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The Impact of AI and Machine Learning on Cybersecurity | Everything You Need to Know

Last updated on 04th Nov 2022, Artciles, Blog

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Mithelesh (Cloud Security Specialist )

Mithelesh is the Senior Cloud Security Specialist in AWS Athena, CSV, JSON, ORC, Apache Parquet, and Avro. He has skills with PostgreSQL RDS, DynamoDB, MongoDB, QLDB, Atlas AWS, and Elastic Beanstalk PaaS.

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    • In this article you will learn:
    • 1.Introduction to an AI and Machine Learning on a Cybersecurity.
    • 2.AI and Cyber Security.
    • 3.Main Challenges Cybersecurity Faces Today.
    • 4.How AI Improves a Cybersecurity.
    • 5.Drawbacks and Limitations of Using AI for a Cybersecurity.
    • 6.Conclusion.

Introduction to an AI and Machine Learning on a Cybersecurity:

Experts are agree that a Artificial Intelligence (AI) and Machine Learning (ML) have both the negative and positive effects on a cyber security. AI algorithms use a training data to learn how to respond to various situations. As they progress, they learn by a copying and adding additional information. This article reviews a positive and negative effects of AI on cyber security.

AI and Cyber Security:

  • Cyber security is one of many uses of an artificial intelligence. A report by a Norton showed that the global cost of a typical data breach recovery is $3.86 million.
  • The report also states that a companies need an average of 196 days to recover from the any data breach. For this reason, organizations should invest a more in AI to avoid wastage of a time and financial losses.
  • AI machine learning and a threat intelligence can recognize a patterns in data to enable security systems to learn from a past experience. In addition AI and machine learning enable the companies to reduce an incident response times and adhere to a security best practises.

Main Challenges Cybersecurity Faces Today:

Despite advances ina cyber security, attacks are becoming more and more dangerous. The main challenges of a cyber security include:

Geographically distant IT systems— a Geographical distance makes a manual tracking of incidents more complex . a Cyber security experts need to bridge a gap in infrastructure to successfully monitor the incidents across sectors.

Manual Threat Hunting – can be a costly and time-consuming, resulting in a more going unnoticed.

Reactive nature of cyber security – Companies can solve a problems only when they have already been happened. Predicting threats before they occur is the major challenge for a security experts.

Hackers often hide and change their IP addresses— Hackers use a different programs such as Virtual Private Networks (VPNs), proxy servers, a Tor browser and more. These programs are help hackers to remain for anonymous and undetected.

Machine Learning on Cybersecurity

How AI Improves a Cybersecurity:

Threat hunting:

  • Traditional security techniques use a signatures or indicators of the compromise to identify threats. This technique may work well for a threats already encountered, but they are not be effective for threats that have not yet discovered.
  • The signature based on technology can detect about 90% of a threats. Replacing traditional techniques with an AI can increase the detection rate to 95%, but will get an explosion of false positives.
  • The best solution would be to combine the both traditional methods and AI. This can result in 100% detection rate and reduce a false positives.
  • Companies can also use the AI to enhance a threat hunting process by integrating behavioral analysis. For example, can leverage AI models to develop profiles of the each application within an organization’s network by a processing high amounts of an endpoint data.

Vulnerability management:

  • There were a 20,362 new vulnerabilities reported in a 2019 a 17.8% increase over 2018. An Organisations are struggling to be prioritise and manage the massive amount of a new vulnerabilities they encounter on daily basis.
  • Traditional vulnerability management methods are wait for hackers to exploit a high-risk vulnerabilities before deactivating them.
  • While traditional vulnerability databases are the critical to managing and containing known vulnerabilities, AI and machine learning techniques such as a User and Event Behavioural Analytics (UEBA) can analyze a baseline behavior of user accounts, endpoints, servers and identify anomalous behavior.
  • Can identify what may be indicate a zero-day unknown attack. This can help keep to organisations secure even before vulnerabilities are officially reported and also patched.
Benefits of using AI in Cybersecurity

Data centers:

AI can optimise and monitor more essential data center processes like backup power, cooling filters, power consumption, internal temperature and bandwidth usage. AI’s computational powers and a continuous monitoring capabilities provide the insight into what values ​​will improve an effectiveness and security of a hardware and infrastructure.

Network security:

There are two time-intensive aspects of a traditional network security, creating security policies and understanding the organization’s network topography:

  • Policies—Security policies are identify which network connections are be legitimate and which should monitor for a malicious behavior. Can use these policies to effectively implement zero-trust model. The real challenge lies in formulating and maintaining policies in view of large number of networks.
  • Topography—Most organizations do not have a precise naming conventions for the applications and workloads. As a result, security teams spend a lot of time determining what type of a workload a given application is be concerned with.
  • Companies can leverage AI to improve the network security by learning a network traffic patterns and recommending functional set of workloads and also security policy.

Drawbacks and Limitations of Using AI for a Cybersecurity:

Resources— Companies need to invest the lot of time and money into resources are computing power, memory, and data to build and maintain AI systems.

Data set- AI model is trained with a learning data set. Security teams need to get their hands on many various data sets of malicious code, malware code, and anomalies. Some companies are do not have a resources and time to obtain all these accurate data sets.

Hackers- also use a AI-attackers’ tests and improve their malware to made it resistant to AI-based security tools. Hackers are learn from existing AI tools to develop a more advanced attacks and attack traditional security systems or an even AI-boosted systems.

Neural Fuzzing – Fuzzing is a process of testing large amounts of a random input data within software to identify its weaknesses. Neural Fuzzing leverages AI to quickly test a large amounts of random input. However, a fuzzing has a creative side as a well. Hackers can learn about weaknesses of a target system by gathering information with the power of a neural networks. A Microsoft developed a method for implementing this approach to improve software, resulting in a more secure code that is harder to use.


Artificial intelligence and machine learning can improve the security, as well as make it simpler for a cybercriminals to systems without any human intervention. This can cause a lot of loss to the any company. It is highly recommended to get a some sort of protection against cybercriminals if need to minimize losses and stay in a business.

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