Inside Reality Defender’s AI Red Team

At Reality Defender, protecting digital integrity isn’t a checkbox, it’s an engineering discipline. Our AI Red Team exists to make sure the models that power our deepfake detection platform stay current and resilient. We don’t wait for adversaries to discover weaknesses; we try to break our own systems first, learn from every failure, and ship a stronger product to customers. That proactive posture is how we translate research into real-world robustness.

In this blog, we’re taking a closer look at what AI red teaming is, how it strengthens deepfake detection, and how our own team applies continuous adversarial testing to stay ahead of emerging threats. 

What is AI Red Teaming?

Think of AI red teaming as ethical adversarial engineering. A structured, ongoing practice that models the mindset, tools, and tactics of adversaries who would misuse AI systems. Red teams typically include multidisciplinary teams of researchers, threat analysts, engineers and domain experts such as voice and video specialists. The team is tasked to create realistic attack scenarios to probe models for edge cases, blind spots, and failures.

The objective is straightforward: discover practical attack paths that could cause misclassification or unintended behavior, and then translate those findings into prioritized fixes, testable mitigations, and measurable validation. It’s not a one-off pen test but an integrated cycle of simulate → expose → remediate → verify that informs development priorities and product roadmaps.

How Red Teaming Strengthens Deepfake Detection

Deepfake detection faces an ever-shifting adversary. New generative models, open-source manipulation tools, and social-engineering techniques emerge almost daily, each capable of evading safeguards that worked yesterday. This constant evolution makes static testing obsolete and proactive red teaming essential.

In this context, red teams act as the pressure test for detection systems. They combine manipulated video, audio, and contextual cues to replicate realistic attacks: cloned voices in customer service calls, real-time synthetic video in onboarding flows, or mixed-media impersonations across communication channels. These controlled simulations reveal how detection models perform under true-to-life conditions, not ideal lab scenarios.

The insights gathered translate directly into model retraining and product-level defenses, ensuring detection systems evolve alongside — and ahead of — emerging threats. In short, red teaming keeps deepfake detection grounded in the reality of how manipulation happens, turning adversarial creativity into continuous resilience.

Inside Reality Defender’s AI Red Team

Our approach rests on four tightly integrated practices:

1. Threat monitoring and research. We ingest new research and tool releases the moment they appear, then rapidly triage which are likely to be weaponized. This gives us a prioritized list of techniques to reproduce and stress-test against our models. For example, in September 2025 the team identified more than 60 new tools and techniques capable of generating deepfakes across modalities.
2. Real-world attack simulations. We build realistic attack chains that mirror how bad actors strike: combining social engineering, voice cloning, contextual prompts, and media manipulation. Rather than synthetic lab experiments, our simulations emphasize the operational context — call centers, onboarding flows, and media publishing pipelines.
3. Systematic adversarial testing. With repeatable test harnesses and attack catalogs, we deliberately push models into failure modes — the edge cases that are invisible in standard benchmarks but devastating in production.
4. Close-the-loop remediation. Findings are shared immediately with AI scientists and product engineers. Remediation is verified through regression suites and targeted validation so fixes stick, rather than patching a symptom. Each release is tested systematically against both standard benchmarks and an evolving adversarial dataset drawn from red team findings. This “find, fix, validate” loop is how we continuously raise the platform’s robustness.

This process is cross-functional by design; red team outputs drive model updates, inform product controls, and shape how we speak about risk with customers. Our red teaming insights are woven into the fabric of the company, involving close collaboration with AI and Data teams for remediation, the Product team to inform the roadmap, and even Sales and Marketing to transparently communicate our deep commitment to security and trust.

Example: Testing the Human Factor

Recent internal research at Reality Defender highlighted how powerful simulations can be. In one controlled deepfake voice exercise, a cloned “executive” convincingly persuaded a junior employee in a mock financial institution to complete a series of small, seemingly harmless requests, the kind of early attack that paves the way for larger breaches.

The exercise succeeded in demonstrating how quickly trust can be established through voice alone: within minutes, the employee accepted the caller’s identity, offered internal information, and never escalated the interaction for verification. The test didn’t involve any real organization or data, but it showed how social context and low-latency speech can bypass human skepticism.

You can read more about that scenario in our Anatomy of a Deepfake Social Engineering Attack case study.

Turning Adversarial Insight Into Real-World Resilience

Red teaming delivers tangible value to customers through three core outcomes:

Trust and safety. Proactive testing reduces the risk of detection failures in the field and strengthens confidence in automated verification systems.

Performance and reliability. Adversarial discovery exposes weaknesses that conventional evaluation overlooks, leading to more robust performance across varied real-world content.

Strategic security depth. While traditional cybersecurity protects infrastructure, red teaming protects the logic of the model — the layer where deepfakes and manipulated media pose the greatest threat.

In a market where trust is everything, these practices demonstrate a commitment to safety that extends beyond compliance — to genuine, measurable resilience.

Case in Point: The Sora 2 Experiment

In late September 2025, Reality Defender researchers conducted a controlled red teaming experiment against OpenAI’s newly launched Sora 2 platform, a system that included identity verification checks meant to prevent impersonation. Within 24 hours, the team successfully bypassed the platform’s multi-step safeguards using real-time, high-fidelity deepfakes of CEOs and public figures.

The purpose of the exercise wasn’t to expose a competitor’s flaws, but to demonstrate a broader truth about AI risk: even the most advanced generative platforms can’t reliably police their own outputs. In this test, Sora’s identity verification failed to flag synthetic media, while Reality Defender’s deepfake detection systems identified every instance in real time.

This is exactly what red teaming exists to uncover. The experiment revealed how verification systems optimized for user experience often overlook the evolving sophistication of manipulated content and why external, adversarial testing is a necessary layer of defense for any organization deploying generative or detection AI.

In a world where deepfake-driven fraud at financial institutions has surged more than 2,000% year over year, these insights matter far beyond a single