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<v Speaker 1>Welcome curious minds to another deep dive. Today, we're plunging

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<v Speaker 1>into a truly electrifying topic, the rapidly evolving intersection of

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<v Speaker 1>generative AI, specifically things like chat, GPT and the world

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<v Speaker 1>of cybersecurity. Think of this as your shortcut really to

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<v Speaker 1>getting properly informed in a field that's just moving incredibly fast.

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<v Speaker 1>We want to give you not just facts, but maybe

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<v Speaker 1>some surprising insights too. And our source material today it's

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<v Speaker 1>pretty unique. We're using excerpts from the Chat GPT for

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<v Speaker 1>Cybersecurity Cookbook by Clint o'dungan. And this isn't just theory,

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<v Speaker 1>you know. It's packed with practical hands on recipes to

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<v Speaker 1>really supercharge your cybersecurity skills. Our mission is to kind

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<v Speaker 1>of distill the core wisdom from these pages exactly.

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<v Speaker 2>Our mission here is to pull out the most important

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<v Speaker 2>luggets of knowledge and insight from this source. We want

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<v Speaker 2>to show you how these tools can give you real muscle,

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<v Speaker 2>you know, in the fight against digital adversaries, help you

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<v Speaker 2>shift from being reactive to well proactive.

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<v Speaker 1>Yeah, and it's hard to overstate how important this shift

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<v Speaker 1>is generative AI. It's being called a game chain for

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<v Speaker 1>a reason. It's kind of shattering the old barriers to

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<v Speaker 1>entry in cybersecurity. So what that means for you listening

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<v Speaker 1>is that the field is becoming more democratized. It's nurturing

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<v Speaker 1>this new generation of cyber mavens. And this isn't just

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<v Speaker 1>tech talk. It's really about harnessing AI to anticipate threats,

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<v Speaker 1>not just react to them, amplifying our strategic thinking, you know,

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<v Speaker 1>fortifying our digital defenses.

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<v Speaker 2>Okay, so let's unpack this. Let's start with the basics,

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<v Speaker 2>the foundations. How do you actually interact with these powerful tools?

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<v Speaker 2>When we talk about generative AI and large language models lllms,

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<v Speaker 2>maybe quick refresher for some folks, we're talking about AI

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<v Speaker 2>trained on just massive amounts of text data, right.

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<v Speaker 1>That's right, massive amounts, and that lets them understand context,

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<v Speaker 1>generate responses that sound well human, and even create new content.

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<v Speaker 2>But what's the real insight there for someone in cybersecurity?

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<v Speaker 2>What's the key takeaway?

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<v Speaker 1>Well?

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<v Speaker 2>I think what's truly transformative is just how accessible these

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<v Speaker 2>incredibly powerful tools have become. The first step, sure is

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<v Speaker 2>setting up a basic chat GPT account, easy enough, But

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<v Speaker 2>the crucial leap, especially for cybersecurity pros, is getting that

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<v Speaker 2>open AI API key. Ah, the API key, Yeah, because

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<v Speaker 2>that key isn't just for chatting through the web interface.

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<v Speaker 2>It's your gateway to deeper programmatic interaction. It's really essential

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<v Speaker 2>for building customized and automated interactions. You can build a

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<v Speaker 2>whole range of applications that plug chat GPT's intelligence right

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<v Speaker 2>into your existing security workflows.

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<v Speaker 1>So you're embedding the AI and not just.

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<v Speaker 2>Talking to it precisely, you're embedding it.

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<v Speaker 1>Okay, that makes a lot of sense. So you've got

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<v Speaker 1>the API key, where do you start with prompting? It's

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<v Speaker 1>problem more than just asking a simple question.

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<v Speaker 2>I imagine you're right it can be, but it's also

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<v Speaker 2>designed to be quite intuitive. Basic prompting works a lot

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<v Speaker 2>like a natural conversation. You could ask it, for instance,

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<v Speaker 2>generate a Python script to find my public IP address.

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<v Speaker 2>The key thing to grasp I think is that chat

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<v Speaker 2>GPT uses this conversation based approach. It remembers the history

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<v Speaker 2>of your.

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<v Speaker 3>Chat ah, so you can follow ups exactly.

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<v Speaker 2>You can ask follow up questions, refine the request just

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<v Speaker 2>like you're collaborating with a human expert, and it's incredibly

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<v Speaker 2>versatile in how it responds. It can give you code snippets,

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<v Speaker 2>formata tables really useful for structured cyber tasks.

