How we use AI
We are redefining
the boundaries of cybersecurity by enhancing it with Artificial Intelligence.
AI transforms cybersecurity. Our AI-driven systems anticipate threats, adapt to your environment, and safeguard your data with privacy at its core, before breaches occur.
Innovation in machine learning and agentic automation drives this shift, creating security that defends, truly understands and evolves with you. In real time.
Redefining Cybersecurity
with Agentic AI
By integrating agentic AI in cybersecurity we enhance detection, adaptability, and real-time response, thus defining a new standard in online scam prevention.
Going Beyond Traditional Scans
AI-powered cybersecurity delivers human-focused, context-aware protection that anticipates user needs and supports them at every stage of a scam attempt.
Mapping Emerging Threat Landscapes
AI detects, analyses, and blocks mobile scam campaigns before they spread, protecting users across regions.
Reconstructing the Defence Narrative
Combining machine learning and behavioral analysis simplifies the detection of recurring scam patterns, blocks threats in real time, and anticipates future tactics.
Detecting More Complex
Scam Patterns
Correlating signals across devices and networks exposes unified attack campaigns spanning emails, fake domains, social media, and messaging apps.
Exploring the Future of
AI-Driven Cybersecurity
Deepfakes are the new battleground in cybersecurity, a rapidly growing threat driving the next wave of adaptive, AI-powered defense innovation.
How Bitdefender harnesses the power of AI for decades
Innovation at the core of digital life protection.
First ML-based detection
Bitdefender leveraged ML to improve detection of new or unknown malware.
First noise reduction algorithm
The noise detection algorithm helped identify misclassified samples improving detection accuracy across the engines.
First ML-based automated stream detection
The first automated stream detection based on ML technologies to identify threats before they reach the endpoint.
First use of deep learning
The first use of deep learning AI algorithms to increase detection rates of sophisticated malware that evades signature and heuristic-based defenses.
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.
Deep learning ML models for malicious email detection
Advanced deep learning–based machine learning models designed to identify and block malicious emails by analyzing patterns in content, metadata, and sender behavior.
Prediction neural networks for malicious app detection
Utilizes layered deep learning models trained on extensive behavioral and static app features to predict malicious intent before execution
Extended ML models for scam types classification
Enhancing scam detection accuracy by leveraging advanced machine learning architectures
AI App anomaly detection
A cloud-based deep learning system for detecting anomalous and malicious application behavior.
AI Assisted Scam Detection
By leveraging Bitdefender’s threat detection technologies and augmenting their power with the versatility and comprehension capabilities of AI agents we were able to research a new approach to Online Scams prevention.
Threat Wave Clustering
Deep learning models that cluster threat detections with similar indicators of compromise, revealing how scams appear and spread.
AI Voice Honeypots
Our synthetic personas engage fraudsters like real victims, identify manipulation techniques, and methodically extract indicators across the attack.
How Bitdefender redefines cybersecurity through AI research
Science that drives
progress
With over 70 academic papers published and 50+ researchers teaching at top universities, Bitdefender sets new standards in integrating AI in cybersecurity.
DeepFake Detection
An AI-enhanced technology focused on identifying and analysing manipulated media through advanced machine learning.
Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learnin
DeCLIP: Decoding CLIP representations for deepfake localization
Towards generalisable and calibrated audio deepfake detection with self-supervised representations
Weakly-supervised deepfake localization in diffusion-generated images
LLM for Assembly
Models fine-tuned on assembly code to improve feature quality and accuracy in tasks like anomaly detection, search, and classification.
Large Language Models for Malware Analysis
C-ing Clearly: Enhanced Binary Code Explanations using C code
Bitdefender joined the Global Anti-Scam Alliance as a Foundation Member
Global Anti-Scam Alliance (GASA) unites organisations, experts, and law enforcement to protect consumers worldwide from scams.
AI is not the future
in cybersecurity.
It's the reality of today.
The growing use of AI has transformed both sides of the cybersecurity battlefield; while criminals exploit it to enhance deception and scale their attacks, defenders harness the same technology to anticipate, detect, and neutralize those threats faster than ever before.
$1.026 trillion
Global Financial Impact
Scams have now reached a critical level globally, with total financial losses estimated at $1.026 trillion, equating to 1.05% of global GDP. This highlights an intensifying threat landscape, where scams affect more than just individual victims; they have broad economic implications as well. *
2+ billion victims
Victimization Rates
The GASA report reveals that approximately 25.5% of people globally have experienced financial loss due to scams in the last year. This translates to 1 in 4 individuals being affected, demonstrating the pervasiveness of fraudulent activities. *
* According to Global Anti-Scam Alliance 2024 Report
Scams.
The Leading Cyber Threat of Today.
In the age of AI, scams are becoming more sophisticated, more convincing, and harder to detect.
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Crypto Scams & Fake Trading Platforms Fraudulent investment opportunities promising high returns, often using fake websites, influencers, or AI-generated personas. |
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Deepfake Scams AI-generated videos, voices, or profiles used to impersonate trusted people for fraud or deception. |
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Employment Scams Fake job offers that steal data or money while posing as legitimate recruiters. |
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Phishing & Variants Deceptive messages that impersonate trusted sources to steal credentials or financial data. Often, these are just the first step in larger cyberattacks. |
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Ransomware & Extortion Attacks Malware that encrypts files and demands payment, sometimes threatening to leak stolen data if the ransom isn’t paid. |
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Remote Access Trojans Malicious software that spies on users, steals information, or gives attackers remote control of infected systems. |
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Romance Scams Scammers build fake online relationships to trick victims into sending money or personal details. |
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Tech Support Scams Fake pop-ups or calls posing as tech support, pressuring users to pay or install malware. |
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Business Email Compromise Attackers impersonate executives or partners to trick employees into sending money or sensitive information. |
Latest AI Insights
Trends, news and breakthroughs in the world of cybersecurity
Responsible AI. Protected Future.
We believe powerful AI should be paired with strong ethical principles. Our commitment to responsible AI ensures security never comes at the cost of privacy or digital rights
Transparency
We provide clear information about how our AI makes security decisions and what data it uses, balancing technical transparency with usability.
Privacy by Design
We implement privacy-preserving techniques like federated learning and differential privacy to minimize data exposure while maintaining AI effectiveness.
Human Guided AI Protection
While our AI is powerful, we augment it with the deep expertise of Bitdefender Labs.
