Speaker 1: Welcome curious minds to another deep dive. Today, we're plunging
Speaker 1: into a truly electrifying topic, the rapidly evolving intersection of
Speaker 1: generative AI, specifically things like chat, GPT and the world
Speaker 1: of cybersecurity. Think of this as your shortcut really to
Speaker 1: getting properly informed in a field that's just moving incredibly fast.
Speaker 1: We want to give you not just facts, but maybe
Speaker 1: some surprising insights too. And our source material today it's
Speaker 1: pretty unique. We're using excerpts from the Chat GPT for
Speaker 1: Cybersecurity Cookbook by Clint o'dungan. And this isn't just theory,
Speaker 1: you know. It's packed with practical hands on recipes to
Speaker 1: really supercharge your cybersecurity skills. Our mission is to kind
Speaker 1: of distill the core wisdom from these pages exactly.
Speaker 2: Our mission here is to pull out the most important
Speaker 2: luggets of knowledge and insight from this source. We want
Speaker 2: to show you how these tools can give you real muscle,
Speaker 2: you know, in the fight against digital adversaries, help you
Speaker 2: shift from being reactive to well proactive.
Speaker 1: Yeah, and it's hard to overstate how important this shift
Speaker 1: is generative AI. It's being called a game chain for
Speaker 1: a reason. It's kind of shattering the old barriers to
Speaker 1: entry in cybersecurity. So what that means for you listening
Speaker 1: is that the field is becoming more democratized. It's nurturing
Speaker 1: this new generation of cyber mavens. And this isn't just
Speaker 1: tech talk. It's really about harnessing AI to anticipate threats,
Speaker 1: not just react to them, amplifying our strategic thinking, you know,
Speaker 1: fortifying our digital defenses.
Speaker 2: Okay, so let's unpack this. Let's start with the basics,
Speaker 2: the foundations. How do you actually interact with these powerful tools?
Speaker 2: When we talk about generative AI and large language models lllms,
Speaker 2: maybe quick refresher for some folks, we're talking about AI
Speaker 2: trained on just massive amounts of text data, right.
Speaker 1: That's right, massive amounts, and that lets them understand context,
Speaker 1: generate responses that sound well human, and even create new content.
Speaker 2: But what's the real insight there for someone in cybersecurity?
Speaker 2: What's the key takeaway?
Speaker 1: Well?
Speaker 2: I think what's truly transformative is just how accessible these
Speaker 2: incredibly powerful tools have become. The first step, sure is
Speaker 2: setting up a basic chat GPT account, easy enough, But
Speaker 2: the crucial leap, especially for cybersecurity pros, is getting that
Speaker 2: open AI API key. Ah, the API key, Yeah, because
Speaker 2: that key isn't just for chatting through the web interface.
Speaker 2: It's your gateway to deeper programmatic interaction. It's really essential
Speaker 2: for building customized and automated interactions. You can build a
Speaker 2: whole range of applications that plug chat GPT's intelligence right
Speaker 2: into your existing security workflows.
Speaker 1: So you're embedding the AI and not just.
Speaker 2: Talking to it precisely, you're embedding it.
Speaker 1: Okay, that makes a lot of sense. So you've got
Speaker 1: the API key, where do you start with prompting? It's
Speaker 1: problem more than just asking a simple question.
Speaker 2: I imagine you're right it can be, but it's also
Speaker 2: designed to be quite intuitive. Basic prompting works a lot
Speaker 2: like a natural conversation. You could ask it, for instance,
Speaker 2: generate a Python script to find my public IP address.
Speaker 2: The key thing to grasp I think is that chat
Speaker 2: GPT uses this conversation based approach. It remembers the history
Speaker 2: of your.
Speaker 3: Chat ah, so you can follow ups exactly.
Speaker 2: You can ask follow up questions, refine the request just
Speaker 2: like you're collaborating with a human expert, and it's incredibly
Speaker 2: versatile in how it responds. It can give you code snippets,
Speaker 2: formata tables really useful for structured cyber tasks.
