Data Security

Data breaches you must prepare for before 2023 
Ayesha Saleem
| December 7, 2022

Data breaches can immediately impact hundreds of millions or possibly billions of individuals in the data-driven world today. Data breaches have grown in scope along with digital transformation as attackers take advantage of our everyday reliance on data.  

Although it is impossible to predict how big cyberattacks may get in the future, this list of the biggest data breaches from the twenty-first century shows that they have already gotten ridiculously huge. 

types of Data breaches 2023 
Data breaches you must prepare for before 2023

Examples of data breaches and their impact 

Data breaches can be far more than a temporary terror. They may change the course of your life or your business. Businesses, governments, and individuals alike can experience huge complications from having sensitive information exposed. Whether you are offline or online, hackers can get to you through the internet, Bluetooth, text messages, or the online services that you use. 

Intentional attacks can occasionally be linked to the causes of data leaks. It may, however, also be the result of a person’s carelessness or weaknesses in the infrastructure of a business. 

Here is an example of a data breach: 

  • An insider by accident. An illustration would be a worker viewing files on a coworker’s computer without the necessary authorization permissions. There is no information disclosed and the access is accidental. However, the data is regarded as compromised because it was read by an uninvited party. 
  • Lost or stolen devices. An unencrypted and unlocked laptop or external hard drive — anything that contains sensitive information — goes missing. 
  • Malicious outside criminals. “These are hackers who use various attack vectors to gather information from a network or an individual.”  
  • A malicious insider: With the intention of hurting a person or business, this person willfully accesses and/or discloses data. “The malicious insider may have legitimate authorization to use the data, but the intent is to use the information in nefarious ways.” (“What is a Data Breach & How to Prevent One – Kaspersky”) 

 

Listen to the challenges of Data Analysis in cyber security

 

Most common types of data breaches in 21st century 

1. SQL Injection attack 

Similar to XSS assaults, a SQL Injection attack inserts malicious code into the system, but often SQL rather than HTML. In a SQL Injection attack, malicious code from the attacker accesses a system via a database.  

Although a lot of online apps employ databases as trustworthy third parties, it is crucial to make sure that no unauthorized parties are receiving data. Anyone with access to the database can steal information or carry out a SQL Injection attack to steal cash or other assets. 

2. Ransomware 

An unexpected notification informing you that your computer or phone has been compromised is known as ransomware. In this situation, the individual will inform you that if you pay a charge, they will provide it to you in exchange for keeping it private. This might range from being negligible to costing hundreds of thousands of dollars. 

In order to prevent the leak or deletion of crucial or compromising materials, many businesses work with risk management solution providers. 

3. XSS attack 

Web applications that exploit common vulnerabilities like XSS vulnerabilities may be exposed to cross-site scripting (XSS) attacks, a remote code execution (RCE) issue. When a remote attacker transmits malicious code to a web application, RCE problems can develop. 

Because the malicious code switches the web app’s execution to a script that could be anywhere on a computer network or even on other computers in the network, XSS vulnerabilities are usually simple to identify. 

4. MITM assault 

A man-in-the-middle (MITM) attack occurs when a perpetrator intercepts and steals data from a computer or network while posing as a third party. An attacker could, for instance, be present at a workplace, a hotel room, or a storage location when data is being seen by a computer user.  

The most frequent risk of MITM attacks is the public disclosure of sensitive data such as names, email addresses, dates of birth, financial information, health information, and other data. 

5. Digital breach 

A system or network environment where cardholder data is processed, stored, or sent is subject to an electronic breach if someone gains unauthorized access to it or deliberately attacks it. 

This could happen because of application-level assaults finding a system’s weaknesses via web servers or webpages. 

For instance, the healthcare sector is frequently impacted by data breaches since access to these networks exposes a lot of confidential patient credit card information. 

Major data breaches across the world

Some of the major data breaches that took place across the world 

Recently, major corporations have suffered data loss due to hacking and incidents of data breaches. Some of the most renowned of these are: 

1. Canva:

The namesake graphic design service, Canva, a Sydney-based business, was breached 2019, according to ZDNet. According to the hacker who alerted ZDNet, the incident resulted in the theft of data for almost 139 million customers. The compromise was caused by a hacker using the alias GnosticPlayers online. The hacker is well-known. He/she/they have been selling the 932 million user records that they acquired from 44 international businesses since 2019 February on the dark web. 

