Social Engineering and AI

AI-based social engineering attacks

AI is driving innovation and revolutionizing every industry — from healthcare to retail to manufacturing. AI is empowering businesses worldwide to operate with greater efficiency and sophistication. In that same vein, AI is also empowering cybercriminals.

The classic technique of social engineering involves deceptive practices, such as impersonation or manipulation of trust, to extract personal and confidential information from targeted individuals. As AI tools increase in potency and accessibility, social engineering attacks are now significantly more personalized, effective, and scalable. Defending against such attacks requires more sophisticated tools than before.

In this post, we’ll investigate how AI has advanced social engineering to the next level. We’ll look specifically at how emerging technologies, such as deepfakes, have amplified social engineering techniques and increased the potential for fraud. We’ll also explore mitigation strategies and tools available for organizations to fight this battle effectively.

How AI enhances traditional social engineering

Successful social engineering attacks begin with the gathering of enough personal data about a targeted individual to win over their initial trust. Perpetrators then exploit that initial trust to extract more sensitive information for personal gain. This is where AI can play a substantial role.

AI is ideal for collecting large amounts of data quickly and thoroughly. It processes large datasets, identifies patterns, and extracts relevant information with unparalleled levels of speed and precision.

Social engineering is about probability. The question is not whether it succeeds but rather how many attempts are required before it does. AI automation and integration with tools such as communication platforms significantly increase the probability of success in a shorter period. AI tools can now conduct thousands of phone calls simultaneously, each highly personalized to mimic human conversation with impeccable grammar and even the ability to simulate voices familiar to the targeted individual.

Many of these attacks now rely on one of the most groundbreaking recent advancements in AI: deepfakes. Attackers use deepfakes to generate remarkably realistic video and audio snippets that are difficult to distinguish from authentic recordings. To generate deepfakes, an attacker only requires short video and audio recording samples of the person they’re trying to impersonate. AI algorithms then use the samples to accurately replicate voices, appearances, and body language.

Key techniques used in AI-based social engineering Attacks

AI has significantly enhanced long-established social engineering attacks. In this section, we’ll cover some of the most common attacks that can be significantly amplified through integration with AI.

Phishing campaigns

Phishing is one of the most common forms of social engineering attacks. Attackers present a convincing amount of personal information to a targeted individual. Then, they seek to persuade the target to do any of the following:

• Click a malicious link
• Download a file
• Divulge confidential information, such as a password or credit card number

Cybercriminals conduct these attacks in large batches, aiming for at least one individual to fall victim to the deception. 

AI tools can augment phishing campaigns by dynamically adjusting the attack based on user reactions. These tweaks increase the probability of success with less time and effort. 

Business email compromise

Malicious actors frequently target organizations with significant financial resources, making them more attractive for ransom demands than individuals. A common attack method is to target employees via email by impersonating a senior executive. This strategy is designed to intimidate the employee into believing the email’s legitimacy and blindly complying with the fraudulent request. AI tools can search, analyze, and mimic examples of the executive's writing style, making fraudulent emails more convincing and harder to spot as fake.

Spear phishing

Spear phishing is a type of phishing attack that focuses on quality over quantity. While regular phishing attacks cast a wide but uncoordinated net, spear phishing attacks are fewer but highly targeted and well-researched. Spear phishing aims at specific individuals or organizations by carefully crafting messages that exploit known details or relationships to increase the likelihood of a response to the email. Attackers are increasingly using AI to collect data, mimic behavior, and even perfectly translate into many languages, making attacks more personalized and persuasive than before. 

The dangers of AI-generated deepfakes

The very first attempts at deepfake technology were easy to spot as manufactured fakes. However, recent advancements in machine learning and neural networks significantly improved their quality. Even those with a keen eye are finding it increasingly difficult to distinguish a deepfake from genuine content. Deepfake impersonation techniques have already led to significant financial damages for high-profile companies. 

The most dangerous aspect of deepfakes now is their capability to influence public opinion based on false information. Furthermore, as deepfakes become more convincing, this causes genuine audio and video content to be more prone to suspicion. There is a growing skepticism about the authenticity of any type of digital media.

Detection and mitigation strategies

Fortunately, as AI-powered malicious activities evolve, so do detection and mitigation strategies. Many strategies also leverage AI tools to defend individuals and organizations from these attacks. 

Behavioral analysis and anomaly detection are popular AI techniques that enable cybersecurity platforms to spot patterns that would indicate AI-enhanced malicious activities. Here’s how each side is using AI in this evolving conflict:

• Generative AI tools are used for developing increasingly effective attacks by efficiently and accurately formulating phishing emails.
• Cybersecurity platforms use AI technologies such as natural language processing and anomaly detection to distinguish genuine emails from fraudulent ones. They’re relentlessly pursuing their counterpart to stay one step ahead in defending against the threats.

Organizations should not rely solely on AI for cybersecurity and defense. Employee education on basic cybersecurity principles and common attack strategies are the most effective proactive measures for preventing attacks. As AI-based social engineering attacks evolve, it’s important for organizations to keep up cybersecurity training and attack simulation for their employees. Only then can they be prepared for real-world situations to avoid inattentiveness and misjudgment.

Stopping AI Attacks with a Modern Cybersecurity Platform

AI is rapidly changing the world around us in both positive and negative ways. As such, organizations need to adapt quickly. While AI offers numerous advantages to legitimate users, malicious actors can also leverage AI to execute social engineering attacks with unprecedented precision and effectiveness. Cybercriminals often weaponize the same tools that amplify productivity and innovation, but they do so to exploit vulnerabilities more quickly and persuasively than ever before.

Preventing AI-powered attacks requires cybersecurity tools that are equipped to respond in kind. Organizations need robust solutions like CrowdStrike Falcon, a state-of-the-art cybersecurity platform offering cutting-edge technology for identity protection, anomaly detection, and AI-native protection.