Speaker 1: And I've heard you can get even more specific by
Speaker 1: like assigning rules to the AI.
Speaker 3: How does that help?
Speaker 2: Oh, that's a really powerful technique. It's a game changer
Speaker 2: for tailoring the responses. By applying jat GPT roles like
Speaker 2: asking it to act as an AICISO or maybe a
Speaker 2: penetration tester, you immediately get answers filtered through that specific expertise.
Speaker 2: That's vital when you need really specific, nuanced advice. And
Speaker 2: then you can go further, you know, enhancing output with
Speaker 2: templates or asking it to format output as a table
Speaker 2: that ensures the information comes back structured, clear organized essential
Speaker 2: for reports, incident response, documentation analysis. It cuts down your
Speaker 2: workloads significantly, directly addresses that need for clear organized info.
Speaker 1: That sounds incredibly useful for structuring information, but it does
Speaker 1: raise a question about limitations. We always hear about the
Speaker 1: knowledge cutoff. How does that affect things in a fast
Speaker 1: moving field like cyber That's.
Speaker 2: A very valid point. While it's incredibly powerful, it's core
Speaker 2: knowledge does have that cutoff. Date September twenty twenty one
Speaker 2: for the models discussed primarily in the book. So yeah,
Speaker 2: if you're asking about the very latest zero day exploit
Speaker 2: that dropped yesterday or real time thread Intel, it won't
Speaker 2: have that baked into its core training. The good news, though,
Speaker 2: is the book covers this, and we'll touch on it too.
Speaker 2: There are clever techniques to work around that. Limitation often
Speaker 2: involves integrating its capabilities with say web browsing features or
Speaker 2: feeling it external up to date data sources.
Speaker 1: And quickly when you're crafting those prompts, what about parameters
Speaker 1: like temperature or maximum length? How do those help fine
Speaker 1: tune things for security tasks?
Speaker 2: Ah? Yeah, those give you crucial control. Temperature, for example,
Speaker 2: affects the randomness or let's say, creativity of the response.
Speaker 2: So for generating team scenarios, you might want a higher
Speaker 2: temperature to get more diverse maybe unexpected attack ideas makes sense.
Speaker 2: But if you're generating, say a critical security policy, you
Speaker 2: dial the temperature way down. You want focused, deterministic, consistent
Speaker 2: output there right, predictable exactly, and maximum length is just
Speaker 2: what it sounds like. It controls how long the response is.
Speaker 2: Get a quick summary or a really comprehensive report lets
Speaker 2: you tailor the output. For the specific job.
Speaker 1: Okay, let's shift gears a bit. Let's really show people
Speaker 1: how this isn't just theoretical, how generative AI has concrete,
Speaker 1: practical applications that are fundamentally changing cybersecurity functions.
Speaker 2: Absolutely. Let's start with vulnerability assessment and threat analysis. The
Speaker 2: core insight here, I think is that AI can now
Speaker 2: help create comprehensive vulnerability assessment plans just by feeding it
Speaker 2: network and system details.
Speaker 3: So it speeds up the planning phase massively.
Speaker 2: Yeah, it accelerates that initial planning and helps ensure you're
Speaker 2: not missing obvious areas and connecting this to the bigger picture.
Speaker 2: It's incredibly Frameworks like the ATT and TK.
Speaker 3: Framework a minory Yeah, chat.
Speaker 2: GPT can generate detailed threat reports based on ATT and CK.
Speaker 2: It can identify potential tactics, techniques and procedures TTPs that
Speaker 2: adversaries might use against your specific setup. What this means
Speaker 2: is AI helps analysts connect the dots much faster, identify
Speaker 2: subtle patterns of adversary behavior that might take a human
Speaker 2: analyst much longer to spot, even helps with suggesting scanning strategies.
Speaker 1: That sounds like a huge time saver for analysts definitely.
