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Speaker 1: T T.

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Speaker 2: I know, we already did an episode about scammers, The

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Speaker 2: Art of the Con.

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Speaker 3: Yes, we talked about what happened at Firefast. Firefast feels

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Speaker 3: like it was forever ago and yesterday.

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Speaker 2: Right, But you know what, I think it's gone all digital.

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Speaker 2: Between inventing Anna, the tender swindler, that couple that was

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Speaker 2: stealing bitcoin from people.

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Speaker 3: People are getting advanced in their scamming technology.

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Speaker 2: Yes, I've got to build up my digital fortress because

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Speaker 2: it's looking grim out here. Yes, I've been getting so

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Speaker 2: much digital spam that is actually scamming on the back end,

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Speaker 2: like links that say track your package here, even if

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Speaker 2: I haven't purchased any ash that has.

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Speaker 3: Been going around a lot. I've gotten text messages that

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Speaker 3: are like track your UPS package, which I might have

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Speaker 3: a UPS package on the way, and so you're tempted

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Speaker 3: to click it. But there's what is it?

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Speaker 2: Scamware, spyware, ransomware, phishing attacks.

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Speaker 3: I don't know any of those words.

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Speaker 2: You remember when everybody was getting emails about those gift

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Speaker 2: cards and people.

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Speaker 1: Were buying them.

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Speaker 3: People were buying gift cards and was not the gift.

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Speaker 1: Yeah, just out of control.

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Speaker 3: I'm TT and I'm Zakiyah and from Spotify. This is

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Speaker 3: Dope Labs. Welcome to Dope Labs, a weekly podcast that

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Speaker 3: mixes hardcore science, pop culture, and a healthy dose friendship.

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Speaker 2: This week, we're talking all about cybersecurity.

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Speaker 3: And with everything that's going on in Russia and Ukraine

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Speaker 3: right now, there's been a lot of talks about a

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Speaker 3: possible cyber attack.

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Speaker 2: We really wanted to know more about how people's online

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Speaker 2: data is collected and used, what happens during a cyber attack,

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Speaker 2: and what we can do to stay safe. Ready for

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Speaker 2: the recitation, Yes, let's get into the resitation.

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Speaker 1: So what do we know?

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Speaker 3: Well? I know that we are always online, whether it

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Speaker 3: be for work or social media. Every time my phone

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Speaker 3: tells me how long I've been, like the amount of

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Speaker 3: screen time I've had, and I go and check the details,

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Speaker 3: I'm like, hm, hmm. I spent a lot of time

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Speaker 3: on TikTok and Instagram, And sometimes I'm ashamed because it'll

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Speaker 3: be like, you were on there today for nine hours.

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Speaker 3: How is that possible? When I work eight hours.

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Speaker 2: That's nobody's business, all right, that's the first.

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Speaker 3: Thing, that's my business, that's my visiness.

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Speaker 2: We also know that there have been some pretty big

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Speaker 2: cybersecurity breaches in the news. You remember the Colonial pipeline attack.

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Speaker 2: You know, it's one thing to feel like it's the

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Speaker 2: matrix and people are moving the zeros and ones and

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Speaker 2: they're green and moving down the screen.

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Speaker 1: But the gas.

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Speaker 3: Honestly, like, how is gas even involved with this? Those

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Speaker 3: types of things just go way over my head because

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Speaker 3: every time I think of cyber attack, I just think

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Speaker 3: digital stuff.

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Speaker 2: What isn't it in the cloud? That gas is real?

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Speaker 3: And right now with everything that's going on, all the

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Speaker 3: tensions because Russia is invading Ukraine, the US banks are

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Speaker 3: preparing for a potential retaliatory cyber attack from Russia.

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Speaker 2: In the topic of cybersecurity, we're seeing that blow up.

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Speaker 2: Even in entertainment. So I already mentioned inventing Anna, and

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Speaker 2: we talked about Tender Swindler, but there's also the Matrix,

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Speaker 2: So the old one and the reboot and Enemy of

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Speaker 2: the State.

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Speaker 1: Too, Yes?

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Speaker 3: Is that the one with Will Smith?

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Speaker 1: Yes?

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Speaker 3: Yes? So what do we want to know?

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Speaker 2: I want to know what's being collected? What are y'all

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Speaker 2: taking off of my phone in my computer?

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Speaker 1: What are you doing with it?

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Speaker 3: Why do you want it?

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Speaker 2: Yes?

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Speaker 3: First and foremost, why I am very born. You want

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Speaker 3: to see all these silly pet videos? I don't know why.

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Speaker 2: And then what's legal versus what's illegal? What are people

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Speaker 2: allowed to do with our information? Whatever they're collecting? My

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Speaker 2: question is what is rants?

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Speaker 3: Where? Here are a lot of things like ransomware, spyware? Where? Where?

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Speaker 3: Where is all the stuff coming?

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Speaker 1: Where isn't the where? Yeah?

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Speaker 3: I don't understand any of it, and so I just

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Speaker 3: want like clear definitions for all of this stuff.

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Speaker 2: Yeah, and like you said, who wants our stuff? Are

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Speaker 2: we the common people? Are we the targets of these

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Speaker 2: types of cyber attacks? Or are they like high value

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Speaker 2: assets like on mission impossible? Is it for the common people?

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Speaker 3: Right? Because I feel like there's not enough money in

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Speaker 3: my bank account for y'all to want to hack it.

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Speaker 2: Yeah, I don't understand. But if I am a target

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Speaker 2: just in case, what do I need to do to

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Speaker 2: be safe?

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Speaker 1: You know?

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Speaker 2: Because Norton isn't out here anymore?

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Speaker 3: No, I have Norton on myself.

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Speaker 2: Nobody's here to save me. So what do I need

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Speaker 2: to do?

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Speaker 3: That's just out of how old we are.

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Speaker 1: That's all right, I'm okay with that.

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Speaker 3: Let's jump into the dissection.

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Speaker 2: Our guest for today's lap is Christina Morillo.

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Speaker 4: My name is Christina Marillo. I am based out of

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Speaker 4: New York City. I am in information security and technology

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Speaker 4: practitioner with about two decades of combined experience and everything

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Speaker 4: from enterprise technology, information security, cloud.

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Speaker 3: Christina recently published a book called ninety seven Things Every

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Speaker 3: Information Security Professional Should Know, and it's a practical guide

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Speaker 3: from a variety of information security experts.

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Speaker 2: So we ask Christina, what exactly is cybersecurity?

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Speaker 4: Cybersecurity is pretty much the practice of protecting systems, networks,

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Speaker 4: and programs from digital attacks. It's like the equivalent of

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Speaker 4: an intruder coming into your home and taking your stuff

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Speaker 4: right or accessing or touching your stuff.

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Speaker 3: I can't imagine somebody coming to my house and just

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Speaker 3: touching stuff. Oh that just seems gross.

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Speaker 2: That makes me feel sick, even when I think about

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Speaker 2: stuff that I'm looking at on my phone, things that

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Speaker 2: I'm saving. Like, I know, I share a lot of links,

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Speaker 2: but I don't want everybody all up in my stuff.

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Speaker 2: And I don't know about it exactly exactly. I don't

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Speaker 2: know about you, but I keep a lot of tabs

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Speaker 2: in bookmarks. What would you do if you lost all

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Speaker 2: of that, I.

