Prática de Shadowing: Cybersecurity Architecture: Data Security - Aprenda a falar inglês com vídeo
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4 .35 million.
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9 .44 million.
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83%.
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What do those numbers have to do with data security?
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I'll tell you in a minute.
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Welcome back to the Cybersecurity Architecture series, where in the first few videos, we talked about principles, fundamentals, about the various domains of cybersecurity.
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We covered identity and access.
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We looked at the endpoint, the network, applications in the last video.
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Today, we're going to talk about data security.
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which is what these numbers have to do with.
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So the first one, 4 .35 million?
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Well, according to the Poneman Institute, that's the average cost of a data breach.
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worldwide.
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That's a lot of money.
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Every time somebody breaks in, that's what it's costing the organization on average.
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And by the way, that's taking out some of the really large numbers that might have skewed the average.
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So this is the baseline that we're looking at.
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$9 .44 million cost of a data breach in the U .S.
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twice as much if you're in the US if you get hit with this.
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And 83%, what does that have to do with anything?
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That is the number of organizations that have been hit by more than one data breach.
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So this is answering the question, why do we need to care about data security?
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Now we're going to talk about in the rest of the video about some of these aspects, about governance, about discovery, about protection, compliance detection, response.
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survey found we reduce the cost of a data breach for you.
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So stay tuned for that.
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First of all, we're going to cover governance and discovery.
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So think of this as a data security ecosystem, we're going to fill this thing out.
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These are the technologies that go into allowing us to secure our data.
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First of all, we have to have some sort of governance plan.
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This is our way of saying, this is what we want to do.
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If I don't define where the finish line is, I can't expect people to end up there.
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So first of all, we're going to have a policy, a data security policy.
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And in that, we're going to spell out what kinds of things are sensitive and what kinds of things aren't.
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We're going to spell out a classification criteria.
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that says this is keys to the kingdom and this is the lunchroom menu.
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Nobody needs to protect that.
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We don't really care.
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That sort of thing, we need to spell out that criteria.
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We need to also spell out what kinds of protections go with those different levels of sensitivity.
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So we may have confidential, unclassified.
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We may have super confidential, super top secret, super duper top secret, whatever your classification scheme is.
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But you need to spell out what those are.
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We can't expect the users in our company to protect the data if we haven't spelled out what those guidelines are.
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governance, policy, classification.
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Build a catalog that tells us this is where the data is.
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This is where the really sensitive stuff is.
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And you want to know where that is all the time.
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We're going to be updating that as well.
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And then build a resilience plan.
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that says, if we lose data, how do we recover?
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So we'll talk a little more about that later as well.
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So that's the first stage, is build the plan.
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Then we're going to move into discovery.
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Now I've said this is what I want to do to cover our data and protect it.
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Now let's go find where the data is.
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A couple of different places that we can look for data types.
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Databases, that's where we expect.
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That's going to be structured data in most cases.
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That's where we're going to see a lot of the keys to the kingdom, but don't overlook this other area.
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files, emails, spreadsheets, all kinds of other things that make up unstructured data.
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We need to be able to discover sensitive content in unstructured data as well.
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In other words, in places where we might not otherwise look.
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We expect the keys to the kingdom to be in the very sensitive database, but what if somebody makes a copy of that?
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What if somebody excerpts parts of it and emails it to someone?
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Now we've got sensitive data flying around a lot of other places.
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And I'd like to look on the network and see as this data is moving around as well.
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And maybe discover when there are certain types of issues that we could be running into.
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protection.
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So DLP is a technology that's important, that allows us to discover this stuff in real time on various systems,
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and as data is flowing across our networks.
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Okay, now we've covered the why of data security.
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These numbers down here should be sufficient motivation for you.
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How do we do governance and discovery, some of the general technologies there.
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Now let's talk about protection and compliance.
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I figured out how I need to secure things and where the stuff is, now how am I going to actually protect it?
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Well, encryption is a huge part of that.
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That is, I scramble the data so that only I can read it and the bad guys can't.
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And I need to be able to do that for data at rest, like sitting in a database for instance, or data at motion.
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Data that's in motion could be moving from one user to another, could be on a web server, and then it's being served up over across the internet.
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So there's, I wouldn't need to be able to keep the data secure all the time, at rest and in motion.
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If I'm going to be encrypting data, the other thing I need to be able to do a really good job of is managing the keys.
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If you lose the keys, you lose the data.
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So remember that we want to generate keys in a random way.
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If anyone can predict the way keys are generated, then all bets are off.
