Not all AI apps are safe, and many are prone to vulnerabilities. AI is like a Swiss army knife—if it falls into the wrong hands, the scale of damage it can cause is disastrous.
National technology guards have warned users worldwide in their latest advisories about technical issues in AI, like adversarial attacks, data poisoning, prompt injections, model inversions, and hallucination exploits. AI is considered a hallmark of innovation and can also be used to launch sophisticated cyber threats. AI-powered cyber attacks target industries such as telecommunications, healthcare, fintech, and IT, and they even go after the government.
This guide will examine how AI cyber attacks work, their different types, and how to prevent AI-powered cyber attacks.
What are AI-Powered Cyber Attacks?
Put simply, an AI cyber attack is a threat launched by someone who uses AI to design and deploy it. For example, if a person uses AI to write malicious code and inject it into apps on users’ devices, that is an instance of an AI-generated cyber attack.
AI can be used to conduct research, reconnaissance of victims and threats, and advise users on how to collect personal and sensitive data. When threat actors can bypass AI’s censorship laws and model biases, they can introduce great danger to global ecosystems.
AI services can help threat actors mask their identities, cause data breaches, and cover their tracks better in the digital world. They can ask questions and get help by receiving answers to critical decisions, especially regarding legal issues. AI services can also help protect people from illegal and medical contacts.
How Do Cybercriminals Use AI for Attacks?
Cybercriminals can use AI to improve their attack performance and change their attack paths. AI can be used to modify and adapt to AI threat detection techniques in real time and make their attacks more challenging to detect, even with the latest cybersecurity tools. AI technology can help attackers launch their attacks much faster before organizations can react to them. It can increase their level of sophistication and, over time, bypass modern evasion techniques. AI can help attackers document all the security measures organizations take to defend against their tactics and create counter-strategies for them.
Malicious GPTs can produce intelligent texts in response to user prompts and give them a hard time dealing with attacks. AI-enabled ransomware is a real problem, and attackers can use various AI and ML techniques to target different business operations and areas of model development. Data poisoning attacks involve injecting fake or misleading information into training data sets to affect the organization’s model’s accuracy and objectivity. Some attacks can apply subtle changes to model data, cause misclassification errors, and even tamper with targets’ parameters and model structures.
Common Types of AI-Powered Cyber Attacks
Here are the most common types of AI-powered cyber attacks:
Social Engineering Attacks
AI-based social engineering attacks are becoming very common. AI algorithms can be used to research, write carefully crafted and convincing emails, and manipulate human behavior. They can convince targets to transfer money and ownership of high-value assets. AI-generated emails can convince targets to grant access to systems and persuade them to give away sensitive information. Hackers can develop and adopt specific personas and create realistic scenarios that receive real attention. They can write personalized messages and create multimedia assets like video footage, audio recordings, and other elements.
Deepfakes
Attackers can create deepfakes that commonly appear on the internet and mimic officials. Disinformation campaigns and fake news are becoming notoriously common for high-profile incidents. For example, an attacker can use existing footage of an official entity in the organization and create doctored video footage and voice recordings. They can use AI to mimic that person’s voice, clone their faces, and instruct the person to carry out specific actions or give instructions. Everyone else in the organization will fall for it because these deepfakes are hyper-realistic, and no one can tell the difference between the fake and real ones.
AI Malware and Web Scraping Bots
Cyber attackers are equipping malware with AI capabilities to evade detection software and adapt to countermeasures. AI-based malware can learn from failed attacks, adjust behavior, and find new vulnerabilities automatically. AI-based web scraping bots can also evade standard bot-detection methods like CAPTCHAs and rate limiters. These bots scrape vast amounts of sensitive data from websites, including personal and competitive data. They mimic human browsing patterns, rotate proxies for their IP, and modulate their timing to avoid security warnings. This renders them very malicious for companies storing customer information or confidential data online.
How to Detect AI-Powered Cyber Threats?
AI cyber attacks require advanced monitoring hardware and behavioral analysis tools. Look for unusual network usage patterns, data access requests, or login attempts that may be characteristic of AI-powered activity. Since AI-powered attacks are likely to execute at machine speed, unusual volume or activity rate spikes should raise an alarm immediately.
