The modern world is becoming increasingly digital, and with this growth, cyber threats are also rising. In 2023, global losses from cyberattacks amounted to $8.4 trillion, and by 2025, this figure is expected to increase to $10.5 trillion(Cybersecurity Ventures).
Artificial intelligence (AI) has become a crucial tool for both cybersecurity experts and hackers. On the one hand, AI helps prevent attacks, analyze millions of events in real-time, and automatically respond to threats. On the other hand, cybercriminals are also using AI to bypass security mechanisms, automate attacks, and exploit system vulnerabilities.
In this article, we will examine the key ways AI is strengthening cybersecurity, explore real-world examples of its use, and analyze new threats posed by machine learning advancements.
1. How AI is Used in Cybersecurity
AI has significantly improved the ability to protect data and networks. Here are the main areas where artificial intelligence is actively applied:
1.1. Automated Threat Detection
Previously, cybersecurity relied on antivirus programs based on signature-based detection, which recognized known malicious code patterns. Today, AI uses behavioral analysis, allowing it to detect even unknown attacks.
Example:
• Darktrace, a cybersecurity system powered by machine learning, analyzes network activity and detects anomalies in real-time. In 2022, the company reported that its algorithms were preventing 150,000 attacks daily worldwide.
1.2. Predictive Analytics in Cybersecurity
AI can predict attacks before they occur by analyzing user behavior, network traffic, and system logs to detect suspicious patterns.
Example:
• IBM Watson for Cyber Security processes 80% of the world’s cyber threat data, analyzing millions of documents, articles, and reports. This enables the early identification of new attack patterns before they become widespread.
1.3. Automated Incident Response
Cyberattacks often take hours or days to respond to, but AI can instantly block suspicious activity and isolate affected systems.
Example:
• Microsoft Sentinel uses machine learning algorithms to automatically disconnect compromised devices from the network or adjust security policies upon detecting threats.
2. New Threats: How Hackers Use AI
AI has become a powerful tool not only for cybersecurity but also for cybercriminals. In 2023, more than 50,000 attacksutilizing AI were recorded (MIT Technology Review).
2.1. Deepfake Attacks and AI-Enhanced Phishing
AI enables the creation of highly realistic fake videos and voice recordings, which are used for fraud and phishing attacks.
Example:
• In 2020, scammers used an AI-generated voice of a German company’s CEO to trick employees into transferring $243,000 to the attackers’ account.
2.2. AI-Driven Cyber Attacks
Machine learning algorithms can analyze system vulnerabilities and identify ways to bypass traditional security defenses.
Example:
• In 2022, Google researchers found that AI models could break CAPTCHA security measures with 94% accuracy, rendering them ineffective against automated cyber threats.
2.3. Adversarial Attacks on AI Systems
Hackers can “trick” machine learning algorithms by subtly modifying input data, leading to incorrect AI decisions.
Example:
• In 2021, researchers demonstrated that a sticker placed on a road sign could cause Tesla’s autopilot to misinterpret a “Stop” sign as a 45 km/h speed limit, posing a serious safety risk.
3. How to Defend Against AI-Powered Attacks?
Despite emerging threats, there are effective AI-driven security measures that organizations can implement.
3.1. Zero Trust Security Model
The Zero Trust approach requires continuous authentication and validation of all users and devices, eliminating implicit trust.
Example:
• Google implemented the BeyondCorp Zero Trust model, allowing employees to work securely from anywhere without a VPN while requiring multi-factor authentication for every session.
3.2. AI-Based Biometric Authentication
AI-powered security solutions use facial recognition, fingerprint scanning, and behavioral biometrics to enhance security.
Example:
• Mastercard implemented AI-based motion analysis for PIN entry, reducing fraud attempts by 80%.
3.3. AI in Quantum Cybersecurity
With the rise of quantum computing, traditional encryption methods may become obsolete. AI is now helping develop quantum-resistant cryptographic protocols.
Example:
• IBM Quantum is working on post-quantum cryptography algorithms designed to withstand quantum computing attacks.
4. The Future of AI in Cybersecurity
According to Gartner, by 2030, more than 70% of organizations will use AI-driven automation for cybersecurity. Key future developments include:
1. Self-improving security systems – AI will not only detect threats but also automatically enhance protection mechanisms.
2. Global threat intelligence platforms – Companies will share cyber threat data to create predictive defense networks.
3. Full automation of cybersecurity operations – Humans will only be required for strategic decision-making.
Conclusion
AI is playing an increasingly important role in the fight against cyber threats, but at the same time, it is becoming a tool for hackers.
Key Takeaways:
• AI accelerates threat detection and reduces response time.
• Cybercriminals are using AI for phishing, bypassing security systems, and attacking machine learning models.
• New technologies, such as Zero Trust and quantum cybersecurity, will help mitigate future threats.
In the coming years, AI will become an integral part of digital security, and organizations that adopt these advanced technologies early will gain a decisive advantage.
