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 with references for a few minutes.
- Virtual collaboration reveals trust gaps: Tools like Zoom, Team, Slack, etc. assume that the person behind the screen is the person they claim. Attackers take advantage of that assumption.
- Defenses generally depend on probability rather than evidence: Deepfake detection tool uses facial markers and analysis Speculation If someone is the real thing. That’s not enough in a high stakes environment.
Also, endpoint tools and user training can be helpful, but 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,
- ID verification: Only verified certified users must be able to participate in sensitive meetings and chats based on encryption credentials, not passwords or codes.
- Device Integrity Check: If a user’s device is not infected, smashed or violated, even if the attacker’s identity is confirmed, it is still a potential entry point for the attacker. These devices will be blocked from the meeting until they are repaired.
- Visible trust indicatorsOther participants should do so look Each and every member of the meeting proves that they are who they say and they are 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.
| A detection-based approach | Preventive approach |
|---|---|
| After a flag abnormality occurs | Blocks unauthorized users from joining |
| Rely on heuristics and speculation | Uses encrypted proof of identity |
| User decision is required | Provide visible, verified trust metrics |
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 to Zoom and Microsoft teams (Video and Chat), RealityCheck:
- Ensure that all participants’ identities are realistic and permitted
- Verify device compliance in real time, even on unmanaged devices
- View visual badges to show you other people you have been 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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