Utilize anomaly detection tools that create standards of everyday practices and identify variations. They need to monitor user and system activity along several different axes at the same time. For example, an account opening abnormal sets of resources or doing so at off-peak hours might indicate AI-driven attacks.
Pursue incremental, iterative testing of defenses—AI systems will slowly test before a mass attack. Employ decoy systems to draw out and expose AI attackers without risking tangible assets, and learn their tactics.
Systematically examine authentication logs for impossible travel patterns or concurrent access from two geolocations. AI environments may attempt to bypass geospatial blocking through distributed networks. You should also search for anomalous data exfiltration patterns since AI attacks will exfiltrate data in patterns designed to avoid volume-based detection tools.
Best Practices to Prevent AI-Powered Cyber Attacks
The first step in preventing AI-powered cyberattacks is knowing what tactics criminals use to enhance their skills and cause data breaches. AI algorithms can solve CAPTCHAs like humans and steal sensitive information by hijacking accounts. Hackers can use AI to crack passwords, launch brute-force attacks, and force themselves into networks and systems.
They can also use AI to listen to your keystrokes, analyze past patterns, and guess passwords with up to 95% accuracy. Audio fingerprinting is another tactic that cybercriminals use to clone your voice and use it against your family and friends. Now that you know some common ways AI is used, you can prevent AI-powered cyberattacks. Here are some of the ways you can do that:
Turn on Multi-Factor Authentication
Multi-factor authentication will add an extra layer of security that AI can’t bypass. Your attacker will need access to multiple devices and can toggle between different forms of identification. It makes hacking into your accounts much more challenging. You can use a mix of biometrics, authenticator apps, and one-time passwords. Even if an attacker can guess your password and breach through the first attempt, the additional layers of defense will block them.
Use Strong Passwords
Strong passwords were needed before the era of ChatGPT. But now you need super-strong passwords. What we mean by this is you need to create passwords that include a mix of letters, numbers, and symbols. Use special characters in them and make your passwords more than 15 characters in length. Don’t use the same password for all your accounts; never reuse passwords. If you can’t remember all your passwords, start using a password manager and don’t never save your passwords in your web browser.
Make your Mobile Device Extra Secure
Turn off the auto-connect setting for Wi-Fi and always use a VPN. You should also turn on the option to remotely wipe your phone if it gets stolen. Never store your data on your mobile phone. Mobile devices are known to be less safe than laptops and desktops, so you have to implement the best cybersecurity measures when you’re using them. Don’t share your personal information on social media or any online accounts where your phone number is shared with others. Try not to disclose your phone number online, since your phone can be the entry point to your email account and other accounts.
Update and Patch Often
AI automation attack tools are trained on fed data, so if you constantly update your network systems and users’ details, AI cannot exploit them so quickly. Ensure you often update your software and patch vulnerabilities, fix the latest bugs, and upgrade to the latest operating systems. These simple measures can mitigate a host of vulnerabilities that hackers are known to exploit to access your devices.
How to Respond to AI-Powered Cyber Threats?
Organizations should apply stringent verification protocols and processes to combat financial fraud from AI-based phishing and deepfake scams. They should implement behavior analytics technologies, track unusual transaction patterns, and provide the best employee education to recognize fraudulent requests.
They should also work on their incident response planning to minimize potential damage. Knowing how to prevent AI-powered cyber attacks is just as important. A good AI incident response plan will include immediate action steps for isolating affected systems. It should also help them analyze breaches to prevent future incidents and communicate strategies for managing external relationships.
The next step is implementing strong access controls and encryption to protect sensitive data. Companies must train their staff to recognize and respond to emerging AI-based threats.
They should employ AI threat detection and security solutions to respond to AI-based cyberattacks in real-time. Regularly patching and updating their systems can mitigate potential vulnerabilities. Also, when engaging with deepfakes or any social engineering scams, it’s a good practice for employees to verify with the actual person themselves instead of just approving requests. All these measures can help deal with and respond to AI-powered cyber threats.
Enhance Your Threat Intelligence
See how the SentinelOne threat-hunting service WatchTower can surface greater insights and help you outpace attacks.
Learn MoreReal-World Examples of AI-Powered Cyber Attacks
In 2019, hackers employed AI-powered voice technology to mimic the voice of a CEO and tricked a financial executive into sending $243,000 to a false account. The AI perfectly mimicked the speech pattern, accent, and tone, making it almost impossible to detect.
