How Hackers Are Using Artificial Intelligence to Attack Companies

1. AI-Generated Phishing

One of the most traditional signs of phishing was the quality of the text: grammar mistakes, awkward translations, inconsistent formatting.

Generative AI has all but eliminated that weakness. Today it is possible to generate grammatically perfect phishing emails, in the right tone and style, personalized at scale with public information about the victim, all in a fraction of the time it would take by hand.

The result is a higher volume of campaigns, each of higher individual quality, making visual identification by the user far more difficult.

2. Voice and Video Deepfakes (Advanced Vishing)

Perhaps the most worrying development of recent years: AI voice-cloning tools that need only a few seconds of public audio (an interview, a social media video) to generate a convincing synthetic voice.

There are already documented cases of successful financial fraud in which an employee received a call with an executive's cloned voice requesting an urgent transfer, and authorized the transaction believing they were speaking with the real person.

3. Malware Generation and Evolution

Generative AI is used to speed up the creation of malware variants, helping evade traditional antivirus signatures through small, automated modifications to the code.

It also lowers the technical barrier to entry: attackers with limited technical knowledge can, with AI assistance, develop working malicious tools that previously would have required advanced programming skills.

4. Automated Vulnerability Hunting

AI-based tools are already used to automate part of the reconnaissance process and the search for exploitable vulnerabilities in publicly exposed systems, accelerating steps that previously required slower, manual analysis.

This shortens the time between a vulnerability being exposed and its active exploitation by automated attackers scanning the internet for targets.

5. Fake Profiles and Social Engineering at Scale

Generative AI is also used to create convincing fake profiles on professional social networks, used to get close to targeted employees before a social engineering attack, a more elaborate and patient approach than traditional mass phishing.

How Companies Should Adapt

The fundamental defenses are still the same as always, but they need to be reinforced with the understanding that attacks are now more convincing:

• Second-channel verification processes: no wire transfer or critical action should depend solely on a call or an email, no matter how convincing it seems
• Up-to-date awareness: teams need to know that voice deepfakes are already a real threat, not science fiction
• Phishing-resistant multi-factor authentication: reduces the impact even when social engineering succeeds
• Monitoring for anomalous behavior: to quickly identify suspicious activity, even when the initial access looks legitimate
• Recurring security assessments: to shrink the attack surface before automated tools find it

Conclusion

AI didn't create new types of attack from scratch; phishing, malware, and social engineering already existed. What has changed is the scale, quality, and speed at which these attacks can be carried out.

Companies that update their defenses based only on threats from years ago are, in practice, preparing for an adversary that no longer exists in the same form.