The document discusses the dual impact of artificial intelligence (AI) and machine learning (ML) on cybersecurity, highlighting both positive and negative effects. While AI and ML enhance cybersecurity measures by improving fraud detection, spam handling, and malware detection, they also present new threats such as advanced malware and efficient phishing attacks. Companies are urged to adopt these technologies carefully, as their applications can be beneficial in the right hands but detrimental if misused.

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Top Positive and Negative Impacts of AI & ML
on Cybersecurity
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With great power comes great responsibilities- it is the most iconic phrase written by Stan Lee
and popularized by Spider-Man. Undoubtedly the line holds to disruptive technologies, including
AI and ML.
You might have heard only praise and good things about AI and ML, but that does not mean that
they cannot become a bitter enemy to humanity. AI & ML are advanced technologies bound to
function under the administrator’s command.
Arti몭cial Intelligence (AI) and Machine Learning (ML) technologies have many positive
applications, from helping researchers better understand neural pathways in the brain to
Varun Bhagat - September 3, 2021  0
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2. assisting law enforcement with identifying suspects in criminal investigations.
They are renowned for the greater good of cybersecurity. However, these technologies also hold
the potential to ruin our perfectly running digital world and become a source of power to the
dark web users/administrators.
If you’re not familiar with how AI and ML might impact cybersecurity, this blog will discuss both
sides of the coin and help you better understand how this technology might a몭ect you one day
soon.
Here are some famous AI-based cybersecurity use cases:
# As per Forbes, 69% of enterprises trust AI for handling cyber attacks.
# As much as 80% of telecommunication industries believe that without AI, cyber security is not
feasible.
# Network security uses AI up to 75% in developed nations, as per Statista.
# Cloud security accounts for nearly 59% of use cases of Arti몭cial Intelligence.
# According to Oracle, 62% of U.S based enterprises have already implemented AI for cyber
security applications.
# IBM says AI’s most outstanding contribution in cyber security is the speed of analyzing threats
with an acceleration up to 64%.
Look at the infographic below depicting a section of the above stats:
Source: Statista

3. A lot has been explored in recent years about how little we know about security. If you ask ten
di몭erent cybersecurity experts, you will likely get ten unique answers. However, many are
unfamiliar with the exact aspects of cyber security.
It isn’t necessarily a bad thing because there is no one-size-몭ts-all approach to cybersecurity.
What works for one 몭rm might fail to work for another in a completely di몭erent industry or with
entirely di몭erent kinds of data; Similarly, what works in one country might not work in another
country. However, everyone agrees to the de몭nition below.
What is Cybersecurity?
Cybersecurity is a proactive defense against hackers, cybercriminals, data theft, and other
cybersecurity threats. Security experts work with IT professionals to identify weaknesses in
systems before intruders can exploit them.
Source: Statista
Businesses need to incorporate cybersecurity into their overall business plan to prevent losses
due to cybersecurity issues. It means implementing tools like encryption software (including
secure mobile device management), building alliances with local law enforcement agencies, or
hiring external cybersecurity teams.
In addition to helping keep your customers’ data safe, these strategies will also help strengthen
your company’s bottom line by protecting its assets from theft and damage in an increasingly
digital world.
Companies must be 몭exible enough to customize their security strategies as needed so they can
adapt to new threats without slowing down their business processes too much.

4. adapt to new threats without slowing down their business processes too much.
Major Cybersecurity Threats in 2021
#1. Malware: These are software programs designed to disrupt computer operation, gather
sensitive information, or access private computer systems. Malware often arrives as spam
messages containing attachments or links that download viruses and worms onto their
computers when clicked on by users.
#2. Ransomware: It hijacks your computer, encrypts all of your 몭les, and then demands a
ransom payment for you to get them back. These sorts of attacks are becoming increasingly
popular with criminals due to their success rate.
#3. Social Engineering Attacks: It is a deception that exploits human nature to trick people into
giving up sensitive information or performing actions that harm their organizations.
#4. Phishing: It is social engineering typically carried out through email spoo몭ng or instant
messaging that aims to steal personal information such as usernames, passwords, and credit
card details by masquerading as trustworthy entity in an electronic communication.
#5. PDF Scams: Using a PDF 몭le to scam is a common exploit used by malicious hackers. In
general, these types of attacks are tough to detect.
#6. Database Exposure Threat: Occurs when sensitive data stored on a server gets exposed to
an unauthorized third party.
#7. Credential Stu몭ng: These are automated, web-based attacks in which attackers gather a list
of usernames and passwords stolen from one website or application and use them to try logging
into other sites and applications.
#8. Accidental Sharing: It is a scenario when users share con몭dential information without
realizing they are doing so. It occurs when users intend to share a document only with their
colleagues or manager, but it accidentally gets shared with all other users on an email
distribution list.
That was all about the common cybersecurity threats; let’s explore how AI & ML in몭uences them.
Positive Impacts of AI & ML Over Cybersecurity Threats
1. Combating Fraud & Anomalies
Anti Fraud use cases are more e몭ective with machine learning as they can monitor transactions,
behavioral patterns of users, or click patterns more e몭ectively. Using Machine Learning for fraud
detection is very e몭cient compared to traditional methods that require a lot of human labor.
In a recent study, Forbes reported that account takeovers (ATO) decreased from 33% to 17%, and
revenue per client increased by 65%. With machine learning, 몭nancial institutions can block
millions of fraudulent transactions before reaching consumers.

