AI Cyber Defense: How to Spot AI Cyber Attacks

Security concerns and data breaches are a perennial problem on the internet. Last year, a mega breach exposed 26 billion records, and in 2025, massive data leaks are continuing to plague businesses and consumers alike.

Now, though, advances in generative artificial intelligence have added a new dimension to cybersecurity concerns.

This might look like a scam that only AI makes possible, like a fake voice or video call, or it could be a supercharged phishing attempt. Often, a company’s AI tools themselves can be a target: Last year, 77% of businesses reported a breach to their AI systems.

Here, we’ll explain all the top ways that AI and security threats can tangle with each other, along with all the top tips for getting away unscathed.

In this guide:

• Types of AI Cyberattacks You Should Know About
• How Common Are AI Cyber Attacks?
• How to Spot AI Cyber-Attacks
• Conclusion: Start Preparing for AI Cybersecurity Threats Now

Types of AI Cyberattacks You Should Know About

Phishing attempts can target anyone, particularly those who have seen their personal data exposed in previous hacks. Given that billions of people have been exposed, you’re likely among them. Using the large language models of generative AI, threat actors can now operate far more efficiently than before.

When the FBI issued a warning about AI cyber threats in May 2024, they put AI phishing attempts as their top priority, right ahead of  another top concern, “voice/video cloning scams.” This makes sense: Both of those issues are among the biggest ones that consumers are likely to be tricked with.

Over on the business side, AI can be used to streamline malware and ransomware attacks, which remain a big vector for security breaches. While the total number of ransomware attacks has dropped in recent years, the total reported amount lost to these attacks still passed $800 million in 2024, so they continue to be a threat.

Finally, if your business trains its own AI model, your operation may be at risk for data poisoning, a threat that relates to the training dataset itself being compromised.

How Common Are AI Cyber Attacks?

Aside from obvious cases like deepfakes or audio clones, it’s hard to determine if AI has been used in a cyber attack. However, in 2024, VIPRE Security Group estimated that 40% of all phishing emails targeting businesses were generated by AI. In total, according to a Deloitte analysis, the impact of generative AI on all cyber attacks will be a 32% growth that extends the total losses to $40 billion annually by 2027.

The global market for the deepfake detection processes that stem the growth of this new type of security concern are estimated to grow 42% between 2024 and 2026 to reach a global market of $15.7 billion.

That total dollar amount isn’t the full picture, though: Cybersecurity professionals are using AI themselves to combat threats. One 2024 survey found that monitoring network traffic was the main use case for AI in cybersecurity, with 54% of respondents using it for that purpose. Other uses included generating defense tests, predicting future breaches, and automating incident response.

It’s also worth noting that in some cases, the use of AI is purely speculative. Plenty of security firms and government agencies have warned about the potential for AI to help ransomware programs adapt in real time for precision targeting or evading detection. However, we don’t have documentation of how common those practices are in reality.

How to Spot AI Cyber-Attacks

Practically speaking, your personal cybersecurity comes down to a laundry list of effective habits, paired with a few helpful tools like a high-quality VPN, and a healthy dose of good luck. Here’s where to start.

How to Spot AI Phishing Attempts

Phishing attempts will most frequently arrive in the form of an email or SMS message that asks you to click a link or enter a password. The problem is that it will be a fake, designed by AI tools to include the official styles or logos that will convince you to part with your personal information.

AI phishing emails are longer than ever and have fewer spelling mistakes. To avoid a phisher, look for these giveaways:

• Unusual requests – This may be a request for money, personal information, or anything out of the norm.
• False sense of urgency – Scammers don’t want to give you time to think. Look for phrases like “Your account will be closed in 24 hours.”
• Links that don’t match the domain – Scammers won’t have the correct email address or domain name, but they may have one that looks very similar

The best advice? Don’t think you’re above being tricked. A shocking 98% of CEOs can’t spot all the signs of a phishing attack.

How to Spot Voice Clones

Scammers might impersonate an elderly person’s grandchild over the phone with a text-to-voice generator, or might pretend to be a worker’s Fortune 500 CEO instead. In fact, they’ve already been using both of those tactics even before AI voice tools made it even easier.

To catch a voice clone scam, look for the following:

• Anything unusual or urgent – Voice clone scams are phishing emails in audio form, so the same tips apply
• Unnatural pauses or robotic-sounding speech – Voice cloning technology isn’t perfect just yet
• Inconsistencies – Scammers will likely not know everything about the person they’re pretending to be

In all these cases, the goal remains the same as any traditional scam. Bad actors are always after either sensitive data or free money (often in the form of untraceable gift cards that can be resold for cash).

How to Spot Deepfakes

AI-generated videos can also trick people into revealing sensitive data. However, the technology isn’t so good that you can’t tell at all. There are a host of key elements that a fake talking head can’t quite match.

• Unrealistic anatomy – Anything might be off. Take a close look at the cheeks, forehead, eyes, eyebrows, glasses, facial hair, and lips.
• Unnatural movements – Check for any apparent reaction that doesn’t fit the context of the video.
• Inconsistent audio – Slow responses or warped audio can be a giveaway.

In the end, most of these giveaways might be a sign of a poor connection or a low-quality camera. Just remember not to commit to giving them money or personal data while on the call itself. Say you’ll call them back, and then contact them through a different channel that you trust.

How to Spot Malware

If your work computer was the victim of malware, it won’t matter if you’ve got an AI or non-AI equipped version. This is because the addition of AI helps scammers to “refine the same basic scripts” that would be used without AI, according to IBM. In fact, IBM’s X-Force team has not yet found evidence of threat actors actually using AI to generate new malware. As a result, IT professionals are taking the same approach that they would with non-AI malware, from patching assets to training workers.

As an employee, you’ll need to contact your IT team immediately to see if you can contain the damage. Here are the warning signs to look for:

• Lack of control – any browser redirects, pop-up windows, new tabs, or new browser extensions are an indication of malware.
• Changes to your homepage or search engine – Be wary of any sudden changes to your pre-set system.
• Freezing up – ransomware will lock up some or all access to your files.

How did the malware get there in the first place? Likely because someone in your company fell for a phishing attack.

How to Spot Data Poisoning

The term “data poisoning” refers to the act of compromising an AI model by messing with the data that it is trained on. It’s only a concern if your operation has its own AI model, but it can have a huge negative impact on the business. You can spot it by noticing any unusual input or output, such as:

• Outliers in the data – Unexpected patterns or anomalies in datasets are a tipoff of manipulation
• Poor predictions – A sudden change in the output of generative AI can be another sign
• Discrepancies with real-world models – AI models are supposed to reflect reality (hallucinations aside)

Data poisoning can dramatically impact an AI model, causing skewed outcomes that might go unnoticed for years. Catching them requires a little intiative and outside-the-box thinking. So far, at least, those are traits that we still can’t replicate with artificial intelligence.