the world of Artificial Intelligence (with of course, a cybersecurity twist).

Mini‑Glossary: Key AI Terms

Here are a few terms you’ll see often in this post. If any feel unfamiliar, don’t worry—there’s a full glossary and resources for deeper learning at the end.

1. LLM (Large Language Model) – Models trained on massive amounts of text to predict and generate language. They don’t “understand” language in a human sense, but they capture statistical patterns that let them produce human-like text.
2. GenAI (Generative AI) – AI models designed to create new outputs—text, images, code, and more—by sampling from patterns learned in training data.
3. RAG (Retrieval‑Augmented Generation) – A setup where an AI model retrieves relevant information from an external knowledge source and then generates a response grounded in that context.
4. MCP (Model Context Protocol) – An emerging protocol that enables AI systems to seamlessly interact with tools, applications, and data sources. It allows AI to connect with platforms like Slack, Google Drive, and databases without writing custom code.
5. Agents / Agentic AI – Systems that can carry out multi‑step tasks by combining planning, decision rules, and tool use, often orchestrating other models or data sources.
6. Guardrails – Mechanisms that constrain AI behavior to improve reliability, safety, and compliance—for example, filters, policies, or validation checks.
7. Prompt – The input text or instruction given to an AI system. This can be a user prompt (your direct request) or a system prompt (background instructions that guide the model’s outputs).

The AI Evolution: From Simple Rules to Self‑Directed Agents

AI systems haven’t always been as capable or flexible as the tools we see today. Over the decades, they have evolved through distinct stages—from rigid, rule‑based programs to statistical learners, generative models, and now agentic systems designed to coordinate multi-step tasks.

A Quick History of AI

Artificial Intelligence might feel like a new phenomenon, but the idea dates back to the 1950s. Early pioneers like Alan Turing, John McCarthy, and Marvin Minsky experimented with symbolic programs that could play simple games or solve math problems with hand-coded rules. In the 1960s–70s, “expert systems” emerged, using large sets of if‑then rules for medical diagnosis, troubleshooting, and other specific domains. These systems worked well in specific contexts but proved brittle and hard to scale. Momentum slowed during periods of reduced funding and optimism, known as AI winters (late 1970s, late 1980s-early 1990s).

The 1980s introduced early machine learning methods, and by the 2010s, advances in deep learning—powered by large datasets and parallel compute—enabled leaps in image recognition, language processing, and robotics.

Today, with Generative AI and emerging Agentic AI, systems are being designed for prediction, content generation, and orchestrated multi-step workflows—driving the fastest period of AI advancement to date.

Why Now?

AI’s sudden leap forward isn’t the result of one discovery, but several forces coming together. New algorithms showed that bigger models can keep improving as they grow. New training methods let AI learn directly from vast amounts of raw data, without needing everything labeled by hand.

At the same time, faster and more affordable hardware made it possible to train and run these giant models. And around that foundation, new tools have matured—databases that help models “look things up,” frameworks that let them handle multi-step tasks, and systems that check their answers for accuracy and safety.

Put together, these advances explain why AI feels like it suddenly jumped from theory into everyday use.

Artificial Intelligence (AI)

Artificial Intelligence is about designing computer systems that can perform tasks we often associate with human intelligence—learning from data, making predictions, solving problems, and generating new outputs. Instead of “thinking” like humans, AI models learn statistical patterns from data and apply them to new inputs.

AI has evolved from simple, rule‑based systems to models trained to recognize patterns, adapt to new data, and coordinate multi-step processes. In this post, we’ll explore key AI approaches and capabilities—Reactive AI, Limited Memory AI, Machine Learning (ML), Generative AI, and Agentic AI—and show how each builds on the strengths of the one before it.

You’ll also learn what Large Language Models (LLMs) are and why they’re at the heart of today’s AI boom, how Generative AI differs from earlier approaches, and where AI already impacts daily life and cybersecurity—from spam filters and fraud detection to copilots and automated workflows.

