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For years, security awareness training, especially around spotting deception and scams, relied on a simple premise: scams contain obvious errors. Spot the errors and you’d likely spot the scam. Typos, awkward phrasing, and poor formatting served as reliable red flags. That premise no longer holds.
Today’s AI-generated scam content is grammatically correct, contextually appropriate, and often indistinguishable from legitimate communications. The phishing email mimicking your bank’s tone doesn’t have spelling errors. The fake customer service page matches the design of the real site. The urgency-laden message from “IT” follows your company’s actual communication patterns.
How then must our defensive tactics change?
Most organizations invest heavily in perimeter defenses, endpoint protection, and network monitoring. These controls operate within a defined security boundary. But scam attacks typically target employees outside that boundary. Scams also target personal email accounts, social media feeds, and mobile devices used for both work and personal purposes.
When an employee receives a convincing phishing attempt on their LinkedIn account and clicks through, enterprise security tools don’t see it. Same with that compromised software package they downloaded from an AI-generated ad running on social media. That turned out to be an infostealer that grabbed all their session cookies including the ones for the corporate systems, and it stole the budget spreadsheets they were working on over the weekend for good measure.
I am not suggesting this is a new vulnerability. Credential reuse and phishing have been problems for years. What has changed is the scale and quality of content that threat actors are now producing. One individual with access to generative AI tools can create hundreds of convincing, personalized phishing emails or professional-looking ads that would have previously required significant time and skill to produce.
This means that the boundary between work and personal is just as porous as it was before, but the attacks on that boundary are getting more effective.
F-Secure’s recent analysis of AI-enhanced scams in 2025, we found that 89% of AI-enhanced scams focus on content generation suggests attackers have identified where AI currently provides the most practical advantage. Content generation is:
This concentration of effort tells us something important: attackers are using AI where it works, not where it’s flashy.
Given this landscape, savvy security teams will elevate several straightforward defensive measures.
Credential hygiene becomes critical. If personal account compromise is easier than ever, ensuring those compromises don’t extend to corporate systems matters more. Unique passwords for work accounts, hardware-based multi-factor authentication, and monitoring for credential stuffing attempts are basic but essential controls.
Security awareness training needs updating. Teaching employees to spot grammatical errors in phishing emails is no longer useful advice. More practical guidance includes verifying requests through separate communication channels, being skeptical of urgency framing and other psychological tricks, and understanding that professional-looking content may still be fraudulent.
Assume personal devices may be compromised. Any device that accesses both personal and corporate resources should be treated as potentially exposed. Zero-trust architectures that continuously verify rather than implicitly trust become more relevant.
Create reporting mechanisms without stigma. Employees are more likely to report suspicious activity or potential compromises if they don’t fear professional consequences. Early reporting can limit damage.
One upside to the AI-generated content tsunami? Some early research indicates that people may be more likely to report their own victimization if the lure was very convincing. AI-generated content may in fact be so convincing that it actually makes people feel less shame and guilt after falling for it. We need to do more research before knowing whether this silver lining is real, but early indications are interesting.
This brings up another elephant in the room, which is that measuring the true prevalence of AI use in scams is inherently difficult, both for the victims and researchers like me. People are bad at judging whether a scam uses AI, and the digital traces in some media, like text, are difficult to spot. We can usually identify when AI tools were clearly used for video and audio content generation, but sophisticated actors may use AI in ways that leave fewer obvious traces. Thus, our 89% figure likely represents a floor, not a ceiling.
The use of AI for scam content generation therefore appears to now be an established pattern rather than a still-emerging trend. Security strategies that accept this will be more resilient than those still oriented around scams containing obvious errors.
Similarly, since we know that the boundary between personal and professional digital life has always been porous, btu AI-generated content makes mistakes at the boundary more acute. Organizations that acknowledge this and design defenses accordingly will be better positioned than those that assume the boundary still provides meaningful separation.
