Automation in cybersecurity is the answer to the growing volume of security events, which can no longer be analysed effectively by hand. Organisations are therefore increasingly using automation and deep learning, which enable real-time threat detection and rapid incident response. In this article, we explain how automated threat detection works, what its limitations are, and why human intervention remains irreplaceable even with advanced technologies.

Why is automation in cybersecurity essential?

Automation in cybersecurity is essential above all because the volume of threats and security data keeps growing. Manual checks would be inefficient and very hard to manage.

In addition, automated detection of cyberattacks is important for several other reasons:

• more complex and sophisticated cyberattacks,
• the limits of manual real-time monitoring,
• fewer false alarms,
• more efficient use of human resources.

Deep learning in a security context

Deep learning is an advanced form of machine learning. In cybersecurity it is used for automated threat detection and for analysing the behaviour of systems, devices and users. By using multi-layer neural networks, it can process large volumes of data and identify patterns typical of cyberattacks, phishing or malware.

The difference between machine learning and deep learning

Machine learning works with predefined features that a human prepares for the model. Deep learning, on the other hand, can derive these features by itself, automatically and directly from the data.

Put simply, deep learning can handle more complex and less obvious behaviour patterns. This property is extremely important in cybersecurity, especially for detecting new or unknown types of attacks.

How do models learn to recognise security threats?

Deep learning models are trained on large amounts of historical security data containing both normal and malicious behaviour. During training, the neural networks learn to distinguish between normal activity and anomalies that may indicate a cyberattack.

Benefits of automation and deep learning in detecting cyberattacks

One of the main benefits of AI in cybersecurity is the ability to detect different types of attacks in time. In practice, these technologies work in three basic ways:

1. Real-time anomaly detection

This is one of the most important benefits of security automation. Models using deep learning know the normal behaviour of applications and networks, so they can quite easily detect even the smallest anomalies. They do so in real time, minimising the impact of attacks and ensuring smooth operations.

2. User and device behaviour analysis

This analysis has several functions. It is used above all to monitor:

• login patterns,
• work with files,
• movement within the network,
• communication between devices.

If deviations appear in these areas that may indicate the presence of malware, the system starts dealing with them immediately. This approach is common in many areas, including SIEM (Security Information and Event Management) and EDR (Endpoint Detection and Response) solutions.ní typu SIEM (Security Information and Event Management) alebo EDR (Endpoint Detection and Response).

3. Predicting security incidents

Predictive security analytics also plays an important role. Its goal is to estimate the likelihood of future incidents based on historical data. Systems evaluate trends, recurring patterns and weak points in the infrastructure, which allows them to identify risks before an attack actually happens.

Automated incident response (SOAR) in practice

SOAR is an important part of modern cybersecurity that shortens the time between detecting a threat and acting on it. Combined with security automation, machine learning and SOAR tools, it is possible to respond to attacks almost instantly. In practice, this significantly reduces the extent of damage and limits the spread of an attack within the infrastructure.

• Isolating devices and restricting access – if, based on behaviour analysis, the system assesses an endpoint as risky, it can automatically restrict its network access or disconnect it completely.
• Automated security playbooks – these define specific procedures for responding to different types of incidents. When a threat arises, the system automatically launches the preset procedure.

Limits of automation and risks of deep learning in security

Although security automation has many advantages, it also has certain limits and risks. These include, for example, misjudging a situation and generating false alarms, as well as dependence on the quality of training data. If the data are incomplete or biased, models may learn the wrong behaviour patterns.

Automation does not replace people, but it changes their role

Although artificial intelligence can automatically detect threats and respond to incidents, it still cannot fully understand context, and it lacks strategic thinking and a sense of responsibility. Automation therefore mainly takes over routine and time-consuming tasks, but at present it cannot fully replace security analysts. They remain essential for interpreting results, investigating more complex incidents and setting security strategy.

The future of automation in cybersecurity

In the coming years, we can expect greater accuracy and closer cooperation between technology and people. Thanks to automation, threat detection should become faster and more accurate, leading to greater efficiency and productivity. Fewer false alarms and a stronger focus on data quality should also become the norm.

This significant shift will mean we can rely on artificial intelligence even more. Even so, it will not fully replace the human factor, which will remain essential in the years ahead.

This article is an output of the project “Multitenant Cybersecurity Operations Center Delivered as an Open Cloud Service with Machine Learning Elements.” Project code – 09I05-03-V02-00010.