Speaker 1: What about on the development side, secure software development? How
Speaker 1: does AI play a role there?
Speaker 2: Right, code analysis and secure development AI helps throw the
Speaker 2: entire secure software development life cycle the SSTLC. It can
Speaker 2: assist with things like security requirement generation right at the start,
Speaker 2: or generating secure coding guidelines tailored to your project. Okay,
Speaker 2: but where it gets really impactful is its ability to
Speaker 2: actually look at code and identify potential security vulnerabilities. And
Speaker 2: it can even generate customs script for security testing. So
Speaker 2: think of it like having a tireless, incredibly knowledgeable peer
Speaker 2: programmer constantly looking over your shoulder, helping you build security
Speaker 2: in from the start, not just tacking it on at
Speaker 2: the end.
Speaker 1: I can definitely see the value there baking it in early. Okay,
Speaker 1: what about the sometimes dreaded area of governance, risk and
Speaker 1: compliance GRC? Can AI actually help simplify that?
Speaker 2: It absolutely can make a dent there. For GRC chat
Speaker 2: GPT can generate a comprehensive cybersecurity policy for your organization.
Speaker 2: You feed it your specifics. It gives you a solid
Speaker 2: starting point, cuts down dramatically in the boiler platework.
Speaker 3: Not alone sounds useful it is.
Speaker 2: But more than that, it assists with cybersecurity standards compliance.
Speaker 2: It can help break down dense regulations like NIST or
Speaker 2: ISO twenty seven zerols ROLL one, and it helps in
Speaker 2: creating a risk assessment process, including helping with risk ranking
Speaker 2: and prioritization. The key insight AI can synthesize these vast
Speaker 2: amounts of regulatory texts and your own organizational data much
Speaker 2: faster than a person could, leading to more consistent, thorough
Speaker 2: compliance efforts.
Speaker 1: Okay, here's where I think it gets really interesting because
Speaker 1: it's not just about the technical stuff. Right. AI is
Speaker 1: also transforming the more human centric side of cyber especially training.
Speaker 1: Talk about security awareness and training.
Speaker 2: Right AI can develop security awareness training content that's much
Speaker 2: more tailored and adaptive than the old static modules. How so, well,
Speaker 2: we're talking about things like AI powered interactive email phishing training.
Speaker 2: Imagine simulations that are dynamically generated, personalized, much harder to
Speaker 2: spot than generic templates.
Speaker 3: Oh that's clever.
Speaker 2: Or think about chat GBT guided cybersecurity certification study. An
Speaker 2: AI tutor that adapts to your learning speed, focuses on
Speaker 2: your weak spots. And here's where it gets fun, gamifying it.
Speaker 2: The book mentions creating a who did it? Mystery game
Speaker 2: using AI. The real power the insight here is that
Speaker 2: AI can personalize these gamified experiences in real time where
Speaker 2: you're struggling adjust the difficulty. Makes training way more effective
Speaker 2: and honestly less of a chore.
Speaker 1: That's a much more engaging approach. So, if AI is
Speaker 1: this powerful for defense and education, how do we use
Speaker 1: it to sharpen our offensive skills ethically? Of course, for
Speaker 1: training and testing.
Speaker 2: Good question. For red teaming and penetration testing, AI is
Speaker 2: proving very useful. It can swiftly generate realistic red team
Speaker 2: scenarios using the minor at MTK framework. This helps create
Speaker 2: sophisticated attack simulations potentially more thorough or innovative than relying
Speaker 2: solely on human.
Speaker 3: Planning, so better practice scenarios exactly.
Speaker 2: And it's incredibly powerful for open source intelligence or ocent
Speaker 2: gathering info from social media public data, even things like
Speaker 2: analyzing job postings for clues about a company's tech stack
Speaker 2: or vulnerabilities. The surprising thing is how fast AI can
Speaker 2: correlate seemingly random bits of public info to build a
Speaker 2: detailed profile. It can automate things like Google dorking to
Speaker 2: find exposed data, and maybe the most fascinating part these
Speaker 2: GPT powered Kylie Linux terminals.
