Jun 25, 2024
AI and cybersecurity: Opportunities and risks
We explore the influence of AI on cybersecurity, including the benefits of improved threat detection and response, and the dangers of advanced attacks.
In the world of cybersecurity, AI is a powerful tool. It can efficiently sort through data to spot potential threats, automate processes, and learn from past attacks.
Unfortunately, cybercriminals also use AI, making their attacks sneakier and harder to catch. To get you up to speed, this article looks at both sides of AI in cybersecurity.
In this article:
Opportunities for using AI in cybersecurity
Spotting threats
A big advantage of AI is that it can quickly analyze large amounts of data to find anything out of the ordinary. This is an important skill in cybersecurity, where continuous network monitoring is essential to detect anything strange or suspicious.
AI tools can be trained to spot changes in system performance, traffic patterns, or user behavior. When AI detects something suspicious on a network, it sends an instant alert to the security team, helping them solve problems before they get worse.
Modern AI systems use advanced techniques like deep learning and behavioral analysis to understand normal activity. By having a clear idea of what "normal" looks like, these AI systems can effectively spot any changes that might signal a threat.
Faster recovery from cyberattacks
AI can reduce the time needed to understand what has happened during a cyberattack. This is vital because there'll likely be less damage if you can recover quickly from an attack.
When a threat is detected, AI-powered tools quickly analyze various data sources like system logs, network traffic, and user activities to determine what kind of attack it is, how it happened, and how much damage has already been caused.
This information helps IT security teams to focus on preventing further damage and restoring normal operations quickly. AI tools can also be used to generate reports and dashboards that summarize incident details and provide recommendations.
Another way AI tools help respond faster to cyberattacks is by automating tasks. For example, AI can automatically reroute traffic away from vulnerable servers, or communicate with affected users and instruct them to change their passwords.
Learning on the job
Hackers are always finding new ways to break into computers and networks. Fortunately, AI can learn from past attacks and get better at stopping new ones.
For example, reinforcement learning allows AI to learn from its decisions, just like how people learn from their mistakes. By analyzing past attack patterns, reinforcement learning helps AI better predict and handle future threats.
TeamViewer's endpoint protection solution uses machine learning and AI to become increasingly faster and more precise at making predictive malware verdicts. This helps your business find malware before it can do damage.
Threat intelligence
Gathering threat intelligence is an important part of cybersecurity. It involves collecting, analyzing, and sharing information about current and future cyber threats.
AI automates the process of gathering threat intelligence by scanning and collecting information from security reports, industry publications, and even hacker forums on the dark web. It can then transform this data into actionable recommendations for improving cybersecurity.
The effectiveness of AI in threat intelligence is widely recognized. The 2023 Inside the Mind of a Hacker Report revealed that 64% of cybersecurity professionals believe AI enhances the value of their research.
Protection against AI-powered attacks
Cybercriminals are using AI to create new and advanced cyberattacks that are harder to detect. AI-powered cybersecurity tools are one of the best defenses against these advanced attacks.
According to Darktrace’s 2024 State of AI Cyber Security report, 96% of IT security leaders believe that AI-driven cybersecurity solutions significantly improve the speed and efficiency of prevention, detection, response, and recovery. Only 15% believe that traditional non-AI cybersecurity tools can effectively detect and block AI-powered threats.
The risks of AI in cybersecurity
Unfortunately, cybercriminals have the same access to AI as everyone else. They can use AI to develop attack strategies with a much higher chance of success. Below are some of the main ways cybercriminals are using AI.
Social engineering attacks
Social engineering attacks trick people into giving away sensitive information. They include phishing (fraudulent emails) and vishing (fraudulent phone calls).
In the past, scam emails were often easy to spot due to poor spelling, grammar, and word usage. But with AI, scammers can generate personalized, well-written messages that make phishing attempts more convincing and successful.
There are even AI tools designed specifically for scammers. WormGPT and FraudGPT, for example, can create large numbers of persuasive phishing emails in multiple languages and automate their delivery on a large scale.
Deepfakes
Deepfakes are like social engineering attacks in that they help attackers pretend to be someone else. But with deepfakes, AI is used to create fake visual or audio content that looks or sounds real. These can target individuals or be shared quickly online to cause widespread fear and confusion.
Earlier this year, a finance worker in Hong Kong was tricked by a deepfake into paying out US $25 million to scammers who used an AI-generated video to pose as the company’s Chief Financial Officer.
Password hacking
AI can also help cyber criminals in cracking passwords. For example, in a brute force attack, AI automates and speeds up the process of guessing different password combinations until it finds the right one. This method works well against weak or short passwords.
Cybersecurity firm Home Security Heroes tested an AI password cracker called PassGAN. They found that it could crack 71% of common passwords in less than a day.
Data poisoning
Nowadays, AI is used across all parts of a business to automate tasks and enhance productivity. But hackers are now finding ways to exploit AI's weaknesses.
One tactic is "data poisoning," which involves altering the data used to train AI algorithms. This can mean adding false information, changing existing data, or deleting parts of it. Data poisoning is hard to spot and can cause serious harm before it's caught.
An example of data poisoning was Microsoft’s launch of Tay, an AI chatbot on Twitter. Shortly after its launch, trolls exploited its vulnerabilities, causing it to make mean and hurtful remarks online. Microsoft had to deactivate Tay within just 24 hours.
More recently, artists have been using data poisoning to fight back against AI using their art without permission. They use a tool called Nightshade to confuse AI models trying to scan their online work for training. This changes the pixels in the art before it's uploaded, causing the AI to see something different from the actual image. For example, an artwork depicting a house might appear as a car to the confused AI.
Summary
AI tools enhance cybersecurity by quickly identifying threats, speeding up the recovery processes, and using insights from previous attacks. They are also the most effective way to protect against advanced AI-driven cyberattacks. With AI on your side, you can stay one step ahead of cybercriminals and keep your data safe.
If you haven't already, explore how AI-powered tools can help you build a strong and proactive cybersecurity defense.
TeamViewer Endpoint Protection
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