One of the biggest questions I get whenever I’m teaching anything focused on core skills—whether it’s network security, SOC analysis, or ecurity essentials—is whether any of these classes are even necessary anymore because of AI.

The assumption seems to be that AI is going to eliminate the need for foundational knowledge. People believe that AI will simply handle the technical details for us, allowing security professionals to skip over the basics and move directly to the advanced work.

Unfortunately, that assumption creates a sense of hopelessness.

Junior analysts who are trying to enter the field start wondering whether it’s worth studying at all. Companies looking to hire are hesitant because they’re not sure what the workforce will look like in a few years. Everyone is waiting to see where things land.

Context Is Everything

When it comes to core information security skills, I believe we need to recognize something important:

The fundamentals matter more today than they ever have.

The reason is context.

The more context and understanding you have about a technical problem, the more effective AI becomes. Artificial intelligence is not a replacement for expertise. It amplifies expertise.

If you put AI in the hands of someone who doesn’t understand networking, operating systems, or security fundamentals, the impact is often limited. They may get answers faster, but they have no way to evaluate whether those answers are correct.

Put the same tools in the hands of someone who understands TCP/IP, operating systems, network architecture, authentication, and how systems actually work, and AI becomes an incredible force multiplier.

As we move into the future, it will become even more important for security professionals to understand the basics. Those foundational skills provide the context necessary to ask better questions, recognize bad answers, and effectively leverage increasingly powerful AI tools.

What Chess Can Teach Us

I also think we can look at chess as a preview of where our industry is heading.

Chess is more popular today than it has ever been. There are more strong chess players than at any other point in history. At the same time, AI engines can defeat even the world’s best players without breaking a sweat.

So why hasn’t AI killed chess?

Because the chess community embraced it.

Players use AI engines like Stockfish to analyze games, identify mistakes, study openings, improve endgame strategy, and accelerate learning. Tournament organizers use AI to help detect cheating. Coaches use AI to provide insights that would have been impossible to deliver at scale just a few decades ago.

The result isn’t fewer chess players. In fact, it is the opposite. The other result is better chess players.

Where Cybersecurity Is Heading

I believe cybersecurity is heading down a very similar path.

AI will help us learn faster. It will help us identify patterns more quickly. It will help us automate repetitive work and explore technical problems at a scale that wasn’t previously possible.

But none of that eliminates the need for foundational knowledge.

In fact, it increases its importance.

The people who understand the fundamentals will be the ones who get the most value from AI. They’ll be able to guide it, challenge it, verify it, and use it to solve increasingly complex problems.

The future of cybersecurity isn’t humans versus AI.

It’s humans with strong fundamentals using AI to become dramatically more effective than ever before.

Check out John’s Antisyphon courses (including Pay-What-You-Can options!) here:

antisyphontraining.com/instructor/john-strand/

As AI becomes increasingly utilized throughout the security space, it's important to consider how it should be used most effectively. We've all seen companies adding AI into every offering and function they perform, often as much as possible.

In security operations, this creates a divide between two types of companies:

1. Companies that rely primarily on AI to do the work
2. Companies that enable their operators to become higher performers through the use of AI

Both types address a clear problem: volume. Anyone who has worked in a SOC knows that alert volume results in burnout, limits the number of customers you can support, and takes time away from more important tasks. Using AI to accelerate triage of high-volume, lower-complexity alerts is a practical and effective strategy. It frees analysts to focus on the ambiguous cases that require human judgment. With the right context and data, AI can meaningfully assist in distinguishing malicious from benign activity — but "most of the time" isn't good enough when a miss means a breach. AI augments the analyst; it doesn't replace their judgment.

That said, keeping a human in the loop is critical for the fringe cases: AI hallucination, highly complex technical investigations, and even situations where attackers attempt to fool the models with injection or evasion techniques.

Let's talk about some ways you can be the second type of company: the one that enables operators rather than replaces them.

AI for Triage Enhancement

Utilizing AI for enhancement of the triage process is something all security operation centers should be doing right now. While AI may occasionally be wrong or may hallucinate, anyone not taking advantage of it to work across the huge amount of data that SOCs process will fall behind their competitors.

I'm not suggesting that an AI model should be constantly closing alerts. Instead, consider this: Could an AI elevate alerts that would otherwise not have been investigated?

All SOCs process swathes of low-fidelity or low-severity alerts that are usually suppressed, deduplicated, or closed out through automation. What if you could use an AI model to review these alerts that would've been closed anyway and elevate those requiring human response to higher priorities? The worst-case scenario—a false negative—isn't applicable here. These alerts wouldn't have received much attention anyway. In the ideal scenario, you could intelligently review alerts your analysts wouldn't have looked at otherwise, escalating truly concerning activity for a human to look at.

AI for Quality Assurance

Another clear use case for AI in SOCs is quality assurance. Many QA checks on investigations are random, but what if you could curate a set of prompts that tasks AI with looking for common mistakes within your investigations and notifies a human to review them?