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<v Speaker 1>And I've heard you can get even more specific by

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<v Speaker 1>like assigning rules to the AI.

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<v Speaker 3>How does that help?

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<v Speaker 2>Oh, that's a really powerful technique. It's a game changer

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<v Speaker 2>for tailoring the responses. By applying jat GPT roles like

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<v Speaker 2>asking it to act as an AICISO or maybe a

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<v Speaker 2>penetration tester, you immediately get answers filtered through that specific expertise.

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<v Speaker 2>That's vital when you need really specific, nuanced advice. And

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<v Speaker 2>then you can go further, you know, enhancing output with

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<v Speaker 2>templates or asking it to format output as a table

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<v Speaker 2>that ensures the information comes back structured, clear organized essential

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<v Speaker 2>for reports, incident response, documentation analysis. It cuts down your

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<v Speaker 2>workloads significantly, directly addresses that need for clear organized info.

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<v Speaker 1>That sounds incredibly useful for structuring information, but it does

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<v Speaker 1>raise a question about limitations. We always hear about the

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<v Speaker 1>knowledge cutoff. How does that affect things in a fast

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<v Speaker 1>moving field like cyber That's.

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<v Speaker 2>A very valid point. While it's incredibly powerful, it's core

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<v Speaker 2>knowledge does have that cutoff. Date September twenty twenty one

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<v Speaker 2>for the models discussed primarily in the book. So yeah,

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<v Speaker 2>if you're asking about the very latest zero day exploit

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<v Speaker 2>that dropped yesterday or real time thread Intel, it won't

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<v Speaker 2>have that baked into its core training. The good news, though,

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<v Speaker 2>is the book covers this, and we'll touch on it too.

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<v Speaker 2>There are clever techniques to work around that. Limitation often

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<v Speaker 2>involves integrating its capabilities with say web browsing features or

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<v Speaker 2>feeling it external up to date data sources.

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<v Speaker 1>And quickly when you're crafting those prompts, what about parameters

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<v Speaker 1>like temperature or maximum length? How do those help fine

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<v Speaker 1>tune things for security tasks?

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<v Speaker 2>Ah? Yeah, those give you crucial control. Temperature, for example,

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<v Speaker 2>affects the randomness or let's say, creativity of the response.

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<v Speaker 2>So for generating team scenarios, you might want a higher

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<v Speaker 2>temperature to get more diverse maybe unexpected attack ideas makes sense.

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<v Speaker 2>But if you're generating, say a critical security policy, you

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<v Speaker 2>dial the temperature way down. You want focused, deterministic, consistent

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<v Speaker 2>output there right, predictable exactly, and maximum length is just

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<v Speaker 2>what it sounds like. It controls how long the response is.

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<v Speaker 2>Get a quick summary or a really comprehensive report lets

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<v Speaker 2>you tailor the output. For the specific job.

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<v Speaker 1>Okay, let's shift gears a bit. Let's really show people

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<v Speaker 1>how this isn't just theoretical, how generative AI has concrete,

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<v Speaker 1>practical applications that are fundamentally changing cybersecurity functions.

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<v Speaker 2>Absolutely. Let's start with vulnerability assessment and threat analysis. The

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<v Speaker 2>core insight here, I think is that AI can now

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<v Speaker 2>help create comprehensive vulnerability assessment plans just by feeding it

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<v Speaker 2>network and system details.

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<v Speaker 3>So it speeds up the planning phase massively.

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<v Speaker 2>Yeah, it accelerates that initial planning and helps ensure you're

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<v Speaker 2>not missing obvious areas and connecting this to the bigger picture.

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<v Speaker 2>It's incredibly Frameworks like the ATT and TK.

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<v Speaker 3>Framework a minory Yeah, chat.

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<v Speaker 2>GPT can generate detailed threat reports based on ATT and CK.

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<v Speaker 2>It can identify potential tactics, techniques and procedures TTPs that

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<v Speaker 2>adversaries might use against your specific setup. What this means

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<v Speaker 2>is AI helps analysts connect the dots much faster, identify

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<v Speaker 2>subtle patterns of adversary behavior that might take a human

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<v Speaker 2>analyst much longer to spot, even helps with suggesting scanning strategies.

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<v Speaker 1>That sounds like a huge time saver for analysts definitely.

