How Is AI In Cybersecurity Changing The Important Landscape?

The digitalization has skyrocketed in the pandemic as it has brought about numerous benefits such as the improved agility in the businesses as well as in the customer experiences. Additionally, there have also been specific negative effects such as the increased vulnerabilities to the cybersecurity threats for both your data and applications. Use of AI in Cybersecurity can both serve as a boon and bane for the industry as it is getting accepted in the cyber world.

A cyberattack can be termed as a malicious and deliberate attempt for breaching the computer and the information systems of an organization or an individual disturbing the victim’s network for personal gains.

However, one of the biggest concerns with the inclusion of AI in cyber security and its development is the probability of the attackers weaponizing AI and utilizing it to expand and boost their cyberattacks. This is one of the major disadvantages of AI in Cybersecurity more information about these are discussed later). Cybersecurity and cybercrime landscapes are changing rapidly. Boosting the Artificial Intelligence developments for improvising the cybersecurity will be a major gamechanger for protecting against the cyberattacks.

How Is Cybersecurity Landscape Changing With The Inclusion Of AI?

AI has been a beneficial invention for both cybersecurity and mankind as a whole. This article will focus on some of the ways in which AI is modifying the cybersecurity landscape and talk in detail regarding the benefits of using Artificial Intelligence in Cybersecurity.

Cyber Threat Detection

Machine Learning, in short termed as ML, is a subset of Artificial Intelligence (AI) that permits the real-time detection of the cyber threats depending on the analysis of the complete organizational data. The algorithms of machine learning are continuously updating itself and getting adapted for identifying the threats to your system prior to its vulnerabilities getting exploited. These algorithms are capable of understanding all the aspects of an organization’s infrastructure and the network. Additionally, it also understands the possible attack scenarios.

That being said, with constant training and updating (learning, analysing data and understanding the behavioral patterns), the machine learning algorithms will slowly get better at predicting and eliminating the cyber threats. They can thwart the cyber threats before they can attack.

User Behavioral Analysis

Utilizing the Artificial Intelligence and Machine Learning algorithms, these can analyze the user activity on various devices and the web platforms. AI and Machine Learning in cybersecurity usage can be done to understand the behavioral patterns and the user activities. In case if there is any signal of the unusual activity, then the activity will be flagged as unusual activity or marked as suspicious.

The algorithms function by looking for the user patterns such as employee working hours, device IDs, the geographical locations that have been utilized for logging into an account or network, scrolling or typing patterns on those devices etc.

After a normal user behavioral benchmark has been successfully set or a benchmark for the normal traffic of an organization has been determined, the algorithms become capable of detecting the activities that fail to abide by this regular pattern and simultaneously take further actions.

Fraud Detection

It is a fact that the traditional fraud detection depends on the rules that have already been defined. This can result in a huge number of false positives such as blocking the legit transactions and thereby increasing the possibility of losing customers. Machine Learning algorithms are also used to enhance this process with their abilities to scan large amounts of the transactional data and individual patterns.

Thus, AI and ML in cybersecurity are accelerating the real-time fraud detection and heavily reducing the possibilities of the false positives. The AI-powered fraud detection is also capable of implementing the root cause analysis and trace the origin of the fraudulent transactions to their sources such as ATMs or credit cards. The improved anomaly detection of AI is beneficial for the businesses as well as the financial institutions. It helps to build a trustworthy reputation and enhance the customer relationships.

Access & Identity Management

Passwords serve as a fragile security method and often they are the sole barrier between the user accounts and the hackers. When setting and updating passwords is concerned, most people tend to use the same password over and over again on multiple platforms and that too for many years. Some are often found to store their passwords in a note file on their computers. This careless password management paves an easier way for the cybercriminals to acquire access to various accounts.

These days, biometric authentication has been introduced and successfully implemented as an alternative to the weak passwords. However, the cybercriminals have found a way to get past this system as well. AI algorithms are being developed for strengthening biometric authentication.

In order to prevent all the suspicious access, AI algorithms curate a model for the user’s face (facial identification) and identify the main patterns and correlations. The algorithm will also perform when the users are exposed to low lighting or wearing accessories such as hats. This makes it a much easier and secure method of protecting your accounts.

Malware Detection

Malware is an intrusive software that includes trojan, viruses, spyware and ransomware. Usually, they are delivered over emails in the form of a file or link and are designed for causing extensive damage to the data and system of your organization.

The traditional machine learning approach used feature engineering for analysing and extracting the features of the malware program and comparing them against the default set in order to identify if it is a malicious programme. The bad part is that this method excludes the evolving forms of malware.

The complex neural networks and the deep learning algorithms are continuously being developed for countering these increasingly sophisticated malware threats. Such algorithms are designed to analyse and compare the dynamic aspects of a malware program with respect to the static features of it in the bid to identify the abnormalities and block the malware.

Vulnerability Management

With the increase in the complexity of networks, the role of Artificial Intelligence in Cybersecurity can reduce the progressing burden on the enterprise vulnerability management operations together with the automation and intelligent decision making.

