Most people still picture deepfakes as manipulated videos of public figures going viral online. That’s no longer where the real damage is happening. In 2026, fraud is playing out inside live business conversations, onboarding flows, and voice calls where attackers impersonate trusted people and manufacture urgency in the moment. Here are 10 platforms shaping the response.
Sensity AI
Sensity AI detects synthetic media across images, video, and audio, and goes a step further by monitoring how that content spreads across platforms after it’s published. It’s a go-to for threat intelligence teams and media organizations that need to track deepfake campaigns, not just flag individual files.
Diopter AI tackles the live interaction problem that most detection tools can’t address. It monitors calls in real time across Teams, Zoom, Meet, Webex, and VoIP, tracking both synthetic media signals and conversational manipulation patterns as they develop. The platform scores each interaction and delivers a live fraud verdict so security teams can act before the call ends and the damage is done.
Reality Defender
Reality Defender integrates into production pipelines and screens video, audio, and images with low latency at scale. It’s become a reliable layer in financial services onboarding, catching AI-generated identity submissions before they become fraudulent accounts.
Pindrop Security
Pindrop has spent over a decade focused on voice fraud, building models calibrated specifically for telephony conditions and contact center environments. As voice cloning has become more accessible, its acoustic and behavioral analysis tools have become more relevant, not less.
DeepTrust
DeepTrust solves the combined attack problem in identity verification, validating both documents and biometrics together so attackers can’t exploit the gap between separate checks. Regulated industries with compliance obligations find it particularly useful.
GetReal Security
GetReal Security focuses on definitively authenticating specific high-value assets rather than screening content at volume. It’s used when the authenticity of a particular video, recording, or image carries real legal or financial weight.
Sift
Sift reads behavioral signals across digital journeys to catch fraud that media analysis misses entirely. It works alongside deepfake detection tools rather than replacing them, adding a layer that flags suspicious account behavior regardless of whether synthetic media was involved.
Hive Moderation
Hive handles deepfake detection at consumer platform scale through a fast, scalable API. It’s not a forensic tool, but it gives social platforms and marketplaces consistent coverage across millions of user-submitted assets without requiring human review queues.
Intel FakeCatcher
FakeCatcher bypasses the visual artifact problem by detecting physiological signals in facial video that AI-generated content can’t replicate. It catches sophisticated deepfakes that have been specifically optimized to fool standard detection methods.
Facia
Facia rolls facial recognition, liveness detection, and deepfake analysis into a single platform designed for regulated onboarding. It removes the interoperability gaps between separate tools and provides continuous protection through the full authentication lifecycle.
The Bottom Line
The organizations most exposed right now aren’t the ones without any detection tools. They’re the ones with solid content screening and no coverage for live conversations. That’s where the attacks are landing, and that’s the gap worth closing first.