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<v Speaker 1>What about on the development side, secure software development? How

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<v Speaker 1>does AI play a role there?

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<v Speaker 2>Right, code analysis and secure development AI helps throw the

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<v Speaker 2>entire secure software development life cycle the SSTLC. It can

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<v Speaker 2>assist with things like security requirement generation right at the start,

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<v Speaker 2>or generating secure coding guidelines tailored to your project. Okay,

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<v Speaker 2>but where it gets really impactful is its ability to

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<v Speaker 2>actually look at code and identify potential security vulnerabilities. And

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<v Speaker 2>it can even generate customs script for security testing. So

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<v Speaker 2>think of it like having a tireless, incredibly knowledgeable peer

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<v Speaker 2>programmer constantly looking over your shoulder, helping you build security

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<v Speaker 2>in from the start, not just tacking it on at

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<v Speaker 2>the end.

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<v Speaker 1>I can definitely see the value there baking it in early. Okay,

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<v Speaker 1>what about the sometimes dreaded area of governance, risk and

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<v Speaker 1>compliance GRC? Can AI actually help simplify that?

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<v Speaker 2>It absolutely can make a dent there. For GRC chat

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<v Speaker 2>GPT can generate a comprehensive cybersecurity policy for your organization.

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<v Speaker 2>You feed it your specifics. It gives you a solid

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<v Speaker 2>starting point, cuts down dramatically in the boiler platework.

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<v Speaker 3>Not alone sounds useful it is.

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<v Speaker 2>But more than that, it assists with cybersecurity standards compliance.

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<v Speaker 2>It can help break down dense regulations like NIST or

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<v Speaker 2>ISO twenty seven zerols ROLL one, and it helps in

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<v Speaker 2>creating a risk assessment process, including helping with risk ranking

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<v Speaker 2>and prioritization. The key insight AI can synthesize these vast

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<v Speaker 2>amounts of regulatory texts and your own organizational data much

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<v Speaker 2>faster than a person could, leading to more consistent, thorough

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<v Speaker 2>compliance efforts.

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<v Speaker 1>Okay, here's where I think it gets really interesting because

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<v Speaker 1>it's not just about the technical stuff. Right. AI is

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<v Speaker 1>also transforming the more human centric side of cyber especially training.

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<v Speaker 1>Talk about security awareness and training.

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<v Speaker 2>Right AI can develop security awareness training content that's much

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<v Speaker 2>more tailored and adaptive than the old static modules. How so, well,

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<v Speaker 2>we're talking about things like AI powered interactive email phishing training.

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<v Speaker 2>Imagine simulations that are dynamically generated, personalized, much harder to

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<v Speaker 2>spot than generic templates.

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<v Speaker 3>Oh that's clever.

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<v Speaker 2>Or think about chat GBT guided cybersecurity certification study. An

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<v Speaker 2>AI tutor that adapts to your learning speed, focuses on

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<v Speaker 2>your weak spots. And here's where it gets fun, gamifying it.

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<v Speaker 2>The book mentions creating a who did it? Mystery game

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<v Speaker 2>using AI. The real power the insight here is that

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<v Speaker 2>AI can personalize these gamified experiences in real time where

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<v Speaker 2>you're struggling adjust the difficulty. Makes training way more effective

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<v Speaker 2>and honestly less of a chore.

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<v Speaker 1>That's a much more engaging approach. So, if AI is

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<v Speaker 1>this powerful for defense and education, how do we use

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<v Speaker 1>it to sharpen our offensive skills ethically? Of course, for

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<v Speaker 1>training and testing.

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<v Speaker 2>Good question. For red teaming and penetration testing, AI is

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<v Speaker 2>proving very useful. It can swiftly generate realistic red team

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<v Speaker 2>scenarios using the minor at MTK framework. This helps create

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<v Speaker 2>sophisticated attack simulations potentially more thorough or innovative than relying

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<v Speaker 2>solely on human.

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<v Speaker 3>Planning, so better practice scenarios exactly.

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<v Speaker 2>And it's incredibly powerful for open source intelligence or ocent

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<v Speaker 2>gathering info from social media public data, even things like

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<v Speaker 2>analyzing job postings for clues about a company's tech stack

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<v Speaker 2>or vulnerabilities. The surprising thing is how fast AI can

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<v Speaker 2>correlate seemingly random bits of public info to build a

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<v Speaker 2>detailed profile. It can automate things like Google dorking to

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<v Speaker 2>find exposed data, and maybe the most fascinating part these

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<v Speaker 2>GPT powered Kylie Linux terminals.