2. Shanghai National Police (SHGA):

The Shanghai National Police (SHGA) database was infiltrated in 2022. Major crucial data and information about billions of Chinese residents are contained in this database. The lot was being sold by HackerDan for 10 Bitcoin. 

3. Facebook:

Several online vulnerable databases were found to include 419 million records of Facebook users, dealing a fatal blow to over 20% of Facebook’s 2.3 billion users, according to TechCrunch. The records were kept on a server and were not password-protected, so anyone with an internet service could freely retrieve them. 

4. Microsoft:

250 million Microsoft customer records from 14 years ago have been exposed online without data encryption, according to a recent investigation. Recently, Microsoft has primarily been featured for different purposes in the headlines. Despite being frequently exploited, Microsoft has not released a patch for the Internet Explorer zero-day vulnerability. 

5. LinkedIn:

In June 2021, the dark web forum hosting 700 million LinkedIn users’ data was leaked, affecting more than 90% of the company’s user base. By abusing the site’s API, a hacker going by the handle “God User” used data scraping techniques before leaking the first private set of data of approximately 500 million users. 

Protect yourself from data breaches 

You must take the necessary steps to protect data and your confidential documents and electronic media in order to safeguard yourself from becoming a victim of a data breach, whether the perpetrator is a dumpster-diving identity thief looking for your discarded medical bills or a hacker who is targeting the network of your business in order to steal customer information. 

 

References: 

What is a Data Breach & How to Prevent One – Kaspersky 

51 impactful data science quotes by thought leaders 
Ayesha Saleem
| September 7, 2022

50 self-explanatory data science quotes by thought leaders you need to read if you’re a Data Scientist, – covering the four core components of data science landscape. 

Data science for anyone can seem scary. This made me think of developing a simpler approach to it. To reinforce a complicated idea, quotes can do wonders. Also, they are a sneak peek into the window of the author’s experience. With precise phrasing with chosen words, it reinstates a concept in your mind and offers a second thought to your beliefs and understandings.  

In this article, we jot down 51 data science quotes that were once shared by experts. So, before you let the fear of data science get to you, browse through the wise words of industry experts divided into four major components to get inspired. 

Data science quotes

Data strategy 

If you successfully devise a data strategy with the information available, then it will help you to debug a business problem. It builds a connection to the data you gather and the goals you aim to achieve with it. Here are five inspiring and famous data strategy quotes by Bernard Marr from his book, “Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things” 

  1. “Those companies that view data as a strategic asset are the ones that will survive and thrive.” 
  2. “Doesn’t matter how much data you have, it’s whether you use it successfully that counts.” 
  3. “If every business, regardless of size, is now a data business, every business, therefore, needs a robust data strategy.” 
  4. “They need to develop a smart strategy that focuses on the data they really need to achieve their goals.” 
  5. “Data has become one of the most important business assets, and a company without a data strategy is unlikely to get the most out of their data resources.” 

Some other influential data strategy quotes are as follows: 

6. “Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming.” – Chris Lynch, Former CEO, Vertica  

7. “You can’t run a business today without data. But you also can’t let the numbers drive the car. No matter how big your company is or how far along you are, there’s an art to company-building that won’t fit in any spreadsheet.” Chris Savage, CEO, Wistia 

8. “Data science is a combination of three things: quantitative analysis (for the rigor required to understand your data), programming (to process your data and act on your insights), and narrative (to help people comprehend what the data means).” — Darshan Somashekar, Co-founder, at Unwind media 

9. “In the next two to three years, consumer data will be the most important differentiator. Whoever is able to unlock the reams of data and strategically use it will win.” — Eric McGee, VP Data and Analytics 

10. “Data science isn’t about the quantity of data but rather the quality.” — Joo Ann Lee, Data Scientist, Witmer Group 

11. “If someone reports close to a 100% accuracy, they are either lying to you, made a mistake, forecasting the future with the future, predicting something with the same thing, or rigged the problem.” — Matthew Schneider, Former United States Attorney 