Another instance was spear phishing attacks using AI on major companies. The AI browsed social media profiles to design personalized messages about recent company events and used correct industry terminology. Success rates for these attacks were nearly three times those of regular phishing attacks.
HP Wolf Security also found hackers using AI to generate malware, write malicious scripts, and inject them into codebases. Threat actors also embedded malware in image files and used it across malvertizing campaigns. HP found ChromeLoader campaigns getting more prominent, and the malware loaded into browser extensions, taking over victims’ browsing sessions and redirecting their searches to attacker-controlled sites.
Mitigate AI-Powered Cyber Attacks with SentinelOne
SentinelOne can chart attack paths with its patented Storylines technology, correlate security events, and reconstruct historical timelines for deeper analysis. It’s Offensive Security Engine with Verified Exploit Paths can predict attacks before they happen. SentinelOne can also remediate critical vulnerabilities with one-click remediation and rollback unauthorized changes.
Unlike traditional signature-based solutions, SentinelOne’s behavioral AI recognizes malicious actions rather than known code patterns, making it particularly effective against novel AI threats. The platform automatically contains threats upon detection, preventing lateral movement and data exfiltration.
SentinelOne can fight against AI-powered malware, ransomware, zero-days, phishing, social engineering, and other cyber threats. It enables endpoint protection and detects subtle compromise indicators for all users and devices. SentinelOne also employs AI threat detection and continuous monitoring, which doesn’t require human oversight. It also addresses security silos and can ensure constant compliance with regulatory frameworks like SOC 2, HIPAA, NIST, CIS Benchmark, and others. SentinelOne’s global threat intelligence is always up-to-date, and when combined with Purple AI, its gen AI cybersecurity analyst, organizations can get the latest security insights. Its agentless CNAPP helps companies achieve holistic security, and SentinelOne can perform periodic and regular cloud security audits and vulnerability assessments.
Conclusion
AI-powered cyber attacks aren’t going away anytime soon. Since attackers use artificial intelligence to enhance their skills, organizations must also evolve their security strategy. Strong authentication, regular updates, staff training, and strong AI security systems offer a multi-layer protection system to detect and counter such advanced attacks. While AI attacks are advancing, security technologies are also maturing similarly.
You must understand how cyber criminals use AI and counter it with their tools. Your organization’s defenses must be iterative, and you need to adopt a proactive security stance.
Secure your organization with SentinelOne today.
FAQs
AI-powered cyberattacks are any type of threats launched, crafted, or created with the help of AI. They can be threats like social engineering campaigns or phishing emails written with AI. They could also be tools used to automate certain aspects of attacks, such as researching victims or bypassing specific security measures. AI-based attacks can also discover potential vulnerabilities, turn off security operations, and create malware that can adapt and stay undetected over time.
After you’ve detected an AI-driven threat, quarantine impacted systems simultaneously to prevent further propagation. Activate your incident response team to evaluate the attack vector and contain damage. Preserve evidence in its original format for forensic examination while you eliminate the malicious code. Watch network traffic for unusual patterns that may indicate continued activity. Identify what data may have been accessed and adhere to any legal notification requirements. Finally, offer a thorough post-incident review to identify security weaknesses and take additional steps against future such attacks.
Zero Trust architecture thwarts AI attacks by authenticating everyone who tries to access resources, regardless of location or position. It does away with the notion of an internal trusted network, viewing every request for access as malicious. Through strict identity authentication, least privilege access, micro segmentation, and continuous monitoring, Zero Trust significantly lessens the attack surface available by AI technology. Zero Trust prevents lateral mobility across networks and contains the damage even when first-line defenses are compromised.
AI-driven cyber attacks are growing because they radically enhance attackers’ abilities while decreasing the needed expertise. These tools can analyse enormous datasets to find vulnerabilities, adjust automatically to defense efforts, and run constantly without operator intervention. AI makes it possible to deliver highly tailored social engineering attacks at scale and create realistic deepfakes for fraud purposes. As AI software becomes more available and advanced, even less-talented attackers can initiate sophisticated campaigns that would have previously necessitated elite hacking units.