Frequently Asked Questions
What are the main AI-powered cyber threats that security teams need to prepare for?
In this blog post, we'll cover four major AI threats your security operations team should plan and budget for to stay ahead of the game. AI-powered malware can identify security gaps within an organization's systems and quickly exploit them. This kind of attack can cause a network to shut down entirely or result in hackers surreptitiously infiltrating sensitive information.
How do attackers use AI to carry out business email compromise (BEC) scams?
Using AI, attackers can generate massive business email compromise (BEC) scams. The process involves scraping data from employee LinkedIn profiles, mapping out the products, projects, and groups those employees work on, feeding that information into an LLM, and generating social engineering content. The result is extremely convincing emails that look as if they are from the employees' bosses or CFOs, and can even include precise details about the projects they're working on.
What is a data poisoning attack and how does it affect machine learning models?
The goal of these attacks is to intentionally introduce false data into an organization's collection of data, thereby skewing the results of any predictive modeling or machine learning algorithms. A form of adversarial attack, data poisoning involves manipulating training datasets by injecting poisoned or polluted data to control the behavior of the trained ML model and deliver false results.
How does deepfake technology work and why is it a cybersecurity threat?
Deepfake technology uses machine learning algorithms to analyze and learn from accurate data, such as photos, videos, and voices of people, and then generate new data that resembles the original with some changes. Developers use two ANNs to create deepfakes: one generates fake data, and the other discerns how convincing the data looks. The generator applies this judgment to improve its output until it deceives the discriminator, thus creating a Generative Adversarial Network (GAN).
How should SOC teams defend against AI-based cybersecurity threats?
SOC teams must start building threat models, upskill, and procure the right tools to meet emerging challenges. SOC teams need to invest in advanced AI-based malware detection solutions that can identify and thwart these sophisticated attacks. SOC teams should also build AI models that can identify content generated by AI and distinguish between genuine messages and those that could be malicious.
