Artificial Intelligence in Cybersecurity: The Culture of the Future



Have you ever wondered what intelligence means? This characteristic of humans encompasses – but it’s not limited to – learning, reasoning, problem-solving, perception and the use of language. These are also the characteristics that AI researchers have focused on, in order to find solutions for various aspects of our society today. The one we’ll focus on today is the use of artificial intelligence in cybersecurity.

Artificial Intelligence in Cybersecurity – Definition of AI

Encyclopaedia Britannica describes artificial intelligence as “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.” 

A few examples of AI that everyone might have heard of are the following: smart assistants like Siri and Alexa, drone robots, chatbots, email spam filters, songs or TV recommendations from Spotify and Netflix. 

Artificial intelligence falls under two main categories: 

Narrow AI 

Narrow artificial intelligence is a simulation of human intelligence and operates only within a limited context. Usually, even if narrow AI machines seem intelligent, they are only focused on performing a single task very efficiently. 

Artificial General Intelligence 

Artificial general intelligence refers to machines that have general intelligence and that are able to apply that intelligence to solve a problem. 

Moreover, AI is divided into various types: 

artificial intelligence in cybersecurity concept image


1. Reactive machines 

These are the most basic types of AI machines, purely reactive, without the ability to form memories or use past experiences for current situations. This does not mean they are not impressive: IBM’s Deep Blue, which beat international chess grandmaster Garry Kasparov in the 1990s, was a reactive machine. It perceived the world directly, acted on what it saw, and it did not rely on an internal concept of the world. 

2. Limited memory 

This second type of AI includes machines that can also look into the past. This behaviour can be now encountered at self-driving cars, who need to monitor other traffic participants’ speed and direction, but also at chatbots and virtual assistants. 

3. Theory of mind

In psychology, the theory of mind refers to the understanding that people and creatures have thoughts and emotions that influence their behaviour. The theory of mind is crucial for the formation of our society since it allows us to have social interactions: it would be very difficult for us to collaborate without taking into account somebody else’s motives, intentions and knowledge about us and the environment. AI machines from this third type should also adjust their behaviour according to these factors. 

4. Self-awareness 

Self-aware machines would represent the last step of AI development. Self-aware/conscious beings are aware of themselves, of their internal states and are able to predict what others (would) feel. 

Artificial Intelligence in Cybersecurity – Challenges 

Cybersecurity evolves constantly, but it still faces challenges, as The Computer Society nicely describes them: 

Geographically-distant IT systems—geographical distance makes manual tracking of incidents more difficult. Cybersecurity experts need to overcome differences in infrastructure to successfully monitor incidents across regions.

Manual threat hunting—can be expensive and time-consuming, resulting in more unnoticed attacks.

Reactive nature of cybersecurity—companies can resolve problems only after they have already happened. Predicting threats before they occur is a great challenge for security experts.

Hackers often hide and change their IP addresses—hackers use different programs like Virtual Private Networks (VPN), Proxy servers, Tor browsers, and more. These programs help hackers stay anonymous and undetected.

In these conditions, AI’s help is more than welcome.

Artificial Intelligence in Cybersecurity – How Can It Help

AI and ML (machine learning, a subset of AI that uses statistical techniques to give computer systems the ability to learn using data, without programming) have become crucial technologies in cybersecurity because they can learn over time and have the ability to detect and respond to deviations from established norms. 

Artificial intelligence can analyze millions of events and detect various kinds of cyber threats, from malware to phishing attempts – the more data they analyze, the smarter they get. 

AI machines can help cybersecurity solutions by dealing with: 

human error. Even the most experienced IT team can get tired if they have to deal with too many manual processes. Responsive tools could help them find and mitigate issues easier. 

repeated activities. Manual processes can’t be perfectly repeated each time, so it’s best that AI and ML systems deal with repetitive activities. 

threat alert fatigue. A decent cybersecurity strategy should imply more layers – this means more sources of threat alerts, which could lead to decision fatigue if the security personnel is the one who must decide what should be done. 

threat response time. Threat response time is one of the parameters of efficiency in cybersecurity. Most damages from cyberattacks happen in only a few seconds, so anyone and anything that aims to offer detection and protection must work even faster. Unfortunately, human response to a detected threat can sometimes be slow. AI and ML technologies can gather data immediately and prepare them for analysis, offering simplified reports and recommended actions.  

new threats identification and prediction. New, unknown cyber threats are dangerous also because they can deceive IT teams and slow down their reactions or they can go completely undiscovered. ML can find similarities between possible new threats and previously identified ones more efficiently. 

artificial intelligence in cybersecurity

Artificial Intelligence in Cybersecurity – Benefits 

The use of AI in cybersecurity software offers great advantages, like: 

a. Better detection of cyberattacks 

The behavioural analysis used by AI technologies allows companies to prevent online threats from happening in the first place. With AI, the focus can shift from remediation to prevention. 

b. Accurate predictions

AI allows better predictions regarding possible future attacks because its algorithms can swiftly scan data and analyze it according to how the system is trained. 

c. Faster response 

Time is one crucial element in cybersecurity, since most of the time, it only takes a few seconds of not paying attention to become the victim of a cybersecurity threat. AI allows companies to take automatic (ergo, faster) countermeasures to prevent cyberattacks and fight against online threats.  