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They can read all your data.
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So it has to be done randomly and we need to be able to keep it.
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We also need to be able to keep up with the lifecycle of a key.
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That is, don't encrypt it once and forget it.
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It's not encrypt and forget.
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It's encrypt and then continuously follow the life cycle and re -encrypt at appropriate times.
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We have keys that we rotate in and we rotate out.
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Another aspect of all this that you need to be paying attention to is this notion of quantum safe crypto.
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Quantum computers are going to be able to break all of our existing cryptography in the next number of years.
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We don't know exactly when, but it's not that far off.
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And so we're going to need to keep an eye on this space right here
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so that we use new algorithms
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that will keep our data safe against the quantum threat
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that would be out there if someone had a quantum computer
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and started trying to crack all of our data that we had previously secured.
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Access control is another part of data security.
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Access control is also part of identity and access management, which was the first of the seven domains that we covered.
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But it relates here as well, because it doesn't matter how strong I've encrypted the information, if a user that has access to this,
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set their password to the word password, then it doesn't matter because they're getting in.
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If we don't have a good way of authenticating and authorizing users, then all of this strong crypto isn't going to matter.
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So access controls need to be there.
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Backup.
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If we face in particular a disaster recovery scenario or a ransomware type scenario, where someone says, I've got your data and I'm not giving it back.
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Well, the best defense against that is saying, guess what ransomware guy?
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I've also got a copy of my data and I'll just restore it and you can go pound sand.
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So this is the type of protection that we need, not only to keep the data from prying eyes,
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but also keep the data so that we have a level of resilience that I mentioned earlier.
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or disaster or things of that sort.
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Also, I need to be able to ensure that I'm complying to some of the industry regulations that might exist.
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The Generalized Data Protection Regulation in Europe, GDPR, we have in the US HIPAA for healthcare information.
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There are lots of regulations in lots of industries in lots of parts of the world.
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It's essentially, if you've got any information about somebody, there's a chance that you would be subject to one of these regulatory requirements.
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So I need to be able to report.
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on am I complying with that?
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Just as much I need to be able to report when I'm not complying.
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Because if I don't know that, then one of the costs that goes into these data breaches are the fines that go along with it.
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And with GDPR in particular, it's very substantial.
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And if you think because you're operating a company that's not in the in Europe, in the European Union, that you're exempt from this, think again.
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If you've got European citizen data, even if you don't operate there, you could potentially be subject to that check with your lawyers to find out.
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And then, a policy, I need an ability to retain records, but only as long as is necessary according to the law or other regulation.
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It really doesn't do us good to keep a lot of information and just store everything forever.
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So what we need to do though is the law will require that we store data for a certain period of time, and we want to store it for that long, and probably not longer, because it gets more expensive.
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And the longer we're holding this data, the longer we're holding a burden that if in fact, we could be held liable.
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So it's best not to have it in the first place if you don't really need it.
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Okay, now we've covered the first bunch of security protections that are needed about governance, about discovery, and things of that sort.
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Now we're going to take a look at two more.
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Detection and response.
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Security is about prevention, detection and response.
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These first ones are mostly about the prevention.
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This is about the detection and response.
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Detection.
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Now, what does that involve?
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Well, I need to be able to monitor my systems and see how data is being used.
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How is it moving around my organization?
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Who's using it under what conditions?
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And in fact, I also may want to use a technology called user behavior analytics
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that will monitor users and the way that they're looking at the data.
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If they're downloading normally a thousand files a day and suddenly they start downloading a million files in a day, then that could be suspicious.
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starts having a lot of interest in that data, then that could be an issue.
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If someone is doing something different than their peer group, then that could be an issue.
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So that's what user behavior analytics is looking for, those kind of cases.
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So we're trying to detect misuse and abuse of the data.
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And ultimately, I'm going to generate alerts
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that are going to go up to some console and someone then can go take an action on that.
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And the action is our response part.
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So one of the things I might do is open a case.
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and then assign that case to someone, and then they begin an investigation.
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We might guide all of those efforts with something called a dynamic playbook.
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A dynamic playbook would tell you, this is what just happened, now you need to do the following steps.
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And based upon the results of those steps, I'm going to specify other steps for you to follow.
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So that's the dynamic nature of it, as opposed to just a hard coded script that someone follows.
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This is more like a dynamic script that they can follow.
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But it leads people through and allows them to automate the recovery and orchestration and automation of these problems.
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What's the difference between those two?
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In a perfect world, I'd automate all of my responses, but we don't live in a perfect world.