The AI could highlight the causes for concern, providing a starting point for the human analyst. It could even offer suggestions to both the reviewer and the analyst, making note of possible improvements. The higher quality the QA prompts, the better the feedback.

AI for Validation

Similar to the first use case mentioned, AI could validate analyst investigations and look for logical gaps or potential blind spots. Effectively, it could be used to provide top cover for analysts, giving them more space to triage quicker without chasing down every lead, knowing that the model (ideally trained on past cases!) is fact-checking and logically validating their investigation, freeing them to confidently triage without second-guessing themselves.

Human Augmentation

While AI excels at handling repetitive high-volume tasks, human expertise remains essential for complex investigations, fringe cases, and maintaining accountability. Rather than viewing AI as a cost-cutting tool to reduce headcount (or to take on many more customers with the same headcount), think of it as an investment that multiplies the effectiveness of your existing team. By strategically deploying AI to handle or fact-check routine work, you free your analysts to focus on the sophisticated threats that truly require human judgment and critical thinking.

If you take one thing from this article, I want it to be this:

Using AI to augment your operators may not show the same immediate "return" as replacing them, but, over time, it will increase your service quality tenfold.

What we built, Fusion AI, runs at about a third the cost of a traditional external pentest, a human tester still signs off on every finding, and it is not here to replace anybody.
We have been hearing that one a lot. So when Melisa from our Business Capture team sat down with Brian Fehrman and me for this episode of AI Security Ops, she started with, “What is this thing you built, and is it the same hype everyone else is selling?”

The post Everyone’s Selling AI That Kills Pentesting. We Built One That Doesn’t. appeared first on Black Hills Information Security, Inc..

The Model Context Protocol (MCP) is a proposed open standard that provides a two-way connection for AI-LLM applications to interact directly with external data sources. It is developed by Anthropic and aims to simplify AI integrations by reducing the need for custom code for each new system.

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In Part 2, we’re diving headfirst into one of the most critical attack surfaces in the LLM ecosystem - Prompt Injection: The AI version of talking your way past the bouncer.

The post Getting Started with AI Hacking Part 2: Prompt Injection appeared first on Black Hills Information Security, Inc..

In my journey to explore how I can use artificial intelligence to assist in penetration testing, I experimented with a security-focused chat bot created by Jason Haddix called Arcanum Cyber Security Bot (available on https://chatgpt.com/gpts). Jason engineered this bot to leverage up-to-date technical information related to application security and penetration testing.

The post Augmenting Penetration Testing Methodology with Artificial Intelligence – Part 3: Arcanum Cyber Security Bot appeared first on Black Hills Information Security, Inc..

A common use case for LLMs is rapid software development. One of the first ways I used AI in my penetration testing methodology was for payload generation.

The post Augmenting Penetration Testing Methodology with Artificial Intelligence – Part 2: Copilot appeared first on Black Hills Information Security, Inc..

Burpference is a Burp Suite plugin that takes requests and responses to and from in-scope web applications and sends them off to an LLM for inference. In the context of artificial intelligence, inference is taking a trained model, providing it with new information, and asking it to analyze this new information based on its training.

The post Augmenting Penetration Testing Methodology with Artificial Intelligence – Part 1: Burpference appeared first on Black Hills Information Security, Inc..

You may have read some of our previous blog posts on Artificial Intelligence (AI). We discussed things like using PyRIT to help automate attacks. We also covered the dangers of […]

The post Getting Started with AI Hacking: Part 1 appeared first on Black Hills Information Security, Inc..

RAG connects pre-trained LLMs with current data sources. Moreover, a RAG system can use many data sources.

The post Avoiding Dirty RAGs: Retrieval-Augmented Generation with Ollama and LangChain appeared first on Black Hills Information Security, Inc..

In this video, John Strand discusses the complexities and challenges of penetration testing, emphasizing that it goes beyond just finding and exploiting vulnerabilities.

The post 5 Things We Are Going to Continue to Ignore in 2025 appeared first on Black Hills Information Security, Inc..

This blog post is aimed at the intermediate level learner in the fields of data science and artificial intelligence. If you would like to read up on some fundamentals, here […]

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Many people have heard of ChatGPT, Gemini, Bart, Claude, Llama, or other artificial intelligence (AI) assistants at this point. These are all implementations of what are known as large language […]

The post Pitting AI Against AI: Using PyRIT to Assess Large Language Models (LLMs)  appeared first on Black Hills Information Security, Inc..

| Bronwen Aker // Sr. Technical Editor, M.S. Cybersecurity, GSEC, GCIH, GCFE Go online these days and you will see tons of articles, posts, Tweets, TikToks, and videos about how […]

The post Crafting the Perfect Prompt: Getting the Most Out of ChatGPT and Other LLMs appeared first on Black Hills Information Security, Inc..