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<v Speaker 1>What are those?

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<v Speaker 2>They translate natural language which you type in plain English

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<v Speaker 2>into complex Linux commands used in penetration testing. Seriously, this

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<v Speaker 2>isn't just a convenience. It dramatically lowers the technical bar.

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<v Speaker 2>It means people without deep command line expertise could potentially

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<v Speaker 2>execute sophisticated steps both for ethical hacking and well defense too.

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<v Speaker 2>It fundamentally shifts the skill set required.

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<v Speaker 1>That's wow, that's a truly powerful demonstration on the offensive

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<v Speaker 1>simulation side. Well, let's bring it back to defense. For

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<v Speaker 1>most listeners, the key question is how this power strengthens

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<v Speaker 1>our actual defenses In real time. Let's talk threat monitoring

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<v Speaker 1>and detection.

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<v Speaker 2>Okay AI assists significantly with threat intelligence analysis. It can

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<v Speaker 2>quickly extract indicators of compromise IOCs, those digital fingerprints from

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<v Speaker 2>threat reports and generate clear summaries or narratives about threats.

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<v Speaker 2>But crucially, its application in real time log analysis is huge.

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<v Speaker 2>Sifting through mountains of laws to flag meaningful alerts tend

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<v Speaker 2>to do the noise exactly and specifically detecting advanced persistent

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<v Speaker 2>threats apts using chat GPT for Windows systems. For example,

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<v Speaker 2>it can analyze system behaviors described in logs. The key

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<v Speaker 2>insight is AI speed and pattern recognition. It can see

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<v Speaker 2>subtle anomalies across vast data sets much faster than humans.

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<v Speaker 2>You can even use it for building custom thread detection

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<v Speaker 2>rules I think IRA rules for malware detection. AI can

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<v Speaker 2>help you craft those rules based on descriptions of malware

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<v Speaker 2>behavior or threat.

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<v Speaker 3>Intel, so it helps write the detectors it can.

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<v Speaker 2>Yeah, makes your detection potentially much faster and more comprehensive.

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<v Speaker 2>And it also aids in network traffic analysis and anomaly detection,

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<v Speaker 2>using tools like PCP analyzers to spot unusual network flows.

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<v Speaker 1>Okay, this cloud based AI is clearly incredibly powerful, but

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<v Speaker 1>privacy sensitive data you might be thinking, what does this

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<v Speaker 1>mean if I'm handling highly confidential information?

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<v Speaker 2>A critical question, and that's where local AI models and

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<v Speaker 2>frameworks come so important, end it as a vital alternative.

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<v Speaker 2>The emphasis here is on open source lms, which allow

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<v Speaker 2>for greater customization, scrutiny and understanding because you can actually

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<v Speaker 2>see and modify the code right you control it exactly.

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<v Speaker 2>So for privacy focused solutions, you can implement local AI

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<v Speaker 2>models for cybersecurity analysis with LM Studio that lets you

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<v Speaker 2>run powerful models right on your own hardware. There's also

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<v Speaker 2>local threat hunting with open Interpreter, which runs code locally

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<v Speaker 2>for analysis, and tools like shell GPT that enhance command

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<v Speaker 2>line productivity without sending commands externally. But crucially, the book

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<v Speaker 2>mentions reviewing IR plans with private GPT for one hundred

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<v Speaker 2>percent privacy one percent privacy because private GPT processes everything locally,

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<v Speaker 2>your sensitive incident response plans, your confidential documents. They never

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<v Speaker 2>leave your secure environment to be processed by a third

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<v Speaker 2>party cloud. That's absolutely critical for many organizations.

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<v Speaker 1>So local models offer that control and privacy that's huge.

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<v Speaker 1>Can you also tweak these local models, fine tune them

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<v Speaker 1>for very specific cybersecurity jobs.

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<v Speaker 2>Absolutely, and that's another fascinating aspect. Beyond just running existing

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<v Speaker 2>models locally, you can perform fine tuning LMS for cybersecurity

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<v Speaker 2>with hugging Face's auto train. This lets you take a

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<v Speaker 2>base open source model and train it further on your

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<v Speaker 2>own specific data, tailoring it precisely, tailoring models for highly

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<v Speaker 2>specific cybersecurity tasks. Maybe it's recognizing a particular type of

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<v Speaker 2>phishing email unique to your industry or analyzing proprietary log formats.