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Speaker 3: Wouldn't even know where to begin. I think a problem

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Speaker 3: that I have now is that because I use my

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Speaker 3: phone and my finger on my computer to enter my passwords,

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Speaker 3: I don't remember my passwords anymore. I don't feel like

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Speaker 3: I have to. But I know it's important because if

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Speaker 3: all that stuff was wiped away, I would still need

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Speaker 3: to have access to all of my things, yes, like

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Speaker 3: my email and everything that requires a password, right, So

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Speaker 3: I would feel some type of way.

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Speaker 2: And so all of this, you know, quote unquote stuff, numbers, passwords,

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Speaker 2: all of these things, and also like browsing behavior and

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Speaker 2: what we like and don't like. It's really hard to

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Speaker 2: understand what's legally collected versus, you know, what's illegally collected.

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Speaker 2: So let's start with the legal collection of our stuff

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Speaker 2: on the Internet.

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Speaker 4: The reason why data is collected in the first place

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Speaker 4: is to help answer like everyday questions, right, evaluate outcomes,

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Speaker 4: and also make predictions about trends. So all of our

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Speaker 4: data is valuable.

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Speaker 2: So this changes how we've been thinking about it. T

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Speaker 2: t Absolutely, people want to know when we see a page,

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Speaker 2: what we clicked on, why we clicked on it, to

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Speaker 2: help them understand what's useful.

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Speaker 3: Yeah, like if you come to this website, what are

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Speaker 3: most people clicking on? So that it gives like a

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Speaker 3: lot of people information like what's most popular? What are

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Speaker 3: people interested in? Christina likes to think about data in

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Speaker 3: four categories. The first is personal data.

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Speaker 4: That's like any personally identifiable information like social security numbers

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Speaker 4: or your gender, even your IP address, your device ID. Right,

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Speaker 4: like stuff that can be tied back to you as

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Speaker 4: a human, as an individual.

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Speaker 2: And in addition to personal data, there's also engagement data.

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Speaker 4: So that's how customers interact with the business. So that

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Speaker 4: could be like social media apps, texts, navigating to different websites. Right,

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Speaker 4: everyone is now aware of the cookie pop up that

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Speaker 4: comes up, which is super annoying.

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Speaker 2: We see this all the time on websites. But what

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Speaker 2: exactly are cookies?

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Speaker 4: Cookies are pretty much little software programs that retain information.

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Speaker 4: As an example, they retain maybe your pre di for instance,

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Speaker 4: if you like your website icons to be larger when

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Speaker 4: you visit CNN dot com, if you always search for

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Speaker 4: a specific topic, you know, if you have favorites listed

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Speaker 4: on the site for any reason. Also, you're signing your

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Speaker 4: logging information This is like little pieces of software that

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Speaker 4: are downloaded onto your computer to retain your history.

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Speaker 3: The cookie pop up is something that depending I might

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Speaker 3: just say, fine, whatever, just accept, but sometimes I take

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Speaker 3: this time. I'm like, no, just only collect what is

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Speaker 3: necessary or whatever it says, like I switch all those

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Speaker 3: things off. But I think my default is just like except.

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Speaker 2: No manage my settings. One trust. I want those things

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Speaker 2: all off. I only want the bare minimum that they need.

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Speaker 3: They're taking advantage of the impatient folks. I'm like, move

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Speaker 3: this thing out the way, let me see this toothpaste.

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Speaker 4: Then there's like behavioral data, So that's transactional information, so

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Speaker 4: your purchase history, usage. You know, when you swipe that

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Speaker 4: credit card, you swipe that debit card, all of that

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Speaker 4: information is being stored the credit card to you. There

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Speaker 4: is the time, the location the store all of that information.

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Speaker 2: And last is attitudinal data.

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Speaker 4: That's more about consumer satisfaction. So that's like looking at

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Speaker 4: criteria to purchase an item or a service.

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Speaker 2: That's a lot of different kinds of data. I think

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Speaker 2: previously I would have thought it was all just one thing.

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Speaker 2: So we've hit personal data, engagement data, behavioral data, and

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Speaker 2: then attitudinal data.

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Speaker 3: Yeah, I think most people just think it's your passwords. Yeah,

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Speaker 3: that's all they're selling.

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Speaker 2: And so all of these things can be legally collected.

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Speaker 2: But where does it all go? That's my question?

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Speaker 3: Yeah, and how are companies using that data?

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Speaker 4: So once you have all of this information, all of

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Speaker 4: this information is like dumped into the terabytes and petabytes

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Speaker 4: on hard drives and databases.

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Speaker 3: Right from there, the data is organized into large data sets.

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Speaker 3: Then the companies hire data scientists to develop software to

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Speaker 3: analyze the data sets, and from this they can use

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Speaker 3: the data to discover patterns and make p dictions.

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Speaker 4: So they take all this data and then they can say, okay,

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Speaker 4: well we can predict that. You know, I don't know

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Speaker 4: the twenty twenty one Nissan is going to sell more

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Speaker 4: than any other vehicle in twenty twenty two.

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Speaker 2: So okay, if there's a lot of engagement around a

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Speaker 2: big collection or something that comes out, people can't predict.

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Speaker 2: This is going to make a big splash. People are

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Speaker 2: going to spend a lot of money here. People are

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Speaker 2: clicking here, there's this much interest. Maybe companies adjust once

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Speaker 2: they see how people are responding to things they put out.

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Speaker 3: Right, Yeah, and I can see how that can be

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Speaker 3: valuable to a business. You want to know where to

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Speaker 3: put your marketing dollars. So you're like, if I know

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Speaker 3: that everybody wants this twenty twenty one Nissan, I'm going

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Speaker 3: to put my marketing dollars behind that twenty twenty one

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Speaker 3: Nissan and not another car, because then it just be

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Speaker 3: wasting money. Or if I am, I'm just going to

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Speaker 3: like put more here and a little bit less there.

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Speaker 2: That makes a lot of sense. And this really highlights

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Speaker 2: what Christina told us is the motivation behind collecting this

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Speaker 2: data in the first place.

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Speaker 4: At the end of the day, though, it truly about

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Speaker 4: getting us to buy more or use more of something.

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Speaker 2: So, just like you said, TT companies are using this

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Speaker 2: for marketing. They're trying to create specific targeted marketing campaigns.

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Speaker 4: It's getting us to use more social media, getting us

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Speaker 4: to buy more at Target. Right, Like, for example, when

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Speaker 4: you go to Target and you go in to buy soap,

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Speaker 4: but you coincidentally walk out with like four hundred dollars

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Speaker 4: worth of stuff that you didn't really need. It's not

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Speaker 4: by coincidence.

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Speaker 3: Right. There have been so many times where I have

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Speaker 3: online shop and I'm like, I just need a plane

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Speaker 3: white blouse and next thing, I know, my car is

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Speaker 3: filled with fourteen items and I've got to figure out

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Speaker 3: what is going on and how did those items get

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Speaker 3: in there. I know I put them there, but it

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Speaker 3: just feels like everything is being put in front of

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Speaker 3: me in a very specific way that makes me want

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Speaker 3: to purchase.

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Speaker 2: Do you know how on some of the sites, it'll say, Okay,

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Speaker 2: you're looking at this white blouse, and other shoppers who

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Speaker 2: looked at this also looked at this tan belt with

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Speaker 2: this gold detail. Yes, I want that with my white glass.

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Speaker 3: You know, they also like I'm like, oh no, you

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Speaker 3: know I also like.

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Speaker 2: This, yes, And so all of that, I guess is

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Speaker 2: behavioral and attitudinal data that is being collected to inform

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Speaker 2: my shopping, to make me continue to shop. I'm reinforcing

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Speaker 2: the algorithm because you know, I'm adding it to my cart.

