Ready to Start Your Career?

How Will AI/ML Strengthen Cybersecurity & Prevent Breaches?

Shifa Martin's profile image

By: Shifa Martin

September 18, 2020

How Will AI/ML Strengthen Cybersecurity & Prevent Breaches?

Tales of crippling security breaches are not new. Despite the warnings and exposure that these issues receive, companies seem indifferent, and compromises are on the rise.

Within the last ten years, there have been 300 data breach incidents that include the theft of more than 100,000 records. Here are some data breach statistics of past years that are astonishing.

  • In 2018, the US experienced 1,244 data breaches, which accounted for the theft of 446.5 million records.
  • In the first six months of 2019, 4.1 billion records were exposed to data breaches.
  • As of 2019, per the World Economic Forum, cyber-attacks are among the top five risks to global stability.

Indeed, what is more lethal to the IT industry and can shake the world economies?

Improved Cybersecurity is the demand of growing data-driven industries. Thus, several technology uses are being tried and tested by experts and technologists in the world's largest cybersecurity labs. When technologies like blockchain have their security infrastructure, the usage of AI/ML algorithms for data security is a significant point of discussion among companies.

The Need for Cyber Security Provisions in 2020

Doesn't every enterprise entail the value of customer data and trust? Industries know that a single breach of customer data can cause the loss of billions of dollars. Thus, enterprises are more focused on security. Here are some aspects that indicate the need for cybersecurity provision in 2020.

The following representation depicts how frequent data breach incidents occurred in the past, and the types of fraud.


Image source

  • Per Varonis, only 5% of companies' data folders are properly protected.
  • Per Accenture, 68% of business leaders find themselves exposed to cybersecurity risks, and the problems are increasing.
  • Verizon unveiled that 71% of breaches were motivated by financial gain, while 25% were espionage motivated.
  • Cybersecurity Media indicates that the worldwide number of passwords used by humans and machines would rise to 300 billion by 2020.
  • 52% of breaches result from hacking (phishing, malware, and ransomware), in which 32-33% are attributed to phishing or social engineering, and 28% feature malware breach.

Moreover, Industries like banking/credit/finance, healthcare, government & military, and small & large businesses are frequent targets of Cyber Attacks. Improved data security is currently the most crucial need for businesses.

Notably, enterprises are investing in security more seriously. In 2020, the world security market is expected to reach up to $17.4 billion, clear that businesses are following a decisive approach.

However, the growth rate of AI/Ml in Cybersecurity is awe-inspiring. The elevated usage of AI in Cybersecurity will be worth $46.3 billion by 2027. Undoubtedly, this is a huge influx of money in this market.


Images source

Altogether, enterprises are eager to hire machine learning app developers who can create initially robust programs to prevent cybercrimes. However, the inquiry remains the same; how can AI/ML enhance Cybersecurity and prevent enterprises' data breaches?

Here are some ways AI/ ML could transform Cybersecurity in 2020.

How AI/ML can improve Cybersecurity & prevent data breaches for businesses?

When it comes to huge data, there is nothing more reliable and useful than AI and ML. Being a superset of ML, AI governs a lot of data and enables different processes, not just the integration of ML algorithms but also analytics & automation. However, when you try to understand the use of machine learning in Cybersecurity, you may find that this aspect is a phenomenon in itself, as it offers complete support to a lot of data on both cloud and end-points. Besides, it also enables the use of BigData and Analytics in combination.

Artificial intelligence proves very useful in Cybersecurity. It is suitable to process humongous amounts of data & perform actions to recognize anomalies, unusual behavior, suspicious activities, zero-day attacks, and identify & correct prevailing vulnerabilities. Altogether, ML can help find issues related to higher complexity, accuracy, and inconsistency, faster than any human analyst.

When it comes to cyberattacks, it is essential to respond to the situation to minimize the impact immediately. Al/ML-driven automated responses, such as attack impact reduction and conducting forensics, will help minimize attack exposure. The below diagram displays how AI detection takes place in different ways and is a gleaning insight into AI/ML usage in detection, prediction, and response.


Image source

To prevent any data breach, it is beneficial to fill the gap between machines and humans' response time. Using AI and machine learning algorithms, intelligent cyber weapons can be prevented using intelligent software programs, and data theft can be reduced.