AI can aid in developing the context-based vulnerability risk scores, actively identifying the vulnerabilities and leveraging the sentiment analysis to comprehend the vulnerability exploitation trends. This would dramatically reduce the human effort that is required in assessing the risks and proactively shield the vulnerable assets serving as one of the major advantages of AI in Cybersecurity.

Disadvantages Of Implementing Artificial Intelligence In Cybersecurity

Although Artificial Intelligence answers and tries to address most of the cybersecurity concerns, it is not an ultimate fix. At this moment, AI is difficult to implement on a small scale and the technology in itself seeks for improvements. We can opine that the ups far outweigh the downs and it still does not nullify the fact that there are various drawbacks of the AI solutions as compared to the human beings.

Here are some of the major disadvantages of implementing AI in Cybersecurity.

• The cybercriminals are getting AI savvy as well and thus they can use the AI solutions to conduct illicit activities. The individuals and the companisevarte less likely to be benefitted from the experience of the hackers. But it is possible that they can potentially tap into the progress of the organizations and reverse their findings for creating a better threat.
• The cyber threats continue to evolve, even if you implement AI to your business. This does not imply that you basically become immune to all threats. Malware and viruses improve all the time and this calls for the constant upgradation and redesigning of the AI including their timely maintenance.
• Artificial Intelligence faces a high adoption barrier. It still requires a lot of human resources as well as computing power as compared to the typical antiviruses. You would simply install a ready-made software rather than investing money and time on building customized AI solutions.
• The biometric recognition that is powered by Artificial Intelligence can prove to be hazardous as well. The advanced scanning methods can offer the third parties with exact data on your appearance. This could be misused for privacy monitoring, tracking and other related violations. These methods could be readily used by the repressive countries and the governments for tracking their adversaries.
• The cost of recruiting a team of AI experts at the moment is still high. A couple of these technologies are still in the development phase and it would be riskier to completely depend on them.
• AI has made it easier to collect and process even more data than before. This permits the third party companies to acquire even more data about the users that can result in additional privacy and protection issues.

How AI In Cybersecurity Will Be In 2022 & Beyond?

The following is what you can expect in 2022 and beyond in the frontiers of cybersecurity and AI software. That being said, the various types of cyber security are going to be modified in the coming years.

Predictions For AI In 2022 & Beyond

• There is possibly a big movement on the semiconductor side and especially with the companies like ASNL that already supplies to the semiconductor manufacturing companies. There will be a speed in the acquisitions. Some of the smaller players in the AI semiconductor space might get acquired by the bigger players and some of the discussions related to this are already in progress like the Nvidia Arm Discussion. More of such discussions are also expected.
• Deep mine will continue to build on the work that they have done with Brooklyn Folding. So you need to keep an eye on the next big thing.
• The next AI trends in cybersecurity prediction is more focussed on the governance side. For a while now, there have been rigorous discussions going on about AI governance at the principal levels. However, the countries are now going into nuances of it where they are talking about the individual use case level. The new and upgraded responsible AI principles can actually be put into perspectives with specific and practical AI in Cybersecurity use cases. So, they are not just responsible AI principles on a beautiful white paper but are actually adopted and communists can actually demonstrate through multiple ways that they are developing and deploying their AI applications responsibly. Nevertheless, in 2022, more countries are expected to come out with their guidelines on how to achieve that.

Cybersecurity Trends In 2022 & Beyond

The future of cybersecurity seems promising. Starting from 2022, integration of cyber resilience and cybersecurity into the overall enterprise are anticipated. Risk management and risk management models that are getting pretty encouraging meaning that the enterprises are going to reach the highest levels of organizations. It is expected that ransomware will not be in the minds of the people but it will be there practically.

This being said, the enterprises are required to be prepared for the new generation of the cyberattacks and have to be at par with their cybersecurity teams. They need to be aware of their individual roles in protecting the organization and the important processes to be followed. The same thing needs to be mentioned for individual security as well. Additionally, you need to keep a track of the following:

• How much data is there online and where exactly on the internet?
• If you are using the 2FA or two-factor authentication.
• If you are engaging in using strong passwords.
• If you are securing your digital footprint actions.

Final Words

In addition to all of the listed domains, AI in Cybersecurity can also be utilized across the endpoint protection, bot spam, security recommendations and several related areas. The progressiveness of the artificial intelligence solutions is helping both sides in cyberspace viz., the defenders and the attackers.

As AI is continuing to evolve and the future of AI in Cybersecurity is quite prospective, the organizations are required to upgrade their cybersecurity tools and processes for proactively blocking the threats and simultaneously protecting their data and systems. Choosing the proper technology and building strong security procedures will help you in avoiding crises and stay a step ahead of the potential attackers.

Cyber security in India is also evolving at an equal pace just like globally. As new technologies are getting implemented, the companies need to be at par with these technologies and how they work. They need to take help from the professionals via the top cybersecurity company in India to stay safe.