How Artificial Intelligence is Transforming Cybersecurity in 2024

The rapid evolution of the digital world-Internet of Things, online banking, remote work, and AI-generated content-has led to a surge in cyber threats. According to analysts, a cyberattack attempt occurs every 39 seconds globally, with damages projected to reach trillions of dollars by 2025. Traditional security approaches such as antivirus software and classic monitoring systems can no longer keep up with the speed and sophistication of modern attacks. This is why businesses and governments are increasingly turning to artificial intelligence in cybersecurity.

Artificial Intelligence in Cybersecurity: What Does It Mean?

When we talk about AI in cybersecurity, we're not referring to robots, but to software systems capable of:

• collecting data from multiple sources,
• identifying patterns,
• detecting suspicious activity,
• making decisions automatically.

Whereas security experts once manually defined what constitutes an attack, AI now learns from vast datasets-network traffic logs, virus samples, and phishing templates-to do this automatically.

Main advantage: Unlike traditional systems that only react to known threats, AI can spot new, previously unseen attacks by analyzing indirect indicators.

Example: A standard antivirus looks for a file matching a known virus signature. An AI system, however, might detect a process copying thousands of files per second-blocking it even if it's a never-before-seen threat.

Machine Learning and Cybersecurity

Most AI-driven cybersecurity relies on machine learning (ML): systems that find patterns in data and make predictions without explicit programming.

There are several types of ML used in cybersecurity:

1. Supervised learning: The system is shown examples of both legitimate and malicious behavior (e.g., millions of emails, some phishing, some regular). The model learns to distinguish between them.
2. Unsupervised learning: Here, there are no predefined labels. The algorithm independently searches for anomalies, such as a computer sending an unusual number of packets at night.
3. Deep learning and neural networks: Modern neural networks can recognize highly complex patterns. They're used to analyze images (like phishing website screenshots), voice (fake calls), and large streams of network traffic.

Key benefit: Machine learning adapts to new threats. Hackers create new viruses daily-manual updates are impossible, but AI can spot suspicious activity where humans might miss it.

Example: Banks use ML to monitor transactions. If dozens of small payments to unfamiliar sites are suddenly made from a client's card, the system automatically blocks these transactions to prevent fraud.

How AI Enhances Cybersecurity in Practice

3.1. Automatic Threat Detection

Traditional security tools rely on signatures to identify malware, but thousands of new threats emerge daily-many not yet cataloged. AI excels at detecting abnormal behavior that deviates from established norms, such as:

• a routine process suddenly consuming excessive resources,
• a program attempting to access system files for the first time,
• network overloads from suspicious data packets.

AI flags these anomalies as potential threats-even if they've never been seen before.

3.2. Network Traffic Analysis

Corporate networks now transmit billions of data packets. Manual inspection is impossible. Machine learning algorithms help identify:

• suspicious connections,
• unusual data transfer routes,
• covert communication channels used by attackers.

Example: The system detects that the accounting server is sending data to an unknown foreign IP address at night, immediately notifying security experts.

3.3. Combating Phishing

Phishing remains a top cyberattack method. Users receive emails from "banks" or "couriers" and are lured into entering sensitive information. While traditional protection relied on blacklists, fraudsters constantly create new web addresses.

AI algorithms analyze:

• email structure,
• website design,
• user behavior,

to spot fakes. Even if the link is new, the system recognizes suspicious similarities to legitimate bank pages and issues a warning.

3.4. User Behavior Monitoring

Not all threats are external. Sometimes, attacks originate from within-either intentionally or by accident. AI tracks user behavior:

• which files are accessed,
• which programs are run,
• usual working hours.

If an account behaves abnormally-like downloading gigabytes of data at night-the system alerts security personnel.

3.5. Automated Response to Attacks

Most importantly, AI can not only detect threats but also respond instantly. For example:

• isolating an infected computer from the network,
• automatically blocking suspicious transactions,
• rerouting traffic during a DDoS attack.

This saves time and stops attacks from spreading before humans can react.

AI Versus Cyberattacks

4.1. DDoS Attacks

Distributed Denial of Service (DDoS) attacks flood servers with requests. AI systems analyze traffic in real time, distinguishing real users from bots-instantly rerouting or limiting suspicious traffic to keep services available for genuine clients.

4.2. Malware and Viruses

Modern malware often uses obfuscation: changing code, hiding in memory, or encrypting actions. Machine learning detects such programs by behavior (e.g., sudden mass encryption of files) rather than specific files, blocking even new, unknown viruses.

4.3. Predicting Vulnerabilities

AI helps spot software vulnerabilities before hackers exploit them by analyzing code and comparing it to known patterns-alerting teams to potential weaknesses early on.

4.4. Next-Generation Cyber Threats

Cybercriminals now use AI to create fake voices, deepfake videos, and automate attacks. AI is both a shield and a weapon in modern cyber warfare, pushing organizations to develop ever more advanced defensive systems.