12. “Executive management is more likely to invest in data initiatives when they understand the ‘why.’” — Della Shea, Vice President of Privacy and Data Governance, Symcor

13. “If you want people to make the right decisions with data, you have to get in their head in a way they understand.” — Miro Kazakoff, Senior Lecturer, MIT Sloan 

14. “Everyone has the right to use company data to grow the business. Everyone has the responsibility to safeguard the data and protect the business.” — Travis James Fell, CSPO, CDMP, Product Manager 

15. “For predictive analytics, we need an infrastructure that’s much more responsive to human-scale interactivity. The more real-time and granular we can get, the more responsive, and more competitive, we can be.”  Peter Levine, VC and General Partner ,Andreessen Horowitz 

Data engineering 

Without a sophisticated system or technology to access, organize, and use the data, data science is no less than a bird without wings. Data engineering builds data pipelines and endpoints to utilize the flow of data. Check out these top quotes on data engineering by thought leaders: 

16. “Defining success with metrics that were further downstream was more effective.” John Egan, Head of Growth Engineer, Pinterest 

17. ” Wrangling data is like interrogating a prisoner. Just because you wrangled a confession doesn’t mean you wrangled the answer.” — Brad Schneider – Politician 

18. “If you have your engineering team agree to measure the output of features quarter over quarter, you will get more features built. It’s just a fact.” Jason Lemkin, Founder, SaaStr Fund 

19. “Data isn’t useful without the product context. Conversely, having only product context is not very useful without objective metrics…” Jonathan Hsu, CFO, and COO,  AppNexus & Head of Data Science, at Social Capital 

20.  “I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology, then it’s not a problem.” Aaron Koblin, Entrepreneur in Data and Digital Technologies 

21. “Many people think of data science as a job, but it’s more accurate to think of it as a way of thinking, a means of extracting insights through the scientific method.” — Thilo Huellmann, Co-fFounder, at Levity 

22. “You want everyone to be able to look at the data and make sense out of it. It should be a value everyone has at your company, especially people interacting directly with customers. There shouldn’t be any silos where engineers translate the data before handing it over to sales or customer service. That wastes precious time.” Ben Porterfield, Founder and VP of Engineering, at Looker 

23. “Of course, hard numbers tell an important story; user stats and sales numbers will always be key metrics. But every day, your users are sharing a huge amount of qualitative data, too — and a lot of companies either don’t know how or forget to act on it.” Stewart Butterfield, CEO,   Slack 

Data analysis and models 

Every business is bombarded with a plethora of data every day. When you get tons of data, analyze it and make impactful decisions. Data analysis uses statistical and logical techniques to model the use of data:.  

24. “In most cases, you can’t build high-quality predictive models with just internal data.” — Asif Syed, Vice President of Data Strategy, Hartford Steam Boiler 

25. “Since most of the world’s data is unstructured, an ability to analyze and act on it presents a big opportunity.” — Michael Shulman, Head of Machine Learning, Kensho 

26. “It’s easy to lie with statistics. It’s hard to tell the truth without statistics.” — Andrejs Dunkels, Mathematician, and Writer 

27. “Information is the oil of the 21st century, and analytics is the combustion engine.” Peter Sondergaard, Senior Vice President, Gartner Research 

28. “Use analytics to make decisions. I always thought you needed a clear answer before you made a decision and the thing that he taught me was [that] you’ve got to use analytics directionally…and never worry whether they are 100% sure. Just try to get them to point you in the right direction.” Mitch Lowe, Co-founder of Netflix 

29. “Your metrics influence each other. You need to monitor how. Don’t just measure which clicks generate orders. Back it up and break it down. Follow users from their very first point of contact with you to their behavior on your site and the actual transaction. You have to make the linkage all the way through.” Lloyd Tabb, Founder, Looker 

30. “Don’t let shallow analysis of data that happens to be cheap/easy/fast to collect nudge you off-course in your entrepreneurial pursuits.” Andrew Chen, Partner at Andreessen Horowitz,  

31. “Our real job with data is to better understand these very human stories, so we can better serve these people. Every goal your business has is directly tied to your success in understanding and serving people.” — Daniel Burstein, Senior Director, Content & Marketing, Marketing Sherpa 