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Speaker 3: Always, always, always. All of this feels a little bit creepy, but.

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Speaker 1: It is legal and it is working. It is right.

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Speaker 2: I wanted to see that, and I think that's the

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Speaker 2: strangest part. Do you remember TT when we went to

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Speaker 2: we were in New York.

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Speaker 3: Yes, I know this story, and we.

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Speaker 2: Were shopping and I was really torn on these pink Adidas.

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Speaker 2: They were what Stella McCartney, Yes, I think they might

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Speaker 2: have been Stella McCartney. And they had this really interesting

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Speaker 2: gum soul that was pinkish reddish, but it was really

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Speaker 2: giving me all the vibes I needed. And we stood

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Speaker 2: there and I was like, Oh, I just bought an

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Speaker 2: orange Stella McCartney hat.

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Speaker 1: Instead.

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Speaker 2: We left and that shoe showed up everywhere. Yeah, and

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Speaker 2: any chance there was for me to get an ad

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Speaker 2: it showed up.

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Speaker 3: It also showed up for me. Yeah, I got Instagram

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Speaker 3: ads about that specific shoe, which let us know that

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Speaker 3: even like where you're standing within a store, Like some

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Speaker 3: of these businesses have worked with companies that let them

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Speaker 3: track where you're standing, how long you're standing there.

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Speaker 2: Bluetooth Wi Fi all even if you don't connect, you

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Speaker 2: still have device IDs, right, and that's your personal information.

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Speaker 2: And so this divitation kids, we know it likes this

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Speaker 2: type of stuff, Hugh.

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Speaker 1: And it's all legal.

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Speaker 4: Yes, it's all of these statistics, and it's all around

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Speaker 4: the data science and the psychology around it. It's usually

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Speaker 4: coordinated by like different demographics, right, it could be based

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Speaker 4: on age, sex, location, how much you make if you've

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Speaker 4: gotten your education.

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Speaker 3: And some companies don't just use data for their own

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Speaker 3: marketing purposes, they also package and sell the data to

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Speaker 3: different companies and organizations. Christina points to Facebook as one example.

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Speaker 4: They collect a bunch of information, including attitude and on data. Right,

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Speaker 4: they know of you're feeding, side, depressed, lonely, and they're

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Speaker 4: able to then serve up content that matches your mood,

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Speaker 4: your demographic, how much money you make, what you like,

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Speaker 4: et cetera.

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Speaker 1: Et cetera.

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Speaker 2: I'm not a big Facebook user, but I can say

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Speaker 2: on Instagram, yes, if something is happening and I like

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Speaker 2: one of those you know, text posts, and I'm like, hmm,

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Speaker 2: if wait and see, or if I can show you

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Speaker 2: better and I can tell you was a person when

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Speaker 2: I'm feeling spicy, and I like that. It serves me

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Speaker 2: up more content just like that, even if I'm not

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Speaker 2: following people exactly.

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Speaker 3: Look at your explore page. Your explore page is curated

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Speaker 3: based off of the posts that you like, and my

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Speaker 3: explore page does not look like your Explore page.

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Speaker 1: Right.

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Speaker 2: As a kid, yes, mine is all cooking videos, sassy quotes, and.

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Speaker 1: Animals.

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Speaker 3: Mine is like eyebrow babies acting silly I don't even

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Speaker 3: know travel blog stuff.

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Speaker 2: And now Instagram also has suggested posts in the feed,

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Speaker 2: so they're constantly giving us content so that we'll say yes,

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Speaker 2: I like it, no I don't. They're just trying to

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Speaker 2: learn what we like and don't like.

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Speaker 4: The more they learn about you, the better they can

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Speaker 4: serve you. Now, because Facebook collects all of this data,

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Speaker 4: that makes them extremely valuable. Because now if I'm another corporation,

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Speaker 4: let's say I'm Colgate and I want to sell you

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Speaker 4: more toothpaste, I need to know how you tick, why

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Speaker 4: you shop, how you feel a certain way, how I

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Speaker 4: can market to you as the consumer or to your demographic.

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Speaker 4: And so I would go to a company like Facebook

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Speaker 4: and I would say, Okay, can I buy this data set?

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Speaker 4: Can I pay a charge to access this data set?

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Speaker 4: Or can I pay a feed to market on your platform?

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Speaker 4: Because I want to sell more toothpaste and the demographic

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Speaker 4: I really want to sell to, you know, our top

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Speaker 4: users on Facebook. What a lot of people don't understand

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Speaker 4: is that when we do not pay for a service,

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Speaker 4: we are the product.

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Speaker 3: Zakia, you say this all the time.

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Speaker 2: Yes, if you're not paying for it, you're on the shelf, baby.

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Speaker 3: Okay, nothing in this life is free. You paying somehow.

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Speaker 4: We don't pay for Twitter, we don't pay for Facebook,

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Speaker 4: we don't pay for Google, we don't pay anything. What

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Speaker 4: we pay with is our data.

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Speaker 2: We don't pay cash, but we pay an information about

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Speaker 2: who we are, what we believe, what we're interested in,

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Speaker 2: what we will buy. All of those things, inform companies

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Speaker 2: and help them make money off of our clicks, off

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Speaker 2: what we choose to share. All of those things are

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Speaker 2: just embedded into the process. You can feel kind of helpless,

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Speaker 2: right because if you don't choose to let this information

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Speaker 2: be shared, you can't use the site. Do you remember

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Speaker 2: when there were all those emails coming out about companies

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Speaker 2: updating their privacy policy.

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Speaker 3: Yes, Oh my gosh, that was a wild time. I

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Speaker 3: completely forgot about that.

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Speaker 2: It was like everybody was sending emails at the same time.

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Speaker 2: I'm about what they would and would not collect and

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Speaker 2: all of this stuff. And that wasn't even tied to

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Speaker 2: the United States.

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Speaker 3: Right. In May twenty eighteen, the General Data Protection Regulation

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Speaker 3: or GDPR was put into effect. And this is an

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Speaker 3: Internet privacy and security law passed by the European Union.

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Speaker 3: But since the Internet is global and most websites process

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Speaker 3: data from EU citizens. It pretty much affected the entire Internet, and.

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Speaker 2: So what did that mean for our privacy? As users

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Speaker 2: of these websites.

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Speaker 4: People basically own their data. They have a right to

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Speaker 4: delete their data, to collect their data, to request that

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Speaker 4: a company delete their data. So because of that, there

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Speaker 4: has been a lot of legislation around privacy and more

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Speaker 4: discussions around privacy and security. So now companies have to

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Speaker 4: tell us that they're collecting and what they're collecting. It's

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Speaker 4: a great win for privacy. It's a terrible hit to useability.

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Speaker 4: It makes experience terrible because people rarely know what they're

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Speaker 4: clicking on. People also don't know what they're being asked

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Speaker 4: to click on.

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Speaker 2: Yes, it slows down the process where you're going from

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Speaker 2: site to site.

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Speaker 3: Yeah, Like I said, if I'm in a rush, I'm

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Speaker 3: just hitting accept because I just need to get to

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Speaker 3: what I'm looking for. And I'm like, I don't have

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Speaker 3: time for this. Yes, except the cookies. I love cookies,

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Speaker 3: It's perfect. I prefer chocolate chill and so I just

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Speaker 3: click accept and keep it moving. You know.

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Speaker 2: Another option is to use Duck Duck Go. That's a

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Speaker 2: browser that limits what can be collected about your browsing activity.