5. millions of fraudulent transactions before reaching consumers.
The industry is ripe for applying new techniques like deep neural networks, which allow analyzing
large volumes of information at rapid speeds without compromising accuracy. AI algorithms also
help reduce Internet tra몭c snarls caused by malicious bots trying to access web applications
from various browsers on devices. This can be achieved by hiring top AI developers.
2. Handling Spams
Researchers found that the average word length in an email has increased, suggesting that
scammers are putting more e몭ort into creating authentic-sounding messages. Because of these
complications, a straightforward solution will not be e몭ective at entirely thwarting spam accounts
– but using arti몭cial intelligence does it.
Using advanced AI models called generative adversarial networks (GANs), researchers at MIT
were able to develop algorithms capable of distinguishing between bots and humans with
incredible accuracy.
3. Smart Malware Detection
Intelligent malware detection software was developed by computer scientists to detect malware
using a set of predetermined rules based on known malware automatically. Intelligent anti-
malware uses machine learning, or arti몭cial intelligence, to learn what is safe code and what is
malicious code.
Over time, smart anti-malware can quickly recognize new varieties of malicious code and create
rules that help it determine if speci몭c actions are suspicious or hostile. Not only do intelligent
anti-malware keep data protected, but its ability increases as hackers develop new methods for
attacking computers.
4. Data Leak Prevention
With massive amounts of data collected on people’s online habits, companies must use their
immense stores of data responsibly. With arti몭cial intelligence (AI) and Machine Learning (ML),
companies can now mine their big data sets to understand better what kinds of activities might
pose data leak risks.
The most vulnerable data types include customer credit card numbers, social security numbers,
or health records, all pieces of information worth protecting from external threats. However,
current cybersecurity methods aren’t capable of preventing all potential threats e몭ectively.
Negative Impact of AI & ML Over Cybersecurity Threats
1. Malware
AI-based malware is more e몭cient in hiding from the reach of the anti-malware software. Thus,
when the software 몭nds and eliminates malware, the hackers would have already gained
potential information from the user’s device.

6. 2. Botnet
Bots are pieces of software that run automated tasks on a compromised system. Malware
authors often use them for malicious purposes like attacking websites or other computers and
stealing information from infected systems.
One such malware has been Cryptolocker, which aggressively spreads itself via spam messages
posing as speeding tickets. It encrypted users’ 몭les until they paid a ransom in BitCoins to get
their data back.
A botnet is a matrix of computers controlled by an attacker through malware installed on each
computer. Combining these two has made it easy for criminals to wreak havoc online, earning
them pro몭t at the cost of innocent users’ privacy and security.
3. Phishing
ML-based spoof emails are more e몭ective and successful in stealing credentials. It is mainly
because ML algorithms are reasonably successful in creating instant messages and emails that
look like they are from trustworthy resources.
4. ML-based Password Generators
Common passcode guesses include-123456789, date of birth, name of your close family
members, etc. With most websites encouraging solid passwords, it has become a hard nut to
crack the users’ passwords.
However, ML-based algorithms have already been developed that guess passwords accurately.
PassGan is a famous example of password guessing software using the ML algorithm for the job.
So, we can expect more revolutionary password generators from hackers.
Wrapping Up
That was all about how Arti몭cial Intelligence and ML are in몭uencing the security of the digital
world. Under the right hands, they are a boon to humanity, but they can quickly turn into a bane
on the corrupt hands.
As for now, upgrade your security with these technologies to stay in the competition. Connect
with a Machine Learning company in India to maximize your cybersecurity.
Categories: Blog • Enterprise Technology
749 views
Varun Bhagat
PixelCrayons
Varun Bhagat is a technology geek and works as a Sr. IT Consultant with

7. Varun Bhagat is a technology geek and works as a Sr. IT Consultant with
PixelCrayons, a web & software development company in India.He possesses
in-depth knowledge of mobile app development & web development
technologies and helps clients to choose the best platforms as per their needs.
Author Rank: 22
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