32. “A data scientist combines hacking, statistics, and machine learning to collect, scrub, examine, model, and understand data. Data scientists are not only skilled at working with data, but they also value data as a premium product.” — Erwin Caniba, Founder and Owner,Digitacular Marketing Solutions 

33. “It has therefore become a strategic priority for visionary business leaders to unlock data and integrate it with cloud-based BI and analytic tools.” — Gil Peleg, Founder , Model 9 – Crunchbase 

34.  “The role of data analytics in an organization is to provide a greater level of specificity to discussion.” — Jeff Zeanah, Analytics Consultant  

35. “Data is the nutrition of artificial intelligence. When an AI eats junk food, it’s not going to perform very well.” — Matthew Emerick, Data Quality Analyst 

36. “Analytics software is uniquely leveraged. Most software can optimize existing processes, but analytics (done right) should generate insights that bring to life whole new initiatives. It should change what you do, not just how you do it.”  Matin Movassate, Founder, Heap Analytics 

37. “No major multinational organization can ever expect to clean up all of its data – it’s a never-ending journey. Instead, knowing which data sources feed your BI apps, and the accuracy of data coming from each source, is critical.” — Mike Dragan, COO, Oveit 

38. “All analytics models do well at what they are biased to look for.” — Matthew Schneider, Strategic Adviser 

39. “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” Geoffrey Moore, Author and Consultant 

Data visualization and operationalization 

When you plan to take action with your data, you visualize it on a very large canvas. For an actionable insight, you must squeeze the meaning out of all the analysis performed on that data, this is data visualization. Some  data visualization quotes that might interest you are: 

40. “Companies have tons and tons of data, but [success] isn’t about data collection, it’s about data management and insight.” — Prashanth Southekal, Business Analytics Author 

41. “Without clean data, or clean enough data, your data science is worthless.” — Michael Stonebraker, Adjunct Professor, MIT 

42. “The skill of data storytelling is removing the noise and focusing people’s attention on the key insights.” — Brent Dykes, Author, “Effective Data Storytelling” 

43. “In a world of more data, the companies with more data-literate people are the ones that are going to win.” — Miro Kazakoff, Senior Lecturer, MIT Sloan 

44. The goal is to turn data into information and information into insight. Carly Fiorina, Former CEO, Hewlett Packard 

45. “Data reveals impact, and with data, you can bring more science to your decisions.” Matt Trifiro, CMO, at Vapor IO 

46. “The skill of data storytelling is removing the noise and focusing people’s attention on the key insights.” — Brent Dykes, data strategy consultant and author, “Effective Data Storytelling” 

47. “In a world of more data, the companies with more data-literate people are the ones that are going to win.” — Miro Kazakoff, Senior Lecturer, MIT Sloan 

48. “One cannot create a mosaic without the hard small marble bits known as ‘facts’ or ‘data’; what matters, however, is not so much the individual bits as the sequential patterns into which you organize them, then break them up and reorganize them'” — Timothy Robinson, Physician Scientist 

49. “Data are just summaries of thousands of stories–tell a few of those stories to help make the data meaningful.” Chip and Dan Heath, Authors of Made to Stick and Switch 

Parting thoughts on amazing data science quotes

Each quote by industry experts or experienced professionals provides us with insights to better understand the subject. Here are the final quotes for both aspiring and existing data scientists: 

50. “The self-taught, un-credentialed, data-passionate people—will come to play a significant role in many organizations’ data science initiatives.” – Neil Raden, Founder, and Principal Analyst, Hired Brains Research. 

51. “Data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.” – Mike Loukides, Editor, O’Reilly Media. 

Have we missed any of your favorite quotes on data? Or do you have any thoughts on the data quotes shared above? Let us know in the comments. 

 

 

Rebecca Merrett
| November 22, 2017

There’s more to data security and access control than granting teams within a company different access levels and issuing user passwords.

As data scientists, our job is not to run the whole security operation in our organizations to avoid a security breach. However, as we work very closely with data, we must understand the importance of having good, robust mechanisms in place to prevent sensitive and personally identifiable information from getting into the wrong hands, or from any cyber attack. Hence, the need for data security.