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Speaker 1: My mom actually put me onto Duck duck Go.

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Speaker 3: Really mm hmyh VICKI you want it.

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Speaker 4: I use a browser called Duck Duck Go. I use

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Speaker 4: it when I want to go navigate something and I

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Speaker 4: don't want to be trapped or I don't want any

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Speaker 4: potential malicious by whear or anything to be installed.

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Speaker 2: I think this helps us get a really solid understanding

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Speaker 2: of what all the various types of data are, how

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Speaker 2: it's being used by companies. I mean, it seems like

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Speaker 2: it's being used in aggregate, you know, so in groups

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Speaker 2: to understand trends and behavior and what people like. And

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Speaker 2: all this stuff is legal. But we're going to take

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Speaker 2: a break and when we come back, we're going to

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Speaker 2: talk about the things that are illegal. So what happens

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Speaker 2: when there's a data breach or a cyber attack, and

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Speaker 2: what we can do to keep our data protected.

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Speaker 3: We're back and we've been talking to expert Christina Morillo

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Speaker 3: about data privacy and how to have more control over

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Speaker 3: the data that's shared with companies online.

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Speaker 2: Next week, we're talking about organ transplants. We'll explore the

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Speaker 2: science behind donor matching with our guest expert, doctor Dave

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Speaker 2: Low from Thermo Fisher. Turns out it's a lot more

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Speaker 2: complicated than just matching blood types.

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Speaker 3: Now let's get back to the lab.

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Speaker 2: So, now that we know our data is being tracked

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Speaker 2: and analyzed all the time, are there protections in place

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Speaker 2: to keep our personal inform?

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Speaker 1: Say?

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Speaker 2: Yes, I understand that what I click on and what

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Speaker 2: you click on and all of our device IDs, all

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Speaker 2: that stuff is supposed to be analyzed in bulk in groups,

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Speaker 2: functionally making it anonymous. But is anonymized data a real

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Speaker 2: thing and is it actually keeping our information safe?

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Speaker 4: We ask Christina, Well, what it's opposed to mean is

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Speaker 4: that any information that is collected is stripped of any

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Speaker 4: personally identifiable information. If you're collecting my data, right, if

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Speaker 4: you're collecting my information, that means that you're going to

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Speaker 4: strip my social Security number, You're going to strip my

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Speaker 4: location data, any information that can be tied back to

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Speaker 4: me as a person, you're stripping it. And that's what

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Speaker 4: that means by anonymized, So you're only collecting information that

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Speaker 4: you really need. So it could be city state, things

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Speaker 4: that are very generic that can apply to a population,

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Speaker 4: versus things that are very specific to me as an individual. Now,

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Speaker 4: we don't really have a guarantee. There's really no assurance.

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Speaker 4: We basically have to trust that what companies say is

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Speaker 4: correct until there's a breach and they say, you know,

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Speaker 4: we really care about your security and care about your privacy. However,

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Speaker 4: you know your data has been exposed in this breach.

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Speaker 1: Yes, I don't want my data in the breach.

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Speaker 3: Yes, but it always seems like everybody's data is in

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Speaker 3: the breach. It always just blows my mind. There'll be

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Speaker 3: a security breach and I'm just like, but I literally

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Speaker 3: have never even used this before, So how is my

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Speaker 3: information there?

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Speaker 1: Yes?

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Speaker 2: And what exactly is the breach? Wikipedia defines a data

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00:21:30,119 --> 00:21:33,760
Speaker 2: breach as the intentional or unintentional release of secure or

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Speaker 2: private confidential information. But it feels wrong, right, It's not secure.

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Speaker 2: If somebody accessed it and it was released, it's not secure.

442
00:21:41,920 --> 00:21:44,840
Speaker 2: It's no longer private. Yeah, it definitely wasn't secure enough.

443
00:21:44,880 --> 00:21:46,760
Speaker 3: It's just like you know those diaries we used to

444
00:21:46,800 --> 00:21:49,160
Speaker 3: have when we were kids and had those bowlocks, and

445
00:21:49,200 --> 00:21:51,480
Speaker 3: we thought everybody's secure, but all you had to do

446
00:21:51,600 --> 00:21:53,879
Speaker 3: is just pull it and it would just pop right open.

447
00:21:54,200 --> 00:21:57,040
Speaker 3: That's what these companies are using to protect our data.

448
00:21:57,080 --> 00:21:59,760
Speaker 3: That's what it feels like. Them little pink locks ain't

449
00:21:59,800 --> 00:22:03,000
Speaker 3: doing nothing. And you know, it feels like these things

450
00:22:03,040 --> 00:22:04,879
Speaker 3: are happening more and more. So.

451
00:22:04,920 --> 00:22:08,399
Speaker 2: There was recently a report reviewing the data breaches in

452
00:22:08,440 --> 00:22:11,560
Speaker 2: the first six months of twenty nineteen, and that report

453
00:22:11,600 --> 00:22:16,440
Speaker 2: showed that there were thirty eight hundred publicly disclosed data breaches.

454
00:22:16,640 --> 00:22:18,600
Speaker 2: And do you want to know how many records were compromised?

455
00:22:18,760 --> 00:22:19,760
Speaker 3: I feel sick already.

456
00:22:20,160 --> 00:22:21,840
Speaker 1: Four point one billion.

457
00:22:21,960 --> 00:22:23,879
Speaker 2: We're gonna link this in the show notes billion with

458
00:22:23,920 --> 00:22:26,040
Speaker 2: a B with a B baby.

459
00:22:26,119 --> 00:22:28,639
Speaker 3: Oh my gosh, it's everybody.

460
00:22:29,200 --> 00:22:32,080
Speaker 2: And just to give you a sense of magnitude, three

461
00:22:32,119 --> 00:22:35,200
Speaker 2: point two billion of that four point one billion, three

462
00:22:35,200 --> 00:22:37,879
Speaker 2: point two billion of those records were exposed by just

463
00:22:38,040 --> 00:22:40,280
Speaker 2: eight breaches. Somebody has too much access.

464
00:22:43,080 --> 00:22:45,640
Speaker 3: That is crazy. Only eight breaches and you got three

465
00:22:45,640 --> 00:22:48,320
Speaker 3: point two billion of those records.

466
00:22:48,359 --> 00:22:51,520
Speaker 2: And the actual type of data that was exposed. It

467
00:22:51,560 --> 00:22:54,359
Speaker 2: had email addresses that was in seventy percent of the

468
00:22:54,400 --> 00:22:57,600
Speaker 2: data and passwords that was also in sixty five percent

469
00:22:57,600 --> 00:22:58,320
Speaker 2: of those breaches.

470
00:22:58,480 --> 00:22:59,760
Speaker 1: So we in trouble.

471
00:23:00,160 --> 00:23:01,480
Speaker 3: We in big trouble.

472
00:23:02,160 --> 00:23:05,240
Speaker 1: Yeah.

473
00:23:05,720 --> 00:23:08,000
Speaker 2: And so when we think about these data breaches. We

474
00:23:08,119 --> 00:23:11,280
Speaker 2: know that's not from a company wanting to know whether

475
00:23:11,320 --> 00:23:13,720
Speaker 2: I like a red top or a white top, right exactly.

476
00:23:13,800 --> 00:23:17,320
Speaker 2: This is something that's a little more malicious and is illegal.