Reactive AI

Reactive AI is the simplest form of artificial intelligence—think “old‑school AI.” It doesn’t learn, adapt, or retain past information; it simply applies fixed rules or heuristics (basic if‑then logic) and always produces the same output for the same input.

Example: “If an email contains the word ‘free,’ then mark it as spam.”

That’s it—predictable, explainable, and easy to audit, but completely rigid. You’ll find reactive AI in early spam filters, rule‑based customer service bots, and classic game opponents with fixed strategies. The upside: it’s reliable for simple, repetitive tasks. The downside: zero flexibility; it can’t adapt or handle new situations.

Limited Memory AI

Limited Memory AI is a step up. These systems can learn from recent data and apply it to decisions, but don’t hold onto information long-term. During inference (when the model is generating outputs rather than being trained), most operate within a finite “context window”—meaning they can only work with a limited amount of recent input at once.

For example, a system may use recent sensor readings to steer a self-driving car, but once that context is gone, it doesn’t “remember” it. Similarly, Large Language Models (LLMs) use your current prompt and conversation history, but they don’t persist memory across sessions unless integrated with external tools such as retrieval systems or vector databases.

This taxonomy (Reactive vs. Limited Memory) is a common explainer, but keep in mind that research literature often classifies systems differently. Generative AI, because of its reliance on short-term context, is usually grouped under Limited Memory AI—but we’ll cover it in more depth below since it represents such a big shift.

Machine Learning (ML)

Machine Learning is an approach to building AI systems that can learn from data instead of relying solely on hand‑coded rules. By analyzing large datasets, ML models spot patterns, make predictions, and can improve performance as more data is introduced.

Key Approaches

• Supervised Learning: The model is trained on labeled data, meaning each example includes the correct answer. Example: training a spam filter on emails labeled “spam” vs “not spam.”
• Unsupervised Learning: The model is trained on unlabeled data and must find patterns or groupings itself. Example: clustering network traffic to flag unusual activity that might indicate a zero-day attack (an exploitation of a previously unknown vulnerability for which no patch or defense yet exists).

Common ML Techniques

• Linear Regression (Supervised): Fits a line through data to predict values, like estimating a house price from its size.
• Neural Networks (Supervised or Unsupervised): Imagine a web of digital “neurons” passing signals to each other. Each connection has a “weight” that acts like a volume knob; tuning those weights helps the system focus on important signals, such as recognizing speech or spotting phishing images.
• Deep Learning: Uses neural networks with many layers to uncover complex patterns in data—the technology behind things like language translation, fraud detection, and self-driving cars. These models often start by learning to predict missing pieces in their input (called self-supervised learning), and can later be fine-tuned with examples or trained further to specialize in specific tasks.

In Cybersecurity Today Machine learning underpins spam detection, fraud monitoring, anomaly detection in networks, and biometric systems like face or voice recognition. It’s the foundation of many AI systems—including the generative AI models that create new content.

Generative AI

Generative AI is one of the most talked-about areas of artificial intelligence. Instead of just applying rules or labels, it generates new outputs—text, images, music, code and more – by combining patterns learned during training with inputs (prompts). These systems don’t store long-term memory of interactions. They use a short-term context window—your prompt and conversation history—alongside the massive training data they were built on.

For example:

• Prompt: “Write a bedtime story about a pony named Buttercup.”
• Output: A new, original story, built on learned patterns from text data.

Instead of pulling prewritten answers from a database, the model predicts sequences of words (or pixels, or notes) based on probabilities, with a bit of randomness (“temperature” in LLM-speak) to vary the result. Behind the scenes, this is powered by large “foundation models” such as LLMs, trained using deep learning on vast datasets.

An easy way to picture it: imagine tackling that massive puzzle mentioned earlier—no picture on the box, pieces scattered everywhere. At first, the system makes rough guesses about where the pieces might fit, learning from each mistake, and slowly spotting patterns. Over time, it can place pieces with surprising accuracy—even combinations it hasn’t seen before.