Strong passwords? Not enough

Setting ourselves up with a strong password might not cut it in today’s world. Some of the world’s biggest banks, which have an army of highly skilled security professionals, have suffered ever-more smarter cyber attacks. Today, users are logging into work systems and databases through biometrics such as fingerprint scanning technology on smartphones, laptops, and other devices or computers.

Two-factor authentication is also a popular mechanism of data security, which goes beyond simply identifying and authenticating a user through their password alone. Users are now logging into systems using a one-time password – which is sent to their work email, requiring another form of login – in combination with their fingerprint password. Generating a random number or token string each time a user logs into a system can reduce the risk of a single password being decrypted or obtained some other way.

Finishing the equation

User identity and authentication are only half of the equation, however. The other half is using anomaly detection algorithms or machine learning to pick up on unusual user activity and behavior once a user has logged on. This is something we as data scientists can bring to the table in helping our organizations better secure our customer or business data. Some of the key features of anomaly detection models include the time of access, location of access, type of activity or use of the data, device type, and how frequently a user accesses the database.

The model collects these data security points every time a user logs into the database and continuously monitors and calculates a risk score based on these data security points and how much they deviate from the user’s past logins. If the user reaches a high enough score, an automated mobile alert can be sent to the security team to further investigate or to take action.

Data security examples

Some obvious data security examples include a user who lives in Boston who logged out of the database 10 minutes ago but is now accessing the database in Berlin. Or, a user who usually logs in to the database during work hours is now logging in at 3 am.

Other examples include an executive assistant, who rarely logs into the database, and is now frequently logging into the database every 10 minutes. A data scientist, who usually aggregates thousands of rows of data is now retrieving a single row.

A marketer, who usually searches the database for contact numbers, is now attempting to access credit card information, even though that marketer already knows she/he does not have access to this information.

Another way data scientists can safeguard their customer or business data is to keep the data inside the database rather than exporting a subset or local copy of the data onto their computer or device. Nowadays, there are many tools to connect different database providers to R or Python, such as the odbcConnect() function as part of the RODBC library in R, which reads and queries data from a database using an ID and password rather than importing data from a local computer.

The ID and password can be removed from the R or Python file once the user has finished working with the data, so an attacker cannot run the script to get the data without a login. Also, if an attacker were to crack open a user’s laptop, he or she would not find a local copy of the data on that device.

Row and column access is another example of data security through fine-grained access controls. This mechanism masks certain columns or rows for different users. These masked columns or rows in tabled data usually contain sensitive or personally identifiable information. For example, the columns which contain financial information might be masked by the data science team but not by the finance/payments processing team.

Conclusion & other tips

Other ways to safely deal with sensitive and personally identifiable information include differential privacy and k-anonymity. To learn about these techniques, please read Dealing with data privacy – anonymization techniques.

Mohd Sohel Ather
| June 24, 2019

Learn how to configure the security of your Windows 10 account and be the true owner of your personal data and your privacy.

Technology is something wonderful that for centuries has been improving people’s lives and facilitating day-to-day, allowing us to do things that until now were impossible. It is undeniable how the internet has changed our lives and connected us with people from all over the globe, but it also has its drawbacks.

The latest advances in technology have reduced our privacy to a level that we had never before reached. It is no longer necessary to be a famous star so that our privacy is worth a lot of money. Many of the services and tools we use every day are collecting our personal data without us being aware, in exchange for making our lives easier.

Whether to protect you from hackers who want to steal your banking information or because you do not feel comfortable sharing your location with all the applications you use, you must be aware of how to protect yourself from these dangers.

Here I propose a few essential steps in Windows 10 to be the owner of your data and know who wants to know where you are or what pages you visit, and even, avoid being spied on through your computer’s camera.

1. Say no to the fast installation of Windows 10

When it comes to Windows 10 or an application, many times we look for the fastest option in which we have to do as little as possible and companies increasingly offer more options for users to get rid of these very tedious processes.

The downside is that the more we disengage from the configuration and installation process, the more power we give companies to do what they want with our privacy, you know the saying, “if you want something to be done well, you have to do it yourself”.