477
00:23:17,480 --> 00:23:20,360
Speaker 3: We all know that data is valuable, especially for companies

478
00:23:20,359 --> 00:23:22,840
Speaker 3: who use data to inform their business strategies, and if

479
00:23:22,840 --> 00:23:25,520
Speaker 3: it's not properly protected, then there's a risk for a

480
00:23:25,520 --> 00:23:29,760
Speaker 3: cyber attack, such as a security breach or a ransomware attack.

481
00:23:30,000 --> 00:23:33,480
Speaker 3: But we wanted to know more about ransomware and the

482
00:23:33,520 --> 00:23:34,879
Speaker 3: people behind them.

483
00:23:34,800 --> 00:23:40,240
Speaker 4: Cyber criminals. They basically want access to your bank accounts, money, right,

484
00:23:40,359 --> 00:23:42,919
Speaker 4: or something of value that then they can sell on

485
00:23:43,000 --> 00:23:47,600
Speaker 4: the dark web. It's malicious software and the sole purpose

486
00:23:47,680 --> 00:23:50,879
Speaker 4: of it is to encrypt, which is lock rate, to

487
00:23:51,040 --> 00:23:52,760
Speaker 4: encrypt your files.

488
00:23:53,000 --> 00:23:55,120
Speaker 2: It's always about the money. It's always about what can

489
00:23:55,160 --> 00:23:58,800
Speaker 2: be sold. Cash rules everything around me. Yes, dollar dollar bill, y'all.

490
00:23:59,320 --> 00:24:02,119
Speaker 2: And it just seems that in the new digital age,

491
00:24:02,280 --> 00:24:03,439
Speaker 2: ransomware is the method.

492
00:24:03,720 --> 00:24:07,800
Speaker 4: So in mainencrypt your entire seed drive, so every file

493
00:24:07,840 --> 00:24:11,160
Speaker 4: you have on your computer is locked, right. Sometimes it'll

494
00:24:11,200 --> 00:24:13,760
Speaker 4: have a nice little lock Sometimes you'll see some kind

495
00:24:13,760 --> 00:24:17,359
Speaker 4: of like logo on your screen that says you've been ransomed.

496
00:24:17,480 --> 00:24:20,000
Speaker 3: Some of these things. You feel like it's obvious, like

497
00:24:20,080 --> 00:24:21,760
Speaker 3: I shouldn't click on this because you click on a

498
00:24:21,800 --> 00:24:24,320
Speaker 3: website and these things just start popping up and flash,

499
00:24:24,359 --> 00:24:25,960
Speaker 3: and it's like, if you click here, you get a

500
00:24:25,960 --> 00:24:28,320
Speaker 3: million dollars in a trip to Aruba. And you know,

501
00:24:28,560 --> 00:24:31,920
Speaker 3: random text messages from unknown numbers that have weird links.

502
00:24:31,960 --> 00:24:34,760
Speaker 3: I'll be getting a lot of those recently. Do you

503
00:24:34,800 --> 00:24:38,000
Speaker 3: remember the Facebook acts and when you get weird links

504
00:24:38,040 --> 00:24:40,879
Speaker 3: from like friends in your messenger and then if you

505
00:24:40,920 --> 00:24:43,760
Speaker 3: click the link, your profile sends it to all of

506
00:24:43,800 --> 00:24:44,800
Speaker 3: your friends.

507
00:24:44,560 --> 00:24:47,800
Speaker 2: Everybody in your contacts. And this was happening not just

508
00:24:47,840 --> 00:24:50,080
Speaker 2: on Facebook. You know, I used to dabble in Facebook.

509
00:24:50,119 --> 00:24:52,959
Speaker 2: I don't anymore, but this was happening on other platforms,

510
00:24:52,960 --> 00:24:54,879
Speaker 2: and people would say, oh, that wasn't me. If you

511
00:24:54,880 --> 00:24:57,199
Speaker 2: get a message from me, don't click the link, you know,

512
00:24:57,280 --> 00:24:57,919
Speaker 2: stuff like that.

513
00:24:58,200 --> 00:25:00,520
Speaker 3: Yeah, I got an Instagram DM from I'm a friend

514
00:25:00,520 --> 00:25:02,800
Speaker 3: that was saying knockoff Guji bags. You can get a

515
00:25:02,880 --> 00:25:06,200
Speaker 3: knockoff Gucci bag for twenty dollars. So I text him

516
00:25:06,400 --> 00:25:09,399
Speaker 3: and say, hey, you need to change your bat's word.

517
00:25:09,359 --> 00:25:11,000
Speaker 2: Yes, because you're selling Gucci bags.

518
00:25:11,000 --> 00:25:11,639
Speaker 1: I don't know if you know.

519
00:25:12,440 --> 00:25:16,800
Speaker 2: And it happens in this world of convenience where FedEx

520
00:25:16,880 --> 00:25:19,480
Speaker 2: is saying click here to check your tracking link, or

521
00:25:20,080 --> 00:25:23,040
Speaker 2: somebody else is saying, hey, want to see the latest

522
00:25:23,080 --> 00:25:26,080
Speaker 2: sales styles, or one in on this great travel deal,

523
00:25:26,119 --> 00:25:30,040
Speaker 2: and links are everywhere, emails, text messages, and everybody's using

524
00:25:30,080 --> 00:25:33,000
Speaker 2: these little shorthand bitley and all this stuff. All it

525
00:25:33,040 --> 00:25:35,160
Speaker 2: takes is one click, and it's so easy to do

526
00:25:35,240 --> 00:25:37,640
Speaker 2: if you let your guard down just a little bit.

527
00:25:38,280 --> 00:25:40,199
Speaker 4: So you click on a link it takes you to

528
00:25:40,800 --> 00:25:45,080
Speaker 4: a bad website that downloads malicious software, or you click

529
00:25:45,080 --> 00:25:49,520
Speaker 4: on an attachment via email that has malicious code attached

530
00:25:49,520 --> 00:25:53,760
Speaker 4: to it, which downloads that bad software onto your computer

531
00:25:54,200 --> 00:25:57,880
Speaker 4: and then does its thing, which is locking off your files.

532
00:25:58,280 --> 00:26:00,399
Speaker 3: What I would need to know is something like this

533
00:26:00,440 --> 00:26:02,879
Speaker 3: happened to me, is how do I get my things back?

534
00:26:03,000 --> 00:26:06,119
Speaker 3: I would like my things back? So after a cyber attack,

535
00:26:06,440 --> 00:26:07,760
Speaker 3: how do you get out of it?

536
00:26:07,840 --> 00:26:07,959
Speaker 1: Like?

537
00:26:08,000 --> 00:26:11,040
Speaker 3: How do you loose yourself from the grips of these

538
00:26:11,080 --> 00:26:13,280
Speaker 3: people who are attacking your software?

539
00:26:13,600 --> 00:26:17,840
Speaker 4: Pay the ransom right, which is risky because the criminal

540
00:26:17,920 --> 00:26:20,200
Speaker 4: or the attacker can decide not to provide the key.

541
00:26:20,320 --> 00:26:22,959
Speaker 4: They can decide, oh, you know, I got your money, sucker,

542
00:26:23,000 --> 00:26:25,080
Speaker 4: and I'm not going to give you back your data, right,

543
00:26:25,320 --> 00:26:27,960
Speaker 4: Or they can say, ah, you paid me, I don't

544
00:26:27,960 --> 00:26:30,880
Speaker 4: know one hundred and fifty million dollars, and so here

545
00:26:31,000 --> 00:26:33,720
Speaker 4: here's a key, and here's access to your data. Maybe

546
00:26:33,760 --> 00:26:35,960
Speaker 4: maybe not. You never know. There's no guarantee, right, it

547
00:26:35,960 --> 00:26:38,359
Speaker 4: doesn't come with a warranty. The other option is that

548
00:26:38,440 --> 00:26:42,639
Speaker 4: companies or individuals do not pay the ransom, and then

549
00:26:42,680 --> 00:26:45,200
Speaker 4: they do restore all their data from their backups because

550
00:26:45,200 --> 00:26:48,080
Speaker 4: they were smart and they did have backups. But the

551
00:26:48,160 --> 00:26:52,560
Speaker 4: risk there is that whatever information the attacker collected can

552
00:26:52,600 --> 00:26:54,760
Speaker 4: now be leaked and sold on the dark web.

