Reality Defender

How do you determine what’s real? Reality Defender CRO Brian Levin says answering that question in real time is the next required layer of cyber. In his latest Cyber Conversations clip from RSAC, Brian frames what we're building at Reality Defender in plain terms: a trust infrastructure for AI. Monitor every piece of media that gets created and determine, in real time or asynchronously, whether it's been compromised. Then plug those determinations straight into the existing and traditional cyber landscape: identity, fraud, insider threat, executive impersonation, social engineering.

Every security stack has a perimeter, an identity layer, and an endpoint layer. Soon every one of them will need a detection layer. That's the conversation we're bringing to Gartner® Security & Risk Management Summit in two weeks (June 1–3, National Harbor, MD). Voice on a support call. Face in a video meeting. If the stack can't answer "is this real?" the moment those signals hit your systems, the rest of it doesn't matter.   We'll be on the floor with the teams already running detection in production today. If you're coming to Gartner®, let's connect and compare notes: https://lnkd.in/emtXm7pp (GARTNER is a trademark of Gartner, Inc. and/or its affiliates.)

Most organizations have an incident response plan. Almost none have one for a deepfake. The first 60 minutes after a fabricated CEO video, a cloned voice on a wire transfer call, or a fake press release decide whether the story stays internal or becomes a quarterly headline. We built a free playbook for that hour: a triage decision tree, the legal-and-PR coordination loop most teams skip, communication templates you can adapt before you need them, and the technical steps for capturing evidence. Arm your team with what they need to disarm deepfakes and download the guide here: https://lnkd.in/eZmwWk5D

A Fortune 500 hiring team caught a $2M fraud at the interview stage. The signal that tipped them off was a deepfake voice on a video call. Our latest case study walks through a hiring pipeline that passed every standard check: identity verification, credential validation, reference calls. The call in question kept on until the candidate's voice triggered the detection layer. From there, the attack chain unraveled. The lesson isn't that the standard checks failed. They worked exactly as designed against the attacks they were built for. The newer attack of synthetic media at the interview stage needs detection where the human conversation actually happens. Read the full case study: https://lnkd.in/eq5g7dhG

Three things break single-model detectors in production: adversarial optimization, codec compression, and domain shift between training data and what your traffic actually looks like. Each one compounds the others. Parallel multi-model detection running simultaneous classifiers with different architectures and feature sets is built for the conditions a single model can't survive. Read the full breakdown on why lab benchmarks fail in the real world, and learn the three questions every procurement team should be asking deepfake vendors: https://lnkd.in/eevCP28P

The CAPTCHA problem moved to voice. For two decades, fraud and identity teams have leaned on a quiet assumption: the caller on the other end is a person. Voice biometrics, call-flow heuristics, identity verification flows — all of it built around that assumption. AI voice agents break it. This clip from our recent agentic AI webinar walks through which fraud and identity controls still hold up against autonomous callers, which ones quietly fail in production, and where detection has to move to fill the gap. Watch the full replay on demand: https://lnkd.in/eGv-4kwH

Five days at IBM Think. One question wouldn't go away: what's our response window? We came to Boston expecting deepfake questions from fraud and elections teams. We left having had the same conversation with security leaders in healthcare, defense, manufacturing, insurance, and banking. Because with synthetic media now part of the standard adversary toolkit, the threat of deepfakes isn't sector-specific anymore. A few things stuck with us from the week. The conversation has moved from "is this real?" to "how fast can we know?" Detection has to live where decisions actually get made — inside contact centers, inside meetings, inside the workflows that authorize wires and grant access. And the teams pulling ahead aren't the ones with the most tools. They're the ones treating AI-generated content as something bound to happen. Thanks to everyone who stopped by, joined the sessions, and traded notes in the hallway.

Keith Enright, Google's former Chief Privacy Officer and a founding member of Reality Defender's Ethics Committee, argues the question on every board agenda ("how do we keep our people from misusing AI?") is the inverse of the one that matters: "how will we know when someone uses AI to impersonate us?" The full Q&A with Keith goes into where current regulation falls short, why employee impersonation is moving faster than the executive cases that get the press, and what the next governance questions look like for boards taking AI seriously. Read the full conversation: https://lnkd.in/e9QaH5Sx

For analysts working in fraud, compliance, or legal contexts, a probability score is the start of an answer, not the end of one. They need to be able to point at which models contributed, what context analysis surfaced, and what the metadata revealed — and why all of it added up to the call. Aphrodite B.'s latest piece walks through exactly that: how Reality Defender arrives at an image scan result, what each of the three signals (model output, Context Aware analysis, metadata) contributes, and what "Suspicious" actually means when it lands in your queue. Read more on the Reality Defender blog: https://lnkd.in/eGQ_f5Ra

Your SOC knows how to contain ransomware. Your fraud team knows how to flag a suspicious wire. But when an executive's voice on a video call asks for an emergency credential reset, who owns that call — and what's their next move? Most security teams don't have an answer. Deepfake incidents fall into a gap between incident response, fraud, and identity, and that gap is exactly where attackers are operating. The Deepfake Incident Response Playbook from Reality Defender is built for that gap. Inside: a four-tiered response framework, clear ownership lines across SOC, fraud, and identity teams, and the verification steps to take in the first sixty seconds of a suspected impersonation. Download it before you truly need it: https://lnkd.in/eZmwWk5D