Speaker 1: What are those?
Speaker 2: They translate natural language which you type in plain English
Speaker 2: into complex Linux commands used in penetration testing. Seriously, this
Speaker 2: isn't just a convenience. It dramatically lowers the technical bar.
Speaker 2: It means people without deep command line expertise could potentially
Speaker 2: execute sophisticated steps both for ethical hacking and well defense too.
Speaker 2: It fundamentally shifts the skill set required.
Speaker 1: That's wow, that's a truly powerful demonstration on the offensive
Speaker 1: simulation side. Well, let's bring it back to defense. For
Speaker 1: most listeners, the key question is how this power strengthens
Speaker 1: our actual defenses In real time. Let's talk threat monitoring
Speaker 1: and detection.
Speaker 2: Okay AI assists significantly with threat intelligence analysis. It can
Speaker 2: quickly extract indicators of compromise IOCs, those digital fingerprints from
Speaker 2: threat reports and generate clear summaries or narratives about threats.
Speaker 2: But crucially, its application in real time log analysis is huge.
Speaker 2: Sifting through mountains of laws to flag meaningful alerts tend
Speaker 2: to do the noise exactly and specifically detecting advanced persistent
Speaker 2: threats apts using chat GPT for Windows systems. For example,
Speaker 2: it can analyze system behaviors described in logs. The key
Speaker 2: insight is AI speed and pattern recognition. It can see
Speaker 2: subtle anomalies across vast data sets much faster than humans.
Speaker 2: You can even use it for building custom thread detection
Speaker 2: rules I think IRA rules for malware detection. AI can
Speaker 2: help you craft those rules based on descriptions of malware
Speaker 2: behavior or threat.
Speaker 3: Intel, so it helps write the detectors it can.
Speaker 2: Yeah, makes your detection potentially much faster and more comprehensive.
Speaker 2: And it also aids in network traffic analysis and anomaly detection,
Speaker 2: using tools like PCP analyzers to spot unusual network flows.
Speaker 1: Okay, this cloud based AI is clearly incredibly powerful, but
Speaker 1: privacy sensitive data you might be thinking, what does this
Speaker 1: mean if I'm handling highly confidential information?
Speaker 2: A critical question, and that's where local AI models and
Speaker 2: frameworks come so important, end it as a vital alternative.
Speaker 2: The emphasis here is on open source lms, which allow
Speaker 2: for greater customization, scrutiny and understanding because you can actually
Speaker 2: see and modify the code right you control it exactly.
Speaker 2: So for privacy focused solutions, you can implement local AI
Speaker 2: models for cybersecurity analysis with LM Studio that lets you
Speaker 2: run powerful models right on your own hardware. There's also
Speaker 2: local threat hunting with open Interpreter, which runs code locally
Speaker 2: for analysis, and tools like shell GPT that enhance command
Speaker 2: line productivity without sending commands externally. But crucially, the book
Speaker 2: mentions reviewing IR plans with private GPT for one hundred
Speaker 2: percent privacy one percent privacy because private GPT processes everything locally,
Speaker 2: your sensitive incident response plans, your confidential documents. They never
Speaker 2: leave your secure environment to be processed by a third
Speaker 2: party cloud. That's absolutely critical for many organizations.
Speaker 1: So local models offer that control and privacy that's huge.
Speaker 1: Can you also tweak these local models, fine tune them
Speaker 1: for very specific cybersecurity jobs.
Speaker 2: Absolutely, and that's another fascinating aspect. Beyond just running existing
Speaker 2: models locally, you can perform fine tuning LMS for cybersecurity
Speaker 2: with hugging Face's auto train. This lets you take a
Speaker 2: base open source model and train it further on your
Speaker 2: own specific data, tailoring it precisely, tailoring models for highly
Speaker 2: specific cybersecurity tasks. Maybe it's recognizing a particular type of
Speaker 2: phishing email unique to your industry or analyzing proprietary log formats.
