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Proven AI cybersecurity technology since 2008

The Past: Building the Foundation​

We first embraced AI in 2008, both incorporating it into Bitdefender security solutions and contributing to the broader community through open research. This has helped us successfully predict and stop new and unknown threats.

  • A model we developed in 2014 successfully blocked the WannaCry ransomware attack in 2017, despite it exploiting a previously unknown vulnerability.

  • Bitdefender published 25,000 AI agents trained on 60 classic Atari games to help other researchers dealing with imitation learning.

Best Protection, Best Performance for business chart - AV Comparatives

Today: Making a Real-World Impact​

A one-size-fits-all approach fails in cybersecurity. That’s why we integrate a large and diverse set of machine learning (ML) models alongside non-AI technologies to address specific challenges efficiently. This approach has earned us top results in real-world protection and APT tests, XDR and MDR evaluations.

  • To identify the slightest variations, our anomaly detection builds a unique ML model for every user and device, not just organizations.

  • Our platform extracts over 60000 unique data points used by AI across our multiple security layers.

Dragos Gavrilut - VP of Threat Research

The Future: Ready for What’s Next​

At Bitdefender, we don’t just keep up with AI advancements, we help to shape them. With over 70 academic papers published and 50+ Bitdefender researchers teaching at universities, we are highly active in advancing AI to better tackle present and future challenges and threats.

  • With genetic algorithms we’re training AI models using a process inspired by natural selection, leading to better cybersecurity outcomes.

  • We use generative adversarial networks (GANs) with two AI teams in constant battle: one creates new breach methods, the other counters them. This sparring enables our AI to anticipate and neutralize threats when they emerge in the real world.

Explore Bitdefender AI on TechZone

Bitdefender AI Innovation Timeline

2008

First ML-based detection

Bitdefender leveraged ML to improve detection of new or unknown malware.

2011

First noise reduction algorithm

The noise detection algorithm helped identify misclassified samples.

2013

First ML-based automated stream detection

The first automated stream detection based on ML technologies.

2014

First use of deep learning

The first use of deep learning AI algorithms to increase detection rates.

2017

First tunable machine learning

Bitdefender HyperDetect enables organizations to fine-tune ML detection and stop advanced attacks at pre-execution.

2017

Fileless Attack Protection

Using custom ML models to perform feature extraction from command lines and PowerShell scripts stops file-less malware. This research earned us the “Key Innovators” title by the European Commission.

2020

Anomaly Detection in EDR

Anomaly Defense leverages AI technologies to build behavior baselines and spot anomalies with minimum noise.

2022

Native XDR with human-readable Incident Advisor

Bitdefender Native XDR uses ML to automatically correlate and consolidate threat signals across endpoints, identities, apps, network, clouds, mobile devices and beyond. The Incident Advisor answers all key analyst questions in a human-readable format.

2024

GravityZone AI Assistant

The Bitdefender GravityZone AI Assistant leverages Large
Language Models (LLMs) to streamline and simplify threat investigations by answering
analysts’ questions instantly.

180+

Top technology brands licensing Bitdefender technology.

60000+

Unique data points extracted and used by our machine learning algorithms.

400+

Threats discovered every minute.

50

Billion daily threat queries from hundreds of millions of systems.

See How Bitdefender tackles AI threats

We believe AI is more beneficial for defenders than attackers, but fear, uncertainty and doubt clouds reality.  Here at Bitdefender, we rely on science, not speculation.  We constantly re-evaluate attack vectors to ensure our security solutions stay ahead of the evolving AI threat landscape.

AI and cyber crime
  • 01

    AI-Enabled Social Engineering

    Social engineering attacks are early adopters of AI, making traditional defenses like user awareness even less effective. Technology bridges this gap. Our team of experts, focused on deepfakes, business email compromise, and fraud, keeps improving advanced security algorithms used in solutions and technologies like GravityZone Security for Email, Network Protection, or our XDR sensor for productivity applications. 

  • 02

    AI-Generated Malware and Ransomware

    The rise of AI-generated malware might sound scary, but our layered security approach is built to scale. We detect malicious behavior, even in previously unseen variations of existing malware. In our telemetry, we identify over 500,000 new threats daily (over 400 per minute), with our systems efficiently handling this high volume.  As AI-generated code fuels a surge in unique malware variations, a strong foundation for your multi-layered security, delivered by the GravityZone platform, becomes even more critical.

  • 03

    Automated Attacks and Reconnaissance

    The window between a vulnerability's discovery and patch deployment is shrinking, often lasting less than 24 hours. This creates a prime target for automated opportunistic attacks that exploit unpatched internet-exposed devices.  
     
    Risk management and Patch management solutions provide a clear picture of your vulnerabilities, allowing you to prioritize patching and mitigate risks from exposed devices. But security works in layers.  Even with strong prevention, attackers might find a way in. That's where minimizing dwell time becomes crucial with solutions like GravityZone XDR or Bitdefender MDR.  .

Proven. Unsurpassed Cybersecurity Effectiveness.

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