Agentic AI

Agentic AI goes beyond single-step responses. These systems are designed to plan, coordinate, and carry out sequences of actions using tools or external data sources—without the user spelling out every step.

Instead of answering a single prompt, an agent might:

• Break a complex task into sub-tasks.
• Call tools or APIs to get information.
• Adjust its plan based on results.

Example: Instead of just booking a flight when asked, an agentic system could check your calendar, compare prices, and reschedule if plans change.

The Agentic Loop

Most agent frameworks follow a cycle:

1. Plan the next step.
2. Act by using a tool or retrieving data.
3. Observe the result.
4. Revise the plan.

In cybersecurity, such an agent might scan logs, flag anomalies, pull related threat intelligence, and draft a remediation plan.

Why Guardrails Matter

Agentic systems are more complex than simple, direct, interactions with models. Guardrails (rules, safety checks, oversight) and observability (monitoring and evaluation) are essential to keep them reliable, transparent, and aligned with goals.

You’ll see this approach emerging in workflow automation, customer service, and research assistants that can operate across multiple steps. The upside is efficiency—agents can save time and tackle dynamic, multi-step problems. But with this autonomy comes the need for increased oversight. As an agent gains more capability and data access, it becomes more important to maintain observability, and periodically verify the agent’s output.

How to Actually Learn (Without Burning Out)

If you’re just beginning your journey into AI, it’s easy to feel overwhelmed by the sheer volume of resources available. To keep things manageable, start small: choose one free online course that introduces AI fundamentals and pair it with a blog or podcast that matches your interests. This focused approach prevents information overload and helps you build a solid foundation without feeling scattered.

Don’t worry if you’re not a math or programming expert. At the outset, it’s much more important to grasp the core concepts than to stress over equations or code. Dive in with hands-on practice using interactive labs or AI playgrounds, as these experiences are far more effective than simply reading articles or watching videos. Keep in mind, AI is evolving rapidly, so developing the skill of learning itself—adapting, exploring, and staying curious—will serve you better than trying to master a single tool or technology.

What’s Next in the Series

Coming up next in the series, we’ll take a closer look at how AI is being used in cybersecurity, highlighting real-world examples that we explore ourselves. We’ll also guide you through the basics of getting started with our Foundation AI model and more, making the next steps in your learning journey both practical and approachable.

Key AI Terms You’ll Hear Everywhere

Where to Learn More: AI & Cybersecurity Courses

If you want to strengthen your AI and cybersecurity skills—whether as a beginner or a seasoned security pro—there’s now a wide mix of well‑rated free and paid learning options online. Many offer hands-on labs, and some might even be free through your employer’s training portal.

Free Courses & Labs (Beginner to Intermediate)

• Codecademy – Enterprise Security: AI, GenAI, & Cybersecurity: Beginner‑friendly intro to AI concepts in enterprise security.
• Kontra Interactive Labs: Interactive challenges focused on OWASP Top 10 AI risks.
• MIT OpenCourseWare – Artificial Intelligence (Free lectures): Foundational concepts, including decision‑making and machine learning.
• NVIDIA Deep Learning Institute (DLI) (Free courses) (Free Courses): Entry modules on AI, deep learning, and cybersecurity overlap.
• eCornell / Employer Learning Portals: Check if your company offers free access to premium learning providers like eCornell, O’Reilly, or Pluralsight.
• OWASP AI Security and Privacy Guide (free online resource): Not a course, but a must‑read for developers and security teams working with AI.