553
00:26:55,000 --> 00:26:56,360
Speaker 3: I don't have one hundred and fifty million dollars.

554
00:26:56,359 --> 00:26:57,600
Speaker 1: I don't either, do you have a no?

555
00:26:57,760 --> 00:27:01,320
Speaker 3: Okay, that's what I didn't want to listen, But now

556
00:27:01,359 --> 00:27:02,480
Speaker 3: you know we don't got it.

557
00:27:02,640 --> 00:27:03,840
Speaker 1: You know how I live, and you know I do

558
00:27:03,880 --> 00:27:06,000
Speaker 1: not have a one hundred and fifty million dollars. I didn't

559
00:27:06,000 --> 00:27:08,320
Speaker 1: want one hundred fifty thousand either, okay.

560
00:27:09,119 --> 00:27:11,360
Speaker 2: And I think the other thing is if I had

561
00:27:11,359 --> 00:27:13,560
Speaker 2: to live off my last backup, I might as well

562
00:27:13,600 --> 00:27:14,040
Speaker 2: start over.

563
00:27:14,400 --> 00:27:17,679
Speaker 3: Yeah, just give me a brand new everything. Right before

564
00:27:18,160 --> 00:27:21,280
Speaker 3: my qualifying example, I had to write a portion of

565
00:27:21,280 --> 00:27:23,560
Speaker 3: my dissertation and I lost the whole thing because my

566
00:27:23,640 --> 00:27:25,719
Speaker 3: hard drive crash. I had an external hart drive, that

567
00:27:25,760 --> 00:27:29,680
Speaker 3: thing broke and I basically had to start all over again.

568
00:27:29,800 --> 00:27:33,000
Speaker 3: And that's when I started using the cloud. Yes, but

569
00:27:33,080 --> 00:27:35,720
Speaker 3: now even the cloud ain't safe because we've seen celebrities

570
00:27:35,760 --> 00:27:38,080
Speaker 3: clouds get hacked and you know, some of their racy

571
00:27:38,160 --> 00:27:41,600
Speaker 3: photos make it out to the common mess ye, And

572
00:27:41,680 --> 00:27:44,800
Speaker 3: so it ain't safe anywhere. And so the next thought

573
00:27:44,960 --> 00:27:48,520
Speaker 3: is how do we avoid these problems in the first place? Yes,

574
00:27:48,680 --> 00:27:50,760
Speaker 3: how do we get to a place where we are

575
00:27:51,200 --> 00:27:55,359
Speaker 3: aware of all these goings on and protect ourselves?

576
00:27:55,680 --> 00:27:58,560
Speaker 4: Cyber awareness is pretty much kind of like the education

577
00:27:58,680 --> 00:28:00,800
Speaker 4: piece of that, right, So it's like, what do we

578
00:28:00,800 --> 00:28:02,720
Speaker 4: need to do to protect these systems? What do we

579
00:28:02,720 --> 00:28:06,600
Speaker 4: need to do to protect our networks, our software applications?

580
00:28:06,720 --> 00:28:08,879
Speaker 4: What does that look like in real life?

581
00:28:08,960 --> 00:28:11,280
Speaker 2: And it's all about taking these small steps every day

582
00:28:11,320 --> 00:28:14,840
Speaker 2: to start building that awareness around our data to know

583
00:28:14,920 --> 00:28:17,520
Speaker 2: what's important, what you should be protecting. Things that don't

584
00:28:17,520 --> 00:28:20,040
Speaker 2: feel important to you may be useful to someone else.

585
00:28:20,280 --> 00:28:20,680
Speaker 3: Mm hmm.

586
00:28:20,920 --> 00:28:24,480
Speaker 4: Think about it like you're protecting yourself when you come

587
00:28:24,520 --> 00:28:27,320
Speaker 4: home at night, you lock your doors, right, just kind

588
00:28:27,320 --> 00:28:30,600
Speaker 4: of taking those small steps where make sure your passwords

589
00:28:30,640 --> 00:28:33,040
Speaker 4: and your accounts, your bank accounts are secure.

590
00:28:33,160 --> 00:28:35,359
Speaker 2: Christina gave us some really good advice. You know, don't

591
00:28:35,359 --> 00:28:36,200
Speaker 2: share your password.

592
00:28:36,320 --> 00:28:38,360
Speaker 3: But another thing is that you shouldn't use the same

593
00:28:38,360 --> 00:28:42,080
Speaker 3: password for everything. Now this, I know lots of people

594
00:28:42,120 --> 00:28:44,040
Speaker 3: have a Problemly, you know, you got a different password

595
00:28:44,040 --> 00:28:46,400
Speaker 3: for everything. How are you supposed to remember what it

596
00:28:46,440 --> 00:28:49,000
Speaker 3: is for each thing. Some things do have like a

597
00:28:49,120 --> 00:28:52,960
Speaker 3: multi factor authentication. I love that me too. They'll send

598
00:28:53,000 --> 00:28:55,360
Speaker 3: you a text message or an email that says, hey,

599
00:28:55,400 --> 00:28:57,040
Speaker 3: here's this code. Put it in so that we can

600
00:28:57,160 --> 00:28:59,200
Speaker 3: verify that it's you and not somebody else that's trying

601
00:28:59,240 --> 00:28:59,720
Speaker 3: to log into.

602
00:29:00,160 --> 00:29:03,600
Speaker 2: Just press yes, push it directly to my device. Christina

603
00:29:03,640 --> 00:29:06,000
Speaker 2: also told us not to play sketchy games.

604
00:29:06,280 --> 00:29:09,160
Speaker 3: Yeah, like those websites that let you play blackjack and

605
00:29:09,200 --> 00:29:10,200
Speaker 3: stuff like that.

606
00:29:10,120 --> 00:29:12,320
Speaker 2: Right, because some of those games are actually tied to

607
00:29:12,600 --> 00:29:15,800
Speaker 2: bank account information. So it's different if you're just accumulating

608
00:29:15,840 --> 00:29:18,080
Speaker 2: coins that are not real. But once you start tying

609
00:29:18,120 --> 00:29:21,040
Speaker 2: bank info, that feels a little dangerous. I know this

610
00:29:21,120 --> 00:29:22,840
Speaker 2: is gonna be hard for you friend, Yes, you know,

611
00:29:22,920 --> 00:29:23,440
Speaker 2: I like that.

612
00:29:24,440 --> 00:29:27,240
Speaker 3: And the last thing was don't give out personal information

613
00:29:27,360 --> 00:29:30,480
Speaker 3: like your address on social media because once it's online,

614
00:29:30,840 --> 00:29:33,320
Speaker 3: it's there forever. You might delete the tweet, delete the

615
00:29:33,320 --> 00:29:37,800
Speaker 3: Instagram post, those pixels exist in perpetuity for all time. Yeah,

616
00:29:37,840 --> 00:29:41,880
Speaker 3: so you have to be really, really careful about what

617
00:29:41,920 --> 00:29:43,480
Speaker 3: you're putting out on the internet.