When installing Windows 10 make sure you choose the custom configuration to monitor each of the permissions you give the system. Then go to the privacy settings. To get to this section, you only have to press the Windows key and the ‘I’ key at the same time. There you can configure the Windows 10 privacy to your liking, although you will not be able to access the configuration of the other applications. You will have to go one by one. Even if you are configuring your Windows Office, you can click here to read about how to do it properly.

2. Cut the wings to Cortana

Virtual assistants are very useful to make our lives easier but to know each other so well as to be really useful we must give them access to a whole range of personal data. If you work with your computer, you may not be interested in Cortana having access to your company’s data or bank details. Although in the end you do not care and let them work with this information, at least we recommend that you take a look at everything Cortana wants to know about you.

In the configuration section of Cortana, you can delete all the data that this assistant has from you or select those in particular that seem too private.

3. Turn off your location

As mobile phones do, Windows 10 automatically tracks your location at any time and saves this information for about 24 hours to also share it with any third-party application that you have downloaded.

Once again in the privacy settings section, you can deactivate the tracking of your location, or activate it in case you need it in a timely manner. In addition, it is recommended that you review the configuration of each of the applications that you install on your computer and be aware of which ones are trying to know where you are.

4. Block ad tracking

Whether you are browsing the different online stores looking for a new smartphone or you are simply checking hotels to find out how much it would cost you to go on a weekend trip, you will see hundreds of ads about things you have been looking for.

That’s ad tracking and it’s a nuisance, but the good thing is that it can be blocked. Obviously, by default Windows 10 has it activated because it is great for companies to know what you are thinking of spending your money on.

In the privacy settings, you will find a section where you can deactivate the option “Allow applications to use my advertising ID”.

5. Don’t allow access to your camera

How many times has Hollywood warned us with their films and TV series about hackers that are dedicated to controlling the cameras of other people’s laptops to spy on a girl and how many times we would have thought it was an invention of the screenwriters?

Well it is a very real possibility and experts warn of how easy it is to do this, so, it is more than recommended to block the camera and only activate it when you have to use it for a conference or a family video call. Moreover, you cannot only disable the camera from the computer configuration, but you can cover it with small protectors that give a lot of peace and do not cost a lot.

6. Disable access to the microphone

If we block the camera, how can we not block the microphone? It is true that if you want to give instructions to Cortana you need it, but if you have decided to silence her completely, you should also do the same with the microphone.

7. Who can see your account information?

Another data point we give to Windows and it shares with others is the account data, such as name and email address. If we go to the section “Account information” we will see that many of the applications we use have access to this data.

Customizing this section of the configuration never hurts and shares as little data as possible with applications.

8. Eliminate tracking of the timeline

This point is related to point number four. Surfing the internet should imply that others know what you are looking for even if they are completely harmless pages. Tracking the timeline involves tracking all the websites you have visited, eliminating it increases your privacy, and control of your data. The bad thing is that you have to do it on a regular basis, like the browser history, which the experts recommend to delete it in the usual way.

9. Check the privacy settings after each update

We are sorry to inform you that after a system update it is possible that many of the steps you have taken today will be lost and you will have to fix them where you left them. Microsoft usually re-establishes all the privacy settings and reinforces them by default when restarting. Many complaints have been made about this aspect but it has fallen on deaf ears. Nothing happens but this should not prevent us from being attentive. To stay alert, it is best to turn off automatic updates and be aware of when an important one arrives.

This does not mean it’s better not to update. Updates are important to get new interesting features and improvements in system security but we should not give them up unless we see that they return to give problems.

10. Do not leave your privacy in the hands of others

This advice serves both Windows 10 users and any other system and device users, even Apple. User data is becoming the great business of the 21st century. Our privacy, tastes, interests, and other personal data are worth a lot to companies but they should be worth more to ourselves and we should protect them as if they were gold.

It requires a little effort to be aware of tricks like these and steps or tools that help us keep our privacy as intact as possible.

Gilad Maayan
| February 20, 2020

Review the relationship between data science and cybersecurity with the most common use cases.

Data science brings a logical structure to unstructured data. Data scientists use machine or deep learning algorithms to compare normal and abnormal patterns. In cybersecurity, data science helps security teams distinguish between potentially malicious network traffic and safe traffic.