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<v Speaker 2>It creates highly specialized AI tools designed just for your challenges.

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<v Speaker 1>Okay, and looking beyond local models for a moment, what

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<v Speaker 1>about the latest open AI features. They're always releasing new

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<v Speaker 1>stuff that goes beyond just the basic chat interface. What's

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<v Speaker 1>really making a difference now?

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<v Speaker 2>They are moving fast for advanced capabilities. Think about analyzing

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<v Speaker 2>network diagrams with open Eye's image viewer. You can upload

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<v Speaker 2>a complex diagram and the AI helps interpret it quickly.

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<v Speaker 1>That saves time just understanding the layout definitely.

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<v Speaker 2>Then there's the ability to create custom GPTs for cybersekisary applications.

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<v Speaker 2>The book gives the example of Phishguard for phishing detection,

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<v Speaker 2>maybe integrated using Zapier, so you can build your own

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<v Speaker 2>bespoke AI assistant for a specific perhaps repetitive security.

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<v Speaker 3>Tasks take a little AI specialists kind of Yeah.

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<v Speaker 2>And forgetting current info, there's monitoring cyber thread intelligence with

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<v Speaker 2>web browsing, allowing the AI to access and summarize real

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<v Speaker 2>time data from the web. Plus for really digging into

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<v Speaker 2>data vulnerability, data analysis and visualization with chat GPK. Advanced

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<v Speaker 2>data analysis is a game changer. It can process spreadsheets

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<v Speaker 2>of vulnerability data, find trends, create charts.

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<v Speaker 3>Wow, actual data analysis yes.

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<v Speaker 2>And for the ultimate level of automation, building advanced cybersecurity

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<v Speaker 2>assistance with OpenAI using their newer Assistance API. This allows

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<v Speaker 2>for really complex multi step tasks generating files, running code

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<v Speaker 2>snippets for analysis, creating visualizations, building truly powerful automated security workflows.

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<v Speaker 1>But circling back to sensitivity, if you are using these

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<v Speaker 1>powerful cloud features from open Ai for serious.

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<v Speaker 2>Work, then it's absolutely critical to reiterate. For organizations dealing

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<v Speaker 2>with any kind of confidential or sensitive data, using an

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<v Speaker 2>open Ai Enterprise account is crucial.

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<v Speaker 3>Why enterprise specifically because.

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<v Speaker 2>That tier typically comes with guarantees that your input data

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<v Speaker 2>is not utilized in open Ai model training that maintains

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<v Speaker 2>the vital confidentiality and security you need when leveraging their

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<v Speaker 2>cloud services for real work.

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<v Speaker 1>Wow. Okay, we have covered an incredible amount of ground

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<v Speaker 1>today for making cybersecurity education more accessible and frankly more engaging,

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<v Speaker 1>all the way to automating really complex threat detection and response.

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<v Speaker 1>It seems crystal clear that AI is seriously amplifying human

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<v Speaker 1>capabilities here. It's not about replacement, is it not at all.

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<v Speaker 1>It's about empowerment, making us more efficient, more precise, and yeah,

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<v Speaker 1>more strategic in how we approach security.

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<v Speaker 2>You've absolutely got it. I think the biggest takeaway really

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<v Speaker 2>is that knowledge is most valuable when understood and applied.

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<v Speaker 2>So I'd encourage everyone listening to really think about how

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<v Speaker 2>these tools, whether it's local models for privacy, fine tuning

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<v Speaker 2>for specifics, or custom GPTs for automation, could fit into

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<v Speaker 2>your own work or even just your learning journey. There's

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<v Speaker 2>just so much potential sitting there right at your fingertips,

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<v Speaker 2>to genuinely transform how you approach digital safety.

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<v Speaker 1>So maybe a final thought to leave you with as

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<v Speaker 1>you consider your next steps, imagine not just responding to threats,

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<v Speaker 1>but consistently being steps.

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<v Speaker 3>Ahead of the adversary.

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<v Speaker 1>How might integrating AI change your strategic approach? How could

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<v Speaker 1>it help make safety the norm, not the exception in

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<v Speaker 1>your digital world? Thank you so much for joining us

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<v Speaker 1>on this deep dive into the intersection of AI and cybersecurity.

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<v Speaker 1>It's a fascinating space.

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<v Speaker 3>Keep exploring