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Because we see a lot of these things that are first of a kind, type of situations a first of a kind i can't automate
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because i don't know what i should have done
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because i've never seen it before orchestrate is what i have
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to do in a lot of these cases where basically i'm looking for certain things
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and i'm providing guidance
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but like an orchestra the conductor of the orchestra is conducting who's going to come in
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when do the trumpets come in when do the the saxophones come in this sort of thing they're going to
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basically direct all of this.
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And that's what we're going to do in our response.
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So think about those things.
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We're going to talk about these actually in more detail in our next two videos, where we're going to talk about security monitoring and security response.
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Okay, now we've done a quick flyover view of data security.
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We started off talking about governance, setting the plan and saying this is what we intend to do.
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And if we don't have that right, we can't expect to do the other parts right.
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Then we move into discovery, find out where all the data is that we need to apply those policies to.
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Then we're going to put those protections in place.
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Then we're going to check our compliance and see if in fact we're doing what we intended to do.
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We're going to look for anomalies.
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Then we're going to respond when we find those and feed that information back into our policy.
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So the whole thing then becomes this ecosystem of data security, and it involves a lot of different technologies.
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So that's in general how we want to do this.
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Now what I told you is if you would stay to the end, I'm going to tell you the top five things and you can see them here already.
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So no need for a drum roll.
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But According to the cost of data breach survey, these were the top five things that reduced the cost of a data breach.
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Number one, AI.
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Using artificial intelligence was top on the list.
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And in fact, we started using AI a good deal already in this detect phase.
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You'll hear more about that in the future videos as we cover that in the next one.
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But you can expect to see AI be infused in all of these different spaces.
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So look for that moving forward.
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DevSecOps, that was a big part of the discussion in the previous video on application security.
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If you missed that, make sure you go back and check that out.
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But that breaks down the walls between DevSec and ops organizations and put security in a shift left position.
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Incident response.
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We talked about that here.
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That's this response capability, and it's going to be the subject of our video two videos from now.
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So again, stay tuned for that.
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Cryptography, we've talked about that here in this particular space.
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And as I said, if you can't encrypt the data, you can't protect it.
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And then ultimately, employee training.
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Because at the end of the day, it's not all about the technology.
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There's an end user.
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And the user, the human, is almost always the weakest link in any security system. So don't.
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Ignore this part.
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If you do, it's at your own peril.
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Okay, there we go.
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Those are the top five things that you can do to reduce the cost of a data breach, to reduce the likelihood that it ever happens to you in the first place.
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Make sure you have a good plan for doing all of these things.
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All right, now in the next video we will cover monitoring and then the following video security response.
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So make sure you stay tuned to check those out.
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Like, subscribe and hit the notify button so you'll know and not miss any videos in the series.
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Sobre Esta Aula
Nesta aula, você irá praticar a compreensão auditiva e a pronúncia em inglês, com foco em conceitos importantes relacionados à segurança de dados e cibersegurança. Você aprenderá sobre a importância de criar políticas e planos de governança para proteger informações sensíveis. Essa prática não apenas aumentará seu vocabulário técnico, mas também ajudará com a estruturação de frases e a fluência em contextos relacionados à tecnologia.
Vocabulário e Frases-Chave
- Data breach - violação de dados
- Governance policy - política de governança
- Classification criteria - critérios de classificação
- Confidential - confidencial
- Discovery - descoberta
- Recovery plan - plano de recuperação
- Sensitive content - conteúdo sensível
- Unstructured data - dados não estruturados
Dicas de Prática
Para aproveitar ao máximo o aprendizado neste vídeo, considere as seguintes dicas de shadowing que podem ser muito úteis:
- Escute atentamente cada trecho do vídeo e repita imediatamente após a fala do apresentador, tentando imitar a entonação e o ritmo. Essa técnica ajudará você a melhorar a pronúncia em inglês.
- Se o vídeo for rápido, não hesite em pausar e voltar a partes que você não entendeu bem. A prática no shadowing site que você utiliza pode ser mais eficiente se você praticar partes mais lentas antes de avançar.
- Ao repetir, preste atenção também nas pausas e nas emoções que o apresentador expressa. Isso enriquecerá sua prática de conversação em inglês.
- Tente gravar sua própria voz enquanto faz o shadow speech e depois ouça para identificar áreas onde você pode melhorar, como entonação e fluência.
Com dedicação e prática regular, você poderá dominar o vocabulário técnico da cibersegurança e aumentar sua confiança na conversação em inglês!
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.