Artificial Intelligence in Cybersecurity – Drawbacks

Artificial intelligence can make cybersecurity solutions more efficient and save companies from all the problems that a cyberattack might involve: loss of time, loss of revenue, loss of clients, reputation damage, legal measures. 

However, there are certain drawbacks

Resources—companies need to invest a lot of time and money in resources like computing power, memory, and data to build and maintain AI systems.

Data sets—AI models are trained with learning data sets. Security teams need to get their hands on many different data sets of malicious codes, malware codes, and anomalies. Some companies just don’t have the resources and time to obtain all of these accurate data sets.

Hackers also use AI—attackers test and improve their malware to make it resistant to AI-based security tools. Hackers learn from existing AI tools to develop more advanced attacks and attack traditional security systems or even AI-boosted systems.

Neural fuzzing—fuzzing is the process of testing large amounts of random input data within the software to identify its vulnerabilities. Neural fuzzing leverages AI to quickly test large amounts of random inputs. However, fuzzing has also a constructive side. Hackers can learn about the weaknesses of a target system by gathering information with the power of neural networks. 

Moreover, hackers can baffle security algorithms by mixing up the data they train on and the warning signs they look for. 

There might be problems with the data too – there might simply not be enough data (so AI solutions will deliver inaccurate results and even false positives) or cybercriminals may manipulate it. 

Artificial Intelligence in Cybersecurity – How Is it Used 

As we’ve already seen, artificial intelligence in cybersecurity could be helpful in multiple ways. Let’s see exactly how it is used by cybersecurity companies nowadays: 

  • for separating critical issues from regular network activity, identifying activities that might result in attacks
  • for profiling and detecting threats, privilege abuse and compromised accounts
  • for threat hunting in order to prevent attacks 
  • for protecting their clients’ email communication
  • for predicting security breaches and stopping malicious bot activity 
  • for identifying patterns and detecting deviations from typical behaviour for preventing threats 
  • for reversing cyber attacks
  • for scanning metadata and traffic information and alerting administrators to potentially malicious activities. 

How We Use AI 

Artificial Intelligence is a basic component of the Heimdal™ suite: 

Our Heimdal™ Threat Prevention solution, with its two components, Network Protection and Endpoint Protection, includes the The DarkLayer GUARD™  and VectorN Detection™ modules, which have AI at their core. DarkLayer GUARD™ is fueled by our AI driven “Character-Based” Neural networks intelligence and has a 96% accurate ability to predict tomorrow’s threats today.  

The DarkLayer GUARD™ endpoint engine is further the world’s most advanced Endpoint DNS threat hunting tool and boasts our threat to process correlation technology allowing you to spot processes, users, URLs and attacker origins used to infiltrate your network.

The DarkLayer GUARD™ filter works in tandem with our VectorN Detection™ AI-based traffic pattern recognition engine to also give you HIPS/HIDS and IOA/IOC capabilities and spot hidden malware, completely autonomous of code and signatures.

With their help, you can hunt, detect, respond to and prevent APTs, data leaks, ransomware, network malware.

Our Heimdal™ Next-gen Endpoint Antivirus uses Heuristic, behaviour-based engines powered by artificial intelligence to monitor processes and process changes and it uses 4 stages of scanning to detect and identify even the most advanced threats – Local File/Signature & Registry scanning, Real-Time Cloud Scanning, Sandbox and backdoor inspection, Process Behaviour-based scanning. 

Artificial intelligence is also present in our products that deal with email security. Heimdal™ Fraud Prevention monitors communication for false communication and malicious emails in order to prevent CEO and financial fraud and spot Insider Business Email Compromise, and it does so by using 125 detection vectors. The most important are: phraseology changes, IBAN / account number scanning, attachment modification, link execution and scanning, man-in-the-email detection. MailSentry Fraud Prevention is actually able to learn the senders’ communication patterns, in order to detect the smallest modifications. 

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is the next-level mail protection system which secures all your
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  • Deep content scanning for attachments and links;
  • Phishing, spear phishing and man-in-the-email attacks;
  • Advanced spam filters which protect against sophisticated attacks;
  • Fraud prevention system against Business Email Compromise (BEC);

Offer valid only for companies.

You can find more details about this topic by watching one of our previous webinars, Step into the Future of DNS Security: A Talk with our AI Pioneer on Breaking the Sound Barrier

Artificial Intelligence in Cybersecurity – Wrapping Up

Artificial intelligence is a subject that has fascinated us for a long time – although mathematician Alan Turing launched the question “Can machines think?” in the 1950s, traces of people wondering what would happen if they tried to produce a rational creature go back to 1818, when Mary Shelley wrote Frankenstein. Like technology itself, artificial intelligence can be used for good or for bad, it can help in great ways or, in the wrong hands, it can provoke serious damages – it’s only up to us what we make of it. 

However you choose to make use of artificial intelligence in the cybersecurity domain, please remember that Heimdal™ Security always has your back and that our team is here to help you protect your home and your company and to create a cybersecurity culture to the benefit of anyone who wants to learn more about it. 

Drop a line below if you have any comments, questions or suggestions – we are all ears and can’t wait to hear your opinion!


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