What is an AI-powered cyber attack? Real-world examples of AI used in cyber attacks.

Over the past five years, the rise of AI technology has been accompanied by the emergence of innovative AI-driven cyber attacks. These attacks include a variety of techniques such as AI-powered social engineering, phishing, deepfakes, ransomware, and malicious GPTs. While AI has significantly enhanced automation and business efficiency, it has also provided cyber criminals with new tools to exploit. AI-powered cyber attacks utilise machine learning and algorithms to automate and accelerate various processes involved in cyber intrusions. This results in attacks that are not only faster and more frequent but also more impactful and harder for traditional cyber security defences to detect.

Unlike traditional cyber attacks, AI-driven methods can operate with greater frequency and success rates due to their reliance on automation. This technology is especially useful for phishing campaigns, with the ability to design human-like emails which are convincing to victims. The ability to generate tailored social engineering campaigns makes the material highly specific and increases their success rate.

Easily accessible AI-enabled tools allow attackers to replace manual research with sophisticated reconnaissance capabilities. Rather than spending time gathering information about their targets, cyber criminals can now leverage AI to scrape data from online sources, yielding personalised and relevant insights tailored to either general or specific victims.

So how can I protect myself from AI-powered attacks?

In response to these evolving threats, cyber security professionals face new challenges that require enhanced strategies for detection and prevention. Endpoint Detection & Response (EDR) solutions play a crucial role as the first line of defence against these sophisticated attacks. Continuous security assessments are essential for establishing baselines of user behaviour, enabling organisations to identify anomalous activities that may indicate a breach. Furthermore, real-time analysis of input and output data for AI/ML systems is vital for protecting against adversarial AI attacks, creating actionable insights that can inform security measures.

A robust incident response plan is also critical for addressing worst-case scenarios in the event of a cyber attack. Such a plan should encompass preparation, detection, analysis, containment, and response to ensure that organisations are ready to act swiftly when faced with an intrusion. Importantly, while the rise of automation and AI presents new challenges, it also underscores the central role of human expertise in cyber security. Employees represent both the strongest and weakest link in your security posture; therefore, ongoing security awareness training is essential for equipping staff with knowledge about emerging threats.

AI can also serve as a valuable tool in enhancing cyber security measures. Organisations can automate security-related tasks such as monitoring, patching, and detection to optimise response times by prioritising high-risk activities. AI poses challenges but also offers solutions, and this duality highlights the need for a balanced approach in cyber security strategies.

Real-world Examples of AI-based attacks

TaskRabbit (2018): Hackers used AI to breach the online marketplace, stealing over 3.75 million records of personal and financial details from users.

Colonial Pipeline (2021): The DarkSide ransomware group employed both traditional hacking tools and AI techniques to disrupt fuel supply across much of the East Coast of the United States.

Yum! Brands (2023): An AI-driven ransomware attack which led to the closure of nearly 300 UK branches of KFC and Pizza Hut for several weeks.

Activision (2023): Hackers executed a targeted phishing campaign using AI-generated SMS messages to gain access to the company’s employee database.

T-Mobile (2022-2024): The mobile operator announced in early 2024 that 37 million customer records were stolen through tools leveraging AI capabilities.

As AI technology continues to evolve, so too must the strategies employed by cyber security professionals. By harnessing AI defensively while emphasising the need for EDR solutions, and ongoing education, organisations can strengthen their defences against the growing threat posed by AI-powered cyber attacks. This can be harnessed in a positive manner as Google have recently shown. Google have started utilising Gmail and AI-driven technology can be implemented as a cyber security measure as well. Google uses machine learning models to detect and prevent millions of daily phishing attempts targeting its 1.5 billion users.