Securing Video Calls in Real Time

A Real-Time Line of Defense for Modern Threats 

DeepfakeGuard is a live detection engine designed to protect enterprise communication from synthetic media risks. Built to run on major platforms like Zoom and Microsoft Teams, it identifies deepfake videos and voices during video calls, not just before or after. This real-time capability addresses a growing security issue for businesses and governments: the ability to trust who they are communicating with, especially in high-stakes or high-volume conversations. DeepfakeGuard’s approach is proactive it doesn’t just audit recordings after damage is done, but rather intercepts threats as they happen. 

The system integrates directly into video platforms via cloud or on-premise deployment. It can function across multiple systems simultaneously, offering businesses a unified tool for live authentication and anomaly detection. With integration flexibility and a design that avoids platform lock-in, it supports daily enterprise operations where video meetings are now central to decision-making, negotiations, and access control. 

Live Detection Built for Enterprise Use 

What makes DeepfakeGuard effective is not only its detection accuracy but also its ability to scale with enterprise demands. The engine is optimized to handle high volume meeting traffic with low latency, meaning it can run constant real-time checks without slowing down communication. In fast-moving sectors finance, health, legal services this speed is essential to avoid friction or service delays. 

Its enterprise security value comes from combining multiple safeguards into a seamless experience. Unlike tools that only review saved files, DeepfakeGuard runs detection mid-conversation, flagging possible threats without interrupting the flow. It offers organizations peace of mind that synthetic impersonation or unauthorized access won’t go unnoticed, even in large virtual conferences or remote environments. 

Core Features Supporting Security Goals 

DeepfakeGuard includes a set of features that address real-world enterprise concerns: 

• Compliance Recording: Ensures video logs are securely captured and validated with authenticity scoring. 

• Zoom Integration: Runs natively with Zoom to authenticate participants in real time. 

• Teams Integration: Works with Microsoft Teams without disrupting meeting flow. 

• Platform Agnostic: Can be used across multiple video platforms, avoiding vendor dependency. 

• Controlled Delivery: Detection results and alerts can be configured for visibility by moderators, hosts, or compliance officers. 

• Patient Comfort: Supports sensitive sectors like healthcare by ensuring real human interaction and secure environments. 

  
  

Each of these features connects to a different enterprise requirement regulatory, operational, or human while maintaining a single point of control. 

DeepfakeGuard’s Detection Architecture 

The system combines multiple detection engines to validate both video and audio signals. It uses real-time Captcha tests, biometric face tracking, voice liveness analysis, and behavioural verification. These components work together to confirm identity and intent, flagging anything synthetic or manipulated. Its machine-learning models are continuously updated to adapt to new deepfake techniques, helping the system evolve with changing threat patterns. 

The detection results can be delivered discreetly during calls or used for automated access control decisions allowing or denying sensitive steps like file sharing, payment approval, or login validation.  

Why This Innovation Stands Out in 2025 

In 2025, DeepfakeGuard received the Frost & Sullivan Best Practices Award for its contribution to enterprise-grade authentication. It was also showcased at Enterprise Connect as a leading example of secure video communication. What sets it apart is its focus on live, scalable, and platform-integrated security—not just experimental AI, but a deployable enterprise tool. This reflects a shift toward real-time trust systems that can be embedded in daily business infrastructure, rather than sitting on the sidelines as post-analysis tools.