The cybersecurity landscape is dramatically shaped by the advent of generated AI. Attackers are currently leveraging large-scale language models (LLMs) to impersonate trustworthy individuals, automating these social engineering tactics at scale.
Let’s look at the stats of these rising attacks, what is driving them, and how to actually prevent them, rather than detecting them.
The most powerful person on the phone may not be the real thing
Recent threat intelligence reports highlight the increasing sophistication and prevalence of AI-driven attacks.
In this new age, trust cannot be assumed or simply detected. It needs to be proven deterministic and in real time.
Why the problems are increasing
Three trends converge to make AI spoofing the next big threat vector.
AI makes deceptions cheap and scalable. With open source audio and video tools, threat actors will impersonate people who have a reference material for a few minutes. Virtual collaboration exposes trust gaps: Zoom, teams, slack and more tools. Attackers take advantage of that assumption. Defenses generally rely on probability rather than evidence. The deep-fark detection tool uses facial markers and analysis to guess whether someone is real or not. That’s not enough in a high stakes environment.
And while endpoint tools and user training may help, they are not built to answer important questions in real time. Can you trust this person I’m talking to?
AI detection technology is not enough
Traditional defenses focus on detection, such as training users to discover suspicious behaviors or using AI to analyze whether someone is fake. But deepfakes are getting too good and too fast. You cannot use probability-based tools to combat AI-generated deceptions.
Actual prevention requires a different foundation based on proven trust rather than assumptions. In other words,
Identification: Only validated certified users must be able to participate in sensitive meetings and chats based on encryption credentials, not passwords or codes. Device Integrity Check: If your device is infected, jailbreak, or non-compliant, it becomes a potential entry point for the attacker, even if it is confirmed. These devices will be blocked from the meeting until they are repaired. Visible trust indicators: Other participants must prove that each person in the meeting is who they say and is on a secure device. This removes the burden of decisions from the end user.
Prevention means creating conditions that are not difficult to impersonate and impossible. This is how you can participate in risky conversations like board meetings, financial transactions, vendor collaborations, and more before closing AI Deepfake Attacks.
Detection-based approaches Prevention approaches flags are abnormally blocked from unauthorized users from relying on heuristics and speculations after the flags have been generated.
Eliminate the threat of deepfakes from your phone
RealityCheck By Beyond Identity was built to fill this trust gap within collaboration tools. We provide all participants with visible, verified identity badges backed by cryptographic device authentication and ongoing risk checks.
Currently available on Zoom and Microsoft Teams (Video and Chat), RealityCheck:
Make sure that all participants are authentic and that the compliance of their certified devices is verified in real time. Even unmanaged devices display visual badges to show other people confirmed
If you want to see how it works, Beyond Identity hosts a webinar where the product is working. Sign up here!
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