618
00:29:43,680 --> 00:29:46,479
Speaker 2: All this is telling me is that we must remain

619
00:29:46,600 --> 00:29:50,480
Speaker 2: diligent when we are using these devices. Just think about

620
00:29:50,480 --> 00:29:53,000
Speaker 2: how easy it is to just zone out online or

621
00:29:53,040 --> 00:29:56,040
Speaker 2: on social media and just click click, mindleusly scrolling.

622
00:29:56,280 --> 00:29:59,640
Speaker 3: I have fallen asleep scrolling on Instagram to have almost

623
00:29:59,640 --> 00:30:01,760
Speaker 3: purchase something. I was like, why does it only take

624
00:30:01,800 --> 00:30:03,160
Speaker 3: three taps to purchase something?

625
00:30:03,360 --> 00:30:06,960
Speaker 2: It's so easy to almost retweet a thing around ust

626
00:30:07,040 --> 00:30:09,960
Speaker 2: click a link, and we could really compromise something in

627
00:30:10,000 --> 00:30:12,040
Speaker 2: our personal and because we're all working from home now

628
00:30:12,280 --> 00:30:13,160
Speaker 2: professional too.

629
00:30:13,760 --> 00:30:16,160
Speaker 1: In our personal and professional lives, we're spending.

630
00:30:15,880 --> 00:30:18,840
Speaker 3: A lot more time on the internet at home, so

631
00:30:18,840 --> 00:30:20,880
Speaker 3: we're feeling a little bit more relaxed, a little bit

632
00:30:20,920 --> 00:30:23,720
Speaker 3: more comfortable, so a lot more susceptible.

633
00:30:23,920 --> 00:30:28,160
Speaker 4: As employees, we basically have a professional responsibility to keep

634
00:30:28,200 --> 00:30:31,000
Speaker 4: the security and privacy of our company and customer data.

635
00:30:31,080 --> 00:30:34,960
Speaker 4: So whether it's our information or employer's information, we need

636
00:30:35,000 --> 00:30:37,600
Speaker 4: to make sure that we are doing the best and

637
00:30:37,680 --> 00:30:41,440
Speaker 4: everything that we can to ensure security. And also companies

638
00:30:41,480 --> 00:30:45,760
Speaker 4: have also a responsibility to provide that cyber security awareness, right,

639
00:30:45,840 --> 00:30:48,640
Speaker 4: talk about Hey, this is what we value. If you're

640
00:30:48,640 --> 00:30:51,720
Speaker 4: on your work computer, please refrain from using it for

641
00:30:51,760 --> 00:30:55,000
Speaker 4: your personal matters, et cetera. So it really depends on

642
00:30:55,040 --> 00:30:57,360
Speaker 4: the company. But I think education is a big part

643
00:30:57,360 --> 00:30:59,160
Speaker 4: of it because there's only so much that we can

644
00:30:59,160 --> 00:31:00,920
Speaker 4: do from a system point of view.

645
00:31:01,120 --> 00:31:03,520
Speaker 2: And like you said, you're also more relaxed when you're

646
00:31:03,560 --> 00:31:05,440
Speaker 2: working from home. It's easy to click on stuff when

647
00:31:05,440 --> 00:31:06,560
Speaker 2: I'm in my pajama pants.

648
00:31:06,840 --> 00:31:10,320
Speaker 3: Absolutely, I got my bondet on my at home glasses.

649
00:31:10,840 --> 00:31:13,000
Speaker 1: M different glasses for at home.

650
00:31:13,360 --> 00:31:15,960
Speaker 4: Yeah, you know, it really depends on what in the

651
00:31:16,000 --> 00:31:18,240
Speaker 4: security community we like to call your threat model.

652
00:31:18,360 --> 00:31:19,240
Speaker 3: That sounds scary.

653
00:31:19,400 --> 00:31:23,040
Speaker 2: Threat modeling sounds like now we're really in a spy movie.

654
00:31:23,200 --> 00:31:26,719
Speaker 2: Threat modeling is a process that identifies security vulnerabilities and

655
00:31:26,760 --> 00:31:28,680
Speaker 2: countermeasures to manage risks.

656
00:31:28,880 --> 00:31:30,560
Speaker 4: Who are you, what do you do for work, and

657
00:31:30,560 --> 00:31:32,480
Speaker 4: what are you trying to protect. If you're a diplomat,

658
00:31:32,560 --> 00:31:34,280
Speaker 4: what you need to protect is a little bit different

659
00:31:34,320 --> 00:31:38,000
Speaker 4: than let's say a mom or just a banker, a teller.

660
00:31:38,160 --> 00:31:40,960
Speaker 4: So it really depends on who you are and what

661
00:31:41,000 --> 00:31:42,960
Speaker 4: the risks are of you being attacked.

662
00:31:43,160 --> 00:31:45,400
Speaker 2: Listen, I may not be a diplomat, but that doesn't

663
00:31:45,400 --> 00:31:47,920
Speaker 2: mean I want people going all through my cookies and

664
00:31:47,960 --> 00:31:49,680
Speaker 2: I don't want my data to be up for grabs.

665
00:31:49,840 --> 00:31:52,080
Speaker 2: That's right, and Christina says one of the best ways

666
00:31:52,120 --> 00:31:53,959
Speaker 2: to protect your data is just to make sure you

667
00:31:54,000 --> 00:31:54,960
Speaker 2: have a backup.

668
00:31:54,720 --> 00:31:55,480
Speaker 3: Have a backup.

669
00:31:55,720 --> 00:31:58,600
Speaker 4: Always have a backup of your information, right, same as

670
00:31:58,680 --> 00:32:00,400
Speaker 4: when you have your credit cards in your life, you

671
00:32:00,400 --> 00:32:02,440
Speaker 4: should always like have a copy or something or at

672
00:32:02,520 --> 00:32:03,920
Speaker 4: least know what you have in your wallet so that

673
00:32:03,920 --> 00:32:05,840
Speaker 4: if your wallet gets laws, you can call in cancel.

674
00:32:05,920 --> 00:32:06,120
Speaker 1: Right.

675
00:32:06,200 --> 00:32:08,840
Speaker 3: Okay, So we have to back up everything on every device.

676
00:32:09,160 --> 00:32:11,880
Speaker 3: For a lot of us, that means our computers, our phones,

677
00:32:12,000 --> 00:32:15,120
Speaker 3: and any other device that we use every day. For companies,

678
00:32:15,160 --> 00:32:16,920
Speaker 3: that could end up being a lot of data that

679
00:32:16,960 --> 00:32:19,760
Speaker 3: needs to be backed up, depending on how many employees

680
00:32:19,800 --> 00:32:20,040
Speaker 3: they have.

681
00:32:20,680 --> 00:32:23,320
Speaker 4: They need to have a backup of their environment, have

682
00:32:23,360 --> 00:32:28,280
Speaker 4: a backup of their valuable data, their secret saws, their

683
00:32:28,320 --> 00:32:32,000
Speaker 4: customer information, whatever it is that makes a business run

684
00:32:32,040 --> 00:32:33,960
Speaker 4: or that allows a business to run, they should have

685
00:32:34,000 --> 00:32:38,240
Speaker 4: a backup. Unfortunately, many companies and many individuals do not

686
00:32:38,360 --> 00:32:40,600
Speaker 4: have a backup. So what that means is that if

687
00:32:40,600 --> 00:32:44,440
Speaker 4: they do get ransomed and their files are locked or encrypted,

688
00:32:44,960 --> 00:32:46,200
Speaker 4: then they cannot recover.