Speaker 2: It creates highly specialized AI tools designed just for your challenges.
Speaker 1: Okay, and looking beyond local models for a moment, what
Speaker 1: about the latest open AI features. They're always releasing new
Speaker 1: stuff that goes beyond just the basic chat interface. What's
Speaker 1: really making a difference now?
Speaker 2: They are moving fast for advanced capabilities. Think about analyzing
Speaker 2: network diagrams with open Eye's image viewer. You can upload
Speaker 2: a complex diagram and the AI helps interpret it quickly.
Speaker 1: That saves time just understanding the layout definitely.
Speaker 2: Then there's the ability to create custom GPTs for cybersekisary applications.
Speaker 2: The book gives the example of Phishguard for phishing detection,
Speaker 2: maybe integrated using Zapier, so you can build your own
Speaker 2: bespoke AI assistant for a specific perhaps repetitive security.
Speaker 3: Tasks take a little AI specialists kind of Yeah.
Speaker 2: And forgetting current info, there's monitoring cyber thread intelligence with
Speaker 2: web browsing, allowing the AI to access and summarize real
Speaker 2: time data from the web. Plus for really digging into
Speaker 2: data vulnerability, data analysis and visualization with chat GPK. Advanced
Speaker 2: data analysis is a game changer. It can process spreadsheets
Speaker 2: of vulnerability data, find trends, create charts.
Speaker 3: Wow, actual data analysis yes.
Speaker 2: And for the ultimate level of automation, building advanced cybersecurity
Speaker 2: assistance with OpenAI using their newer Assistance API. This allows
Speaker 2: for really complex multi step tasks generating files, running code
Speaker 2: snippets for analysis, creating visualizations, building truly powerful automated security workflows.
Speaker 1: But circling back to sensitivity, if you are using these
Speaker 1: powerful cloud features from open Ai for serious.
Speaker 2: Work, then it's absolutely critical to reiterate. For organizations dealing
Speaker 2: with any kind of confidential or sensitive data, using an
Speaker 2: open Ai Enterprise account is crucial.
Speaker 3: Why enterprise specifically because.
Speaker 2: That tier typically comes with guarantees that your input data
Speaker 2: is not utilized in open Ai model training that maintains
Speaker 2: the vital confidentiality and security you need when leveraging their
Speaker 2: cloud services for real work.
Speaker 1: Wow. Okay, we have covered an incredible amount of ground
Speaker 1: today for making cybersecurity education more accessible and frankly more engaging,
Speaker 1: all the way to automating really complex threat detection and response.
Speaker 1: It seems crystal clear that AI is seriously amplifying human
Speaker 1: capabilities here. It's not about replacement, is it not at all.
Speaker 1: It's about empowerment, making us more efficient, more precise, and yeah,
Speaker 1: more strategic in how we approach security.
Speaker 2: You've absolutely got it. I think the biggest takeaway really
Speaker 2: is that knowledge is most valuable when understood and applied.
Speaker 2: So I'd encourage everyone listening to really think about how
Speaker 2: these tools, whether it's local models for privacy, fine tuning
Speaker 2: for specifics, or custom GPTs for automation, could fit into
Speaker 2: your own work or even just your learning journey. There's
Speaker 2: just so much potential sitting there right at your fingertips,
Speaker 2: to genuinely transform how you approach digital safety.
Speaker 1: So maybe a final thought to leave you with as
Speaker 1: you consider your next steps, imagine not just responding to threats,
Speaker 1: but consistently being steps.
Speaker 3: Ahead of the adversary.
Speaker 1: How might integrating AI change your strategic approach? How could
Speaker 1: it help make safety the norm, not the exception in
Speaker 1: your digital world? Thank you so much for joining us
Speaker 1: on this deep dive into the intersection of AI and cybersecurity.
Speaker 1: It's a fascinating space.
Speaker 3: Keep exploring