Pros and Cons of AI in Cybersecurity

5.1. Key Advantages

1. Speed of response: AI identifies suspicious actions in milliseconds, while humans might need hours or days.
2. Big data processing: AI can analyze millions of events per minute-impossible for humans alone.
3. Process automation: Routine tasks (log analysis, blocking suspicious processes) are handled by algorithms, reducing analysts' workload.
4. Detection of new threats: AI identifies attacks not yet included in databases, unlike static signature-based antiviruses.
5. Reducing human error: Automated systems decrease the risk of mistakes caused by employees.

5.2. Limitations and Risks

1. False positives: Legitimate actions may be wrongly flagged as threats, causing inconvenience and requiring manual review.
2. Dependence on data quality: Poor or incomplete training data can lead to incorrect results.
3. High implementation costs: AI security systems require powerful hardware, skilled experts, and ongoing updates-potentially too expensive for small businesses.
4. Model vulnerabilities: Attackers may feed specially crafted data to trick AI algorithms, such as slightly altered images misclassified by the system.
5. Ethical concerns: AI often analyzes user behavior and personal data, raising issues of privacy and information control.

Use Cases and Real-World Examples

6.1. Corporate Sector

• Microsoft uses machine learning to detect suspicious sign-ins to Office 365 accounts.
• Google applies AI in Gmail to block phishing emails-over 99% of unwanted messages are automatically filtered.
• IBM's QRadar platform leverages AI to analyze logs and help analysts rapidly identify incidents.

6.2. Financial Industry

• Real-time transaction monitoring: Algorithms compare each operation to typical client behavior, blocking suspicious payments.
• Fraud prevention: AI detects fake websites resembling online banking portals and warns customers.

Estimates suggest AI helps banks save billions of dollars annually by preventing cybercrime.

6.3. Government Agencies

• Analyzing cyberattacks on state networks.
• Monitoring critical infrastructure: energy, transport, communications.
• Detecting cyber-espionage campaigns.

Some countries are already forming cyber defense units powered by AI that operate in real time.

6.4. Small and Medium Businesses

AI security solutions were once the domain of large corporations, but today, many cloud services offer AI-based protection via subscription. Examples include:

• cloud antivirus solutions with AI scanning,
• traffic analysis services,
• website protection tools against attacks.

Now, even small companies can benefit from AI without investing in expensive infrastructure.

The Future of AI in Cybersecurity

7.1. Rising Number of Cyber Threats

Each year sees a rise in cyberattacks. By 2025, we expect to see:

• phishing powered by neural networks to generate convincing messages,
• deepfake videos and fake calls for social engineering,
• automated attacks created by AI algorithms themselves.

This means AI is becoming essential for combating advanced threats.

7.2. AI and Post-Quantum Cryptography

Quantum computers will eventually break current encryption methods. AI is already used to develop and test post-quantum cryptography, which is resistant to quantum attacks-a trend set to grow in coming years.

7.3. Zero Trust and Adaptive Security

The Zero Trust model ("trust no one") is becoming a new standard. AI is integrated into this approach by analyzing user and device behavior-automatically restricting access for suspicious activity. Future adaptive security systems will work continuously, evolving with new threat scenarios.

7.4. Human-AI Collaboration

Despite its power, AI cannot fully replace cybersecurity professionals. Instead, it will serve as an "extra set of eyes," helping analysts detect incidents faster and make informed decisions. The future of cybersecurity lies in collaboration: algorithms handle massive event streams, while experts provide strategic oversight.

Conclusion

• AI and machine learning help identify unknown threats, analyze network traffic, prevent phishing, and mitigate DDoS attacks.
• Machine learning enables adaptive protection and uncovers vulnerabilities before hackers exploit them.
• Neural networks unlock powerful data analysis but require caution due to risks of false positives and attacks on the models themselves.
• Major corporations, banks, and governments are already leveraging AI, and cloud services are making it accessible to small businesses.
• The future will see AI integrated with post-quantum cryptography, Zero Trust, and adaptive defense systems.

As cyber threats grow more complex, artificial intelligence is becoming the digital world's primary shield.

FAQ

1. How does artificial intelligence help in cybersecurity?
   AI automatically analyzes network traffic, detects suspicious actions, blocks malicious processes, and protects against phishing.
2. Will AI replace cybersecurity professionals?
   No. AI automates routine tasks, but humans are still needed for strategic decisions and complex incident analysis.
3. Are neural networks used in cybersecurity?
   Yes, neural networks are used for traffic analysis, image recognition (such as identifying fake sites), and detecting user behavior anomalies.
4. Can an AI security system be fooled?
   Theoretically, yes. There are attacks that feed altered data to machine learning models to trick them. That's why continuous retraining is essential.
5. Will AI remain important for cybersecurity in the future?
   Absolutely. As cyber threats and quantum technologies evolve, AI's role will only become more critical, forming the foundation of adaptive protection and next-generation digital infrastructure.