689
00:32:48,800 --> 00:32:50,640
Speaker 2: This has been eye opening for me. I feel like

690
00:32:50,680 --> 00:32:52,400
Speaker 2: I have a lot of apps I need to delete,

691
00:32:53,080 --> 00:32:55,440
Speaker 2: I have a lot of practices I need to change same,

692
00:32:55,600 --> 00:32:59,080
Speaker 2: and a lot of passwords that must get updated right away.

693
00:32:59,200 --> 00:33:00,959
Speaker 3: I'm really just going to take the time and go

694
00:33:01,000 --> 00:33:04,040
Speaker 3: through each thing and change my password. It's time. It's time.

695
00:33:04,160 --> 00:33:07,560
Speaker 3: It absolutely is time. And I think that the advice

696
00:33:07,800 --> 00:33:12,200
Speaker 3: that we got from Christina was really really important, and

697
00:33:12,480 --> 00:33:14,560
Speaker 3: I think that everybody should be trying to do that

698
00:33:14,600 --> 00:33:17,280
Speaker 3: so that we can, you know, fend off these attackers.

699
00:33:17,440 --> 00:33:19,880
Speaker 2: Yeah, you know, she said, the Internet is like the

700
00:33:19,920 --> 00:33:22,320
Speaker 2: deep sea or deep space, where we only know a

701
00:33:22,440 --> 00:33:24,760
Speaker 2: very small percentage of it. And let me tell you something.

702
00:33:24,960 --> 00:33:27,800
Speaker 2: I don't want to beat the dark web, Okay. I

703
00:33:27,800 --> 00:33:30,920
Speaker 2: want to stay right here in this galaxy, the lights web. Yes,

704
00:33:32,200 --> 00:33:34,600
Speaker 2: I want to step into the light Caroline, Okay, and

705
00:33:34,680 --> 00:33:38,040
Speaker 2: I do not want my information all out there in

706
00:33:38,080 --> 00:33:38,680
Speaker 2: those streets.

707
00:33:39,600 --> 00:33:41,240
Speaker 4: We just have to stay alert, and we have to

708
00:33:41,280 --> 00:33:45,200
Speaker 4: say vigilant to keep ourselves and our companies or just

709
00:33:45,280 --> 00:33:47,840
Speaker 4: personal information safe as safe as possible.

710
00:34:00,240 --> 00:34:03,120
Speaker 3: All Right, it's time for one thing. What's your one

711
00:34:03,120 --> 00:34:03,680
Speaker 3: thing this week?

712
00:34:03,760 --> 00:34:07,400
Speaker 2: Ze, I hope my one thing is our thing. Yes,

713
00:34:07,680 --> 00:34:10,919
Speaker 2: this is an event called Dope Labs Unplugged and it's

714
00:34:10,920 --> 00:34:14,480
Speaker 2: with the Boston Museum of Science on Thursday, April fourteenth.

715
00:34:14,719 --> 00:34:17,759
Speaker 2: Guess what, It's free ninety nine. So all you have

716
00:34:17,800 --> 00:34:19,960
Speaker 2: to do is register and you can join us in

717
00:34:20,000 --> 00:34:23,839
Speaker 2: person in Boston April fourteenth at seven pm. We're going

718
00:34:23,880 --> 00:34:26,719
Speaker 2: to have a live show. Come get these laughs and

719
00:34:26,760 --> 00:34:28,200
Speaker 2: these facts in person.

720
00:34:28,239 --> 00:34:30,040
Speaker 3: We want to see y'all faces. Come.

721
00:34:38,440 --> 00:34:41,080
Speaker 2: That's it for Lab fifty five. I hope that after

722
00:34:41,160 --> 00:34:44,200
Speaker 2: listening to this episode, you're gonna do something a little

723
00:34:44,239 --> 00:34:46,759
Speaker 2: bit different. Maybe change those settings on your phone. Call

724
00:34:46,840 --> 00:34:49,719
Speaker 2: us at two zero two five six seven seven zero

725
00:34:49,719 --> 00:34:51,560
Speaker 2: two eight and tell us what you thought, or give

726
00:34:51,640 --> 00:34:53,280
Speaker 2: us an idea for a lab you think we should

727
00:34:53,280 --> 00:34:55,560
Speaker 2: do this semester. We like hearing from you, so call us.

728
00:34:55,600 --> 00:34:58,279
Speaker 2: That's at two zero two five six seven seven zero

729
00:34:58,280 --> 00:35:00,960
Speaker 2: two eight and don't forget that there's so much more

730
00:35:01,000 --> 00:35:03,120
Speaker 2: for you to dig into on our website. There will

731
00:35:03,160 --> 00:35:05,640
Speaker 2: be a cheat cheat there for today's lab and additional

732
00:35:05,680 --> 00:35:08,319
Speaker 2: links and resources in the show notes. Plus, you can

733
00:35:08,360 --> 00:35:10,399
Speaker 2: sign up for our newsletter, so check it out at

734
00:35:10,440 --> 00:35:14,760
Speaker 2: Dope labspodcast dot com. Special thanks to today's guest expert,

735
00:35:14,880 --> 00:35:17,880
Speaker 2: Christina Morillo. You can find and follow her on Twitter

736
00:35:17,920 --> 00:35:22,040
Speaker 2: and Instagram at Divine Techygirl, d I v I n

737
00:35:22,120 --> 00:35:25,680
Speaker 2: E t E c h y g i r L.

738
00:35:26,239 --> 00:35:28,719
Speaker 3: You can find us on Twitter and Instagram at Dope

739
00:35:28,800 --> 00:35:30,080
Speaker 3: Labs Podcast.

740
00:35:29,719 --> 00:35:32,680
Speaker 2: And TT's on Twitter and Instagram at d R Underscore

741
00:35:32,760 --> 00:35:33,600
Speaker 2: T s h O.

742
00:35:34,000 --> 00:35:37,040
Speaker 3: And you can find Zakia on Twitter and Instagram at

743
00:35:37,160 --> 00:35:41,200
Speaker 3: z said So. Dope Labs is a Spotify original production

744
00:35:41,360 --> 00:35:44,960
Speaker 3: from Mega Ownmedia Group. Our producers are Jenny Ratlickmast and

745
00:35:45,040 --> 00:35:48,879
Speaker 3: Lydia Smith of Wavebrunner Studios. Our associate producer from Mega

746
00:35:48,920 --> 00:35:54,120
Speaker 3: Ohmedia is Brianna Garrett. Editing in sound design by Rob Smerciak.

747
00:35:53,719 --> 00:35:55,320
Speaker 2: Mixing by Hannes Brown.

748
00:35:55,600 --> 00:35:59,440
Speaker 3: Original music composed and produced by Taka Yasuzawa and Alex

749
00:35:59,520 --> 00:36:05,440
Speaker 3: Sugu from Spotify Creative producers Candice Manriquez Wren and Corin Gilliard.

750
00:36:05,760 --> 00:36:10,239
Speaker 3: Special thanks to Shirley ramos Yasmin of Fifi, camu Elolia,

751
00:36:10,719 --> 00:36:12,600
Speaker 3: Till krat Key and Brian.

752
00:36:12,320 --> 00:36:15,920
Speaker 2: Marquis, executive producers from Mega OH Media Group, r us

753
00:36:16,040 --> 00:36:31,000
Speaker 2: T T Show Dia and Zakiah Wattley