According to a 2023 Data Breach Investigation Report, approximately 23% of malware is delivered via email. Organizations like Abnormal Security, Google’s AI Red Team, and Darktrace have shown the transformative power of AI in preventing email breaches, proving its revolutionary impact on safeguarding sensitive data.
AI's defensive capabilities are revolutionary in combating phishing and zero-day attacks. Using machine learning algorithms, AI sifts through vast datasets to spot patterns and anomalies, enabling real-time analysis to identify and prevent malicious email attacks before they cause significant damage. Other capabilities include behavioral analysis to monitor user behavior, machine learning to identify new and evolving threats, and natural language processing (NLP) to analyze the context and content of emails.
Related: How AI is revolutionizing email breach detection and response
Case Study 1: Abnormal Security
In several instances, Abnormal's AI-native solution successfully flagged intricate phishing attempts that traditional email security systems like firewalls would have missed, including emails impersonating Netflix, insurance companies, and cosmetics brands. By analyzing normal communication patterns, identities, and contextual cues within organizations, the system identifies suspicious emails—even those that are professionally written and devoid of typical red flags like grammatical errors.
The platform's ability to evaluate the likelihood of text being AI-generated enables it to detect nuanced threats that often bypass conventional filters like antivirus software, proving the transformative role of AI in defending against modern cyber threats.
Case Study 2: Google’s AI Red Team
Google's dedicated AI Red Team has identified six attack vectors targeting AI systems. These attack vectors range from prompt manipulation to data exfiltration.
One notable finding involves the exploitation of large language models through prompt engineering. In this technique, attackers craft malicious prompts that manipulate AI systems to produce unintended or harmful outputs. For example, an attacker might embed an invisible paragraph within a phishing email to trick the AI into classifying the email as legitimate, effectively bypassing traditional anti-phishing protections.
The team also observed that traditional security controls remain effective in mitigating many AI-related risks, such as protecting model integrity and defending against data poisoning or backdoor attacks. Their research shows the importance of combining hacker simulations with AI expertise to construct resilient defenses. Google recommends that organizations validate and sanitize both inputs and outputs to AI models—applying the same standards as those used in traditional cybersecurity practices such as those required by HIPAA.
Case Study 3: Darktrace
Darktrace has transformed threat detection in cybersecurity through its use of advanced deep learning technologies, offering autonomous and real-time identification and response to cyber threats. Unlike traditional systems that rely on predefined rules or threat signatures, Darktrace's platform employs sophisticated machine learning algorithms capable of independently analyzing complex network behaviors and detecting subtle anomalies.
The strength of Darktrace's technology is its ability to learn and understand what constitutes normal network behavior. This enables the system to identify deviations that may indicate potential security threats, even if they do not match known attack patterns. By leveraging deep learning, the platform can detect emerging and previously unseen types of cyberattacks.
Additionally, by continuously learning from network data, the system becomes increasingly adept at distinguishing between genuine threats and benign activities, providing organizations with a highly adaptive and intelligent defense mechanism.
Successful applications of AI in cybersecurity have revealed common patterns that contribute to their effectiveness:
GLTR is a tool that helps identify text generated by large language models by analyzing word probabilities to spot artificially generated content, aiding in phishing email detection.
Prompt engineering involves crafting and optimizing prompts to interact effectively with AI models, ensuring accurate and relevant responses, especially in cybersecurity applications.
GLTR detects AI-generated text, while deep learning analyzes email data for patterns and anomalies, enhancing the detection and prevention of sophisticated phishing attacks.