Let's look at some threats specifically prevented by using machine learning:

  • Spear Phishing Attack
  • DDoS(Distributed Denial of service)
  • DNS Poisoning
  • Port Scanning
  • Ransomware
  • Webshell
  • Watering Hole and more

ML can help in major data breaches & aid businesses to save their customer data. Here are the ways we can use machine learning in Cybersecurity:

  • Fraud Detection: Finding online fraudster content, browsing activities, fake identities, fraudulent social networks.
  • Enhance Human Analysis: Boost staff productivity by identifying acute problems and detecting and reducing false-positive rates.
  • Incident Response & Forensics: This practice includes creating automated responses, forensic conducts & defense, intelligent software programs, infuse accuracy & effectiveness to the response.

Machine learning (ML) is increasingly leveraged by businesses engaged in eCommerce to fight fraudsters. ML can be used to find fraudulent agents within a network. However, there are major challenges, such as how will ML algorithms sense unstructured data and approve/decline decisions of peers in real-time.

Enterprises can also use machine learning-based Cybersecurity to find the problems within KRA/KPIs, false reporting, and more. It will help in improving organizational productivity in terms of infusing experts in the cybersecurity unit. Another critical application can be a reduction in false-positive rates that might transform cybersecurity operations.

"Machine learning is not a remedial solution; instead, it is a supporting technology in Cybersecurity for breach prevention."

Critically, the road for its full adoption and usage is long in Cybersecurity as it's sole utilization is to reduce human intervention and bring in machine intelligence. However, it can benefit cyber-security and help enterprises to prevent data breaches.

How can AI & ML benefit Cybersecurity?

There are no such instances where machine learning can't be applied and trigger benefits to strengthen organizations' cybersecurity. Here are the benefits that we will appreciate in the future.

AI/ML Can Manage Huge Lots of Data

Automation of Cybersecurity through AI/ML technologies will help enterprises manage a tremendous amount of data each day. Structurally robust machine learning algorithms can find threats in the network before any breach or failure happens, and enable enterprises to avert instances of security breaches. Moreover, it can skim across huge data sets swiftly to expose vulnerabilities.

In short, ML-driven automation processes will simplify analysis in cybersecurity events sorting the heap of chaotic data.


Ease In Finding Unknown Threats

For enterprises, it is most desirable to find unknown threats before any mishappening. Hackers evolve their skills and create malicious software, which is hard to detect at first glance. By the time an expert understands the loop, breaches have taken place.

As per G Data software analysis, every day, 7.41 new malware emerged in 2017. Indeed, this is a huge influx. Though, machine learning can be proven helpful to find such unknown agents and tell the enterprise beforehand.

Automation of Cybersecurity will keep a keen eye on changes in network systems to identify new threats. A top machine learning company in India called it the biggest achievement in terms of online fraud detection.

AI Algorithms Grow Intelligent Over Time

The most acclaimed and admired property of AI algorithms is that they learn over time and become more responsive to decision-making. Using these algorithms in Cybersecurity will be a great achievement, as they can identify patterns and predict changes.

After identifying changes, AI algorithms process altogether to detect potential threats of similar traits and defense against them. Moreover, AI cybersecurity solutions grow familiar with network traffic and swiftly identify changes and remove threats by taking required actions.

Altogether, such algorithms will prove helpful to IT security experts to recognize and solve for risks in enterprise systems.

Machine Learning Allows To Fight Scams

Machine learning is very useful in detecting scams. Google is using it to avert phishing scams and spam. Phishing attacks are processes where cyber-criminals manipulate users to click on links, false links imitating brand names, and causing data theft.

Google prepared an algorithm with the combination of ML that detects suspicious emails and triggers warnings to users, and prevents breaches.

Do you think AI/ML can transform the structure of Cybersecurity in the future? Indeed, they will improve data security, including preventing almost every internet breach in the coming days, and it will help several industry enterprises build trust among customers.

Final Words

Data breaches can be prevented through integrating artificial intelligence and machine learning in the future. Enterprises can use automated cybersecurity systems that will put an end to cyber threats and help companies create impeccable reliance among customers.

Cybersecurity companies are continually investing in AI for creating futuristic, more robust, intelligent cybersecurity software. And! Machine learning developers are the significant driving force that excels the innovation in this space.

Global IT giants like Microsoft and Google are also engaged in accelerating the processes by developing game-changing algorithm acquisition. AI solutions for Cybersecurity will grow and become very effective in fighting data breaches and cybercrimes.

Author Bio:

I am Shifa Martin, a Technical Writer and IT analyst at ValueCoders- App Development Company. I have decades of experience in guiding and consulting start-ups, enterprises, and entrepreneurs about web application development, mobile app development, and other advanced technology, such as Machine Learning, Android (Kotlin), React Native, AR/VR, Artificial Intelligence, and much more.

Social Profiles: Linked In- Twitter- Facebook-

Schedule Demo