AI and machine learning are increasingly shaping how cyber threats are detected and analyzed today. Instead of relying solely on known signatures, modern protection systems can recognize patterns, anomalies, and behaviors that may indicate an attack. See how this works in practice based on the technologies used by Bitdefender.
Bitdefender is a cybersecurity solutions provider used by individual users, businesses, and OEM partners. At that scale, protection cannot rely solely on manual analysis or simple file comparisons against a database of known threats. It requires mechanisms that can spot suspicious relationships faster and cope better with new, previously unknown attacks as well.
This is exactly where AI and machine learning play an important role. In Bitdefender technologies, they help analyze large volumes of data, identify relationships that are hard for humans to notice, and detect signals of a potential attack earlier. This is not a single feature or one model, but a whole set of methods that collectively strengthen protection effectiveness.
Although AI and ML often appear side by side, they do not mean exactly the same thing. Machine learning is part of the broader field of artificial intelligence and is used to make decisions or predictions based on data. In cybersecurity, this primarily means better threat detection before threats can cause real damage.
Why Isn't One Model Enough in Cybersecurity?
There is no single universal machine learning model in threat protection that works in every situation. Files are analyzed differently than process behavior, and anomalies at the level of a specific system require yet another approach. That is why Bitdefender uses different types of ML, matching them to specific tasks. This approach includes, among other things:
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deep learning models,
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large language models (LLMs),
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supervised learning,
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unsupervised learning,
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self-supervised learning.
If ready-made approaches do not solve a specific problem, Bitdefender Labs creates its own models tailored to that particular challenge.
How Does Bitdefender Use ML in Practice?
One example of deep learning in Bitdefender GravityZone is feature extraction. This means automatically pulling out important information from input data so the system can recognize characteristic patterns associated with malicious activity.
In on-demand scanning, this type of analysis helps detect malware based on many signals at the same time. These include, for example, API calls, patterns present in the code, file headers, or network behavior. As a result, detection does not rely solely on simple matching against known signatures, but above all on recognizing features typical of malicious software.
This approach clearly shows the advantage of models that learn from data. Instead of looking only for what has already been described, the system can identify behaviors and relationships that look suspicious even when there is not yet a ready-made definition of the threat.
HyperDetect and Process Behavior Analysis
Another example is the patented HyperDetect technology. In this case, Bitdefender combines supervised and unsupervised learning algorithms to analyze the behavior of running processes and catch activity that may indicate an attack.
This matters because some threats do not look like classic malware that can be recognized by a known code pattern. Instead, they reveal themselves only through the way they operate: unusual actions, suspicious relationships between processes, or sequences of behavior that deviate from the norm. HyperDetect was designed precisely to spot these kinds of signals.
This technology also uses large language models, which support the identification of potential threats by adjusting the model's decision boundary margin. As a result, HyperDetect can be more sensitive to new, previously unknown variants of malware.
Protection Against Fileless Attacks
One area where Bitdefender's proprietary models are particularly important is protection against fileless attacks. This type of cyberattack does not rely on launching a traditional malware file. Instead, it uses legitimate system tools such as PowerShell or the command line to carry out an infection or perform malicious actions.
That is exactly why such threats can be difficult for traditional antivirus solutions to detect. They do not always leave behind the typical trace of a suspicious file, and some of their activity may look like normal system operation.
How can you defend against this? The Bitdefender Labs team developed custom ML models capable of extracting features from command lines and PowerShell scripts. This makes it possible to detect patterns typical of attacks operating in memory and attacks built around tools that are legitimate in themselves. For this achievement, the company received the "Key Innovators" title awarded by the European Commission.
Detecting Anomalies at the Level of a Specific System
Another interesting part of Bitdefender's approach is custom solutions used to detect anomalies within a specific customer system. In this case, each such system receives its own individually trained ML model.
This type of solution monitors the behavior of a given system and compares it against various warning signals: MITRE® attack indicators, Bitdefender Labs' own indicators, and events specific to the given user. Over time, the model adapts to what is normal in that environment and what may indicate risk. The detected deviations are then reported to security teams.
This approach has strong practical value. Every organization operates a little differently, uses a different set of tools, and has its own activity patterns. That is why effective anomaly detection often requires not only knowledge of global attack techniques, but also an understanding of the local context of a specific system.
Scalability Is Also Part of Effectiveness
Bitdefender technologies operate across consumer, business, and OEM segments. On the one hand, this gives them very broad threat visibility. On the other, it places high demands on machine learning models.
It is not enough to build a model that works well in a lab or on one type of infrastructure. You also need to ensure that it maintains performance across very different hardware, from servers in data centers to home routers. That is why scalability here is not an extra, but a requirement for using AI and ML effectively across different environments.
What Do the Results Say?
The effectiveness of such solutions is best assessed by their results. Bitdefender points out that, thanks to the use of ML models and proprietary AI solutions, as early as 2014 it was able to identify the behavioral characteristics of WannaCry ransomware, one of the most high-profile encryption attacks of recent years. That was as much as three years before the threat appeared in the wild!
The company also regularly achieves very strong results in independent tests. In AV-Comparatives Advanced Threat Protection tests, Bitdefender consistently blocks threats before they are executed, with higher effectiveness than many competitors.
This Is a Good Time to Act: Use AI-Supported Protection in Your Company
The Bitdefender example clearly shows the direction effective protection is taking today. It is no longer just about detecting known threats, but about identifying signals that may indicate a problem more quickly.
Use this approach in your company - instead of reacting in panic only once an attack has already happened, it is better to act in advance and calmly choose protection suited to your environment. We can help you with that. Tell us what you need, and we will suggest which Bitdefender solutions will work best for you and handle the implementation.