Recent advances in AI technologies have granted organizations and individuals alike unprecedented productivity, efficiency, and operational benefits. AI is, without question, the single most exciting emerging technology in the world. However, it also brings enormous risks. While the dystopian, AI-ruled worlds of sci-fi films are a long way off, AI is helping cyber threat actors launch attacks at a hitherto unknown scale and level of sophistication.
But what are AI-powered attacks? What’s their impact on the cybersecurity landscape? And how can you protect yourself against them? Keep reading to find out.
What is an AI-Powered Attack?
An AI-powered attack leverages machine learning (ML) and AI technologies to enhance the effectiveness, speed, and scale of cyberattacks. These technologies can analyze vast amounts of data incredibly quickly, meaning the attacks they drive can identify and exploit vulnerabilities far quicker and with greater precision than manual attacks. As such, they are often able to bypass traditional security measures.
What are the Types of AI-Powered Attacks?
It’s important to understand that the phrases “AI-powered attack” or “AI attack” are merely catch-all terms for a wide range of cyberattack techniques. Although they might all involve AI, they vary wildly in their approaches. Some of the most notable examples are:
Automated Vulnerability Exploitation
Automated vulnerability exploitations involve using AI-powered tools to identify vulnerabilities in complex systems such as APIs. These tools use ML algorithms to scan codebases, analyze patterns, and predict weak points in security configurations, then automatically exploit these vulnerabilities. This method allows attackers to execute zero-day attacks on multiple targets at once at a speed unattainable manually.
Adaptive Bot Attacks
Traditional security tools like CAPTCHAs or rate-limiting mechanisms can identify potential attacks because they recognize bot behaviors. Adaptive bot attacks bypass these tools by simulating legitimate user behavior or otherwise changing their behavior to avoid detection.
For example, some AI-powered account takeover (ATO) attacks start extremely aggressively, then, once blocked, reduce their requests per second (RPS) and change the IP address to fly under the radar. Similarly, AI-powered API scraping attacks mimic human behavior, meaning alarm bells fail to ring.
AI-Driven Phishing
Arguably the simplest type of AI-powered attack, AI-driven phishing scams are becoming increasingly common. Generative AI tools allow cybercriminals to craft convincing phishing emails at scale, free from the formatting and language errors that have previously given them away. Moreover, these tools facilitate mass customization, helping attackers tailor messages to specific individuals or groups based on publicly available data, dramatically increasing the likelihood of success.
Deepfake Scams
Deepfake technology allows people to create hyper-realistic, AI-generated videos or audio of real-life individuals. You may have seen this technology in the news recently – in the lead-up to the UK general election in 2024, a deepfake video purporting to show the former opposition leader Keir Starmer verbally abusing his staff circulated on social media.
As this deepfake technology has matured, we’ve seen increasing instances of cyber threat actors using it to scam organizations and individuals. For example, in early 2024, a UK design and engineering firm lost HK$200 million to a successful deepfake scam.
Understanding the Impact of AI-Powered Attacks
AI-powered attacks are having a transformative impact on the cyber threat landscape. They facilitate more complex and targeted attacks, making detection and prevention more challenging. They allow attackers to automate and scale operations, leading to a higher volume of attacks. And, ultimately, they drive up the likelihood and severity of financial losses organizations face in the wake of data breaches, fraud, and operational disruptions.
Top Tools for Preventing AI-Powered Attacks
However, hope is not lost. Tools are available to help protect against AI-powered attacks, many of which are powered by AI themselves. AI-powered threat detection systems, for example, leverage ML algorithms to quickly analyze data and detect patterns that could indicate malicious activity. These systems continuously learn from the detection process to improve their ability to identify new and emerging threats – rather than relying solely on known patterns.
Similarly, behavioral analytics tools monitor and analyze user and system behaviors to detect deviations from established patterns. They create a baseline of normal behavior to identify suspicious anomalies, like unusual login locations, abnormal transaction patterns, or unusual file access. AI-powered behavioral analytics tools, as do AI-powered threat detection systems, learn over time to improve accuracy and detect more advanced threats that attempt to simulate human behavior.
Finally, traffic analysis tools offer insights into network activity to detect potential intrusions. These tools use techniques like deep packet inspection, flow analysis, API request decoding and AI-driven anomaly detection to monitor data transfers and identify malicious traffic. They can recognize patterns associated with attacks, such as command-and-control communications, data exfiltration, or distributed denial-of-service (DDoS) attacks.
How Wallarm Can Help
Wallarm offers all of these features and more as part of our Integrated App and API Security Platform. Our solution is designed to keep pace with the evolving cybersecurity threat landscape. It leverages AI and ML technologies to detect and respond to vulnerability exploitation, anomalous behavior, business logic attacks, credential stuffing, bot scraping, and much more to protect your organization today and for years to come, Schedule a demo today to find out more about what we can do for your organization.