Paid Options (Intermediate to Advanced)

• Modern Security – Build, Break, and Defend AI Applications (~$599): Hands‑on course building and securing GenAI apps.
• Coursera (~$49/month): Options include Intro to AI for Cybersecurity, Generative AI for Cybersecurity Professionals, and AI for Everyone.
• edX / University Offerings (variable pricing): Courses like Cybersecurity for AI Systems (Linux Foundation) or AI Applications and Cybersecurity (various universities).
• Udemy – Artificial Intelligence for Cybersecurity (~$20‑50 depending on sales): Practical AI security tools and defense methods.
• SANS Institute – SEC545: GenAI and LLM Application Security (~$5,250): Deep‑dive training on securing generative AI and LLM‑powered applications.
• Pluralsight – AI for Security Professionals Path (Subscription required): Broad AI engineering + security integration.

Pro Tip: Platforms like Coursera, edX, and Udemy often let you audit courses for free (view content without the certificate)—a great way to sample material before committing. Pairing a free intro with a targeted paid course can give you both breadth and depth without overspending.

Curated Content: Blogs, Videos & Podcasts

If you want to follow AI’s rapid evolution—especially its impact on cybersecurity—these resources are worth bookmarking. They combine practical guidance, research insights, and real‑world applications, and most are updated frequently.

Blogs

• SURGe Blogs – Defensive and adversary AI use cases, technical deep dives.
• Cisco Foundation AI Blogs – Covers AI safety, trends, and technical research.
• VulnCheck Blog / OWASP Top 10 for LLMs – Focuses on AI vulnerabilities, mitigation strategies, and secure development.
• Dark Reading (AI Security Articles) – Security industry news, trends, and breaches often explained through an AI lens.
• Stanford HAI (Human-Centered AI) Blog – Thought leadership on responsible AI development.
• AI Snake Oil – Academic blog by Arvind Narayanan and Sayash Kapoor on separating AI hype from real capability.
• Simon Willison – Writes regularly on AI and AI-related topics, and regularly creates new tools to make using AI easier.

YouTube

• Two Minute Papers – Breaks down AI research papers in approachable, concise videos.
• Computerphile – Explainers on AI concepts, algorithms, and real‑world applications.
• Lex Fridman Clips (Short AI Segments) – AI leaders discuss the latest advancements and security risks.
• Károly Zsolnai‑Fehér Deep Dives – In‑depth but friendly AI tech analyses.
• CyberWire (Video Briefings) – Cyber news roundups, sometimes covering AI security.
• Black Hat Conference Channel – Recorded talks on security + AI topics from industry experts.

Podcasts

• Smashing Security – Cybersecurity news and trends, often with a humorous take.
• Practical AI – Applications of AI and ML explained in simple terms.
• AI Today Podcast – Interviews with AI business leaders and practitioners about adoption and risk.
• CyberWire Daily – Daily security news including AI threat developments.
• Lex Fridman Podcast (Long Form) – AI and computer science conversations, with security implications discussed often.
• Security Now! (Steve Gibson) – Weekly breakdowns of cybersecurity issues, sometimes including AI-powered threats and tools.

Newsletters

• The Rundown.ai – Simple, frequent AI news updates.
• TLDR.tech – Short, daily digest of AI, security, and tech headlines.
• Import AI (Jack Clark) – Weekly deep dives into AI developments and their implications.
• Cybersecurity Dive – Industry-specific newsletter including AI‑security stories.
• AI Weekly – Curated AI industry news and research picks.

White Papers & Presentations

• SANS 2025 Threat Hunting Survey: Advancements in threat hunting amid AI and cloud challenges.
• SANS360 – Evolving Zero Trust: Harnessing the Power of AI (PDF available).
• MIT Sloan – Cybersecurity Management of AI Systems (PDF).
• OWASP Foundation – LLM Security & Privacy Guidelines (formal documentation).
• NIST AI Risk Management Framework (Draft & Final PDFs) – Standards-based approach to safe AI development.
• ENISA – Cybersecurity of AI and Standardisation (European Union Agency for Cybersecurity reports).
• Forrester – The State of Generative AI in Security (2024) (paid/enterprise).