Applications of data science in cybersecurity are relatively new. Many companies are still using traditional measures like legacy, antiviruses, and firewalls. This article reviews the relationship between data science and cybersecurity and the most common use cases.

How data science changed cyber security

Large organizations have a lot of data moving throughout their network. The data can originate from internal computers, IT systems, and security tools. However, these endpoints do not communicate with each other. The security technology responsible for detecting attacks cannot always see the overall picture of threats.

Before the adoption of data science, most large organizations used the Fear, Uncertainty, and Doubt (FUD) approach in cybersecurity. The information security strategy was based on FUD-based assumptions. Assumptions about where and how attackers may attack.

With the help of data science, security teams can translate technical risk into business risk with data-driven tools and methods. Ultimately, data science enabled the cyber-security industry to move from assumptions to facts.

The relationship between data science and cybersecurity

The goal of cybersecurity is to stop intrusions and attacks, identify threats like malware, and prevent fraud. Data science uses Machine Learning (ML) to identify and prevent these threats.

For instance, security teams can analyze data from a wide range of samples to identify security threats. The purpose of this analysis is to reduce false positives while identifying intrusions and attacks.

Security technologies like User and Entity Behavior Analytics (UEBA) use data science techniques to identify anomalies in user behavior that may be caused by an attacker. Usually, there is a correlation between abnormal user behavior and security attacks.

These techniques can paint a bigger picture of what is going on by connecting the dots between these abnormalities. The security team can then take proper preventative measures to stop the intrusion.

The process is the same for preventing fraud. Security teams detect abnormalities in credit card purchases by using statistical data analysis. The analyzed information is then used to identify and prevent fraudulent activity.

Data science had a profound effect on cybersecurity.  This section aims to explain the key impacts of data science in the field of cybersecurity.

Intrusion, Detection, and Prediction

Security professionals and hackers always played a game of cat-and-mouse. Attackers used to constantly improve their intrusion methods and tools. Whereas security teams improved detection systems based on known attacks. Attackers always had the upper hand in this situation.

Data science techniques use both historical and current information to predict future attacks. In addition, machine learning algorithms can improve an organization’s security strategy by spotting vulnerabilities in the information security environment.

Establishing DevSecOps cycles

DevOps pipelines ensure a constant feedback loop by maintaining a culture of collaboration. DevSecOps adds a security element to DevOps teams. A DevSecOps professional will first identify the most critical security challenge and then establish a workflow based on that.

Data scientists are already familiar with DevOps practices because they use automation in their workflows. As a result, DevSecOps can easily be applied to data science in a process called DataSecOps. This type of agile methodology enables data scientists to promote security and privacy continuously.

Behavioral analytics

Traditional antiviruses and firewalls match signatures from previous attacks to detect intrusions. Attackers can easily evade legacy technologies by using new types of attacks.

Behavior analytics tools like User and Entity Behavior Analytics (UEBA) use machine learning to detect anomalies and potential cyberattacks. If, for example, a hacker stole your password and username, they may be able to log into your system. However, it would be much harder to mimic your behavior.

Data protection with associate Rule Learning

Associate Rule Learning (ARL) is a machine learning method for discovering relations between items in large databases. The most typical example is market-based analysis. ARL shows relations between items that people buy most frequently. For example, a combination of onions and meat may relate to a burger.

ARL techniques may also recommend data protection measures. The ARL studies the characteristics of existing data and alerts automatically when it detects unusual characteristics. The system constantly updates itself to detect even the slightest deviations in the data.

See 101 Machine Learning Algorithms learn more about ARL.

Backup and data recovery

New backup technologies are leveraging machine learning to automate repetitive backup and recovery tasks. Machine learning algorithms are trained to follow the priorities and requirements of security plans.

Backup and recovery systems based on ML can help incident response teams organize workspaces and resources. For example, ML tools can access and recommend the necessary equipment and locations for a particular business recovery plan based on the company’s needs.

Conclusion

Cyber attacks are always evolving, and no one knows what form they will take in the future. Data science enables companies to predict possible future threats based on historical data with technologies like UEBA. Intrusion Detection Systems (IDS) use regression models to predict potential malicious attacks. Data science can leverage the power of data to create stronger protection against cyber attacks, and data losses.

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