A necessary evil in the fight against malware systems


Artificial intelligence has revolutionized all sectors with its great capacity to process information. In the cybersecurity space, it is capable of increasing the detection, range and precision of cyberattacks.

In this article, we are going to discuss the most important characteristics of artificial intelligence and its relationship with cybersecurity, as well as the challenges that it can present if not properly managed.

Characteristics of artificial intelligence in relation to cybersecurity

Prevention and detection of attacks

Through machine learning algorithms we are able to make predictions from past events and it is possible to consider infinite data and scenarios to identify probable events, in order to locate parameters faster, where a potential attack can hide. Machine learning systems can establish security protocols depending on the type of intrusion into the company’s systems.

Automation processing

AI allows automating processes with a large volume of information, maintaining a high level of protection, which contributes to optimizing actions and improving performance.

Protection of information, assets and infrastructure

It allows to identify vulnerabilities in the company’s technological ecosystem, being an advantage when facing unknown potentially dangerous behaviours.

Optimization of human resources

By automating processes, artificial intelligence allows human assets to be dedicated to more complex critical tasks, while the system deals with those basic and repetitive actions.

This interpretive capacity generates more intelligence from the analysis of the information, with good data and the AI application in the identification of patterns and predictions, allowing more complex and valuable information to be extracted from the data.

Steady improvement of the systems

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Through these intelligent systems, the use of data can constantly evolve, because it uses multi-layered neural networks that are used to build more complex and effective interpretive structures.

Artificial intelligence and cybersecurity

The number of cyberattacks on companies, governments, organizations and individuals is increasing, as is its speed and complexity, overwhelming cybersecurity professionals. The impact of the attacks is estimated to reach 6 trillion by 2021, an alarming figure for all sectors.

Artificial intelligence, as we have already mentioned, can analyse and learn from data with greater efficiency and precision through intelligent algorithms. This feature makes it a priority and a great ally for cybersecurity.

Based on a study by the Capgemini Research Institute, 69% of companies acknowledge that they will not be able to respond to critical threats without AI, this due to the increase in end-user devices, the growth of networks and user interfaces, IoT, 5G transformation, among others.

In cybersecurity, natural language processing allows analysts to respond to threats with greater confidence, speed and great capacity to protect endpoints, data and networks. For example, through a heuristic algorithm, based on a database of benign and malicious code traits, AI involved attempts to make decisions about whether or not the analysed code is harmful.

One of the great advantages of AI systems is that they collaborate by categorizing attacks according to the level of the threat and thus being able to assign the priority with which each incident should be given attention respectively. AI enables the behaviour of ransomware and malware attacks to be identified before they enter the system, thus isolating and mitigating them. Some of the challenges related to the application of machine learning in this arena are:

  1. AI-powered cyber attacks
  2. Anti-VM and Anti-sandbox malware
  3. Ransomware and IoT
  4. Increase in State / Government-sponsored attacks
  5. IT infrastructure
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Some of the steps to build a path to implement AI in cybersecurity are:

  1. Create a data platform: Identify the data sources and create data platforms to operationalize AI/ML.
  2. Collaboration: Collaborate with external parties to enhance the ability to respond.
  3. Training for analysts: Providetrainingto your manpower in order to be AI-savvy.

What are today’s best practices?

Artificial intelligence is undoubtedly changing and making many of the security protocols efficient, but it is extremely important to maintain a security position that includes continuous monitoring, user training, management of software updates and basic controls.

  1. Identification of patterns: the algorithm focuses on patterns that provide information such as network vulnerabilities, anomalies, irregular types of network traffic, unauthorized user logins, and other red flags. Administrators can configure their systems to automatically detect when it occurs, to prevent more traditional attacks, such as malware or phishing.
  2. User education: Training users about what not to do is essential, it is as important as putting in place systems for the protection and security of the organization’s information. Likewise, it is very important to have a response and recovery plan that the staff knows.
  3. Patch holes or vulnerabilities: Failure to apply software updates or address vulnerabilities opens the door to potential attacks.

Machine Learning algorithms are seemingly dominating the industry, so knowing its applicability will guarantee a more robust infrastructure for the security of your company’s data.

The article has been written by Dr. Raul V. Rodriguez, Dean, Woxsen School of Business

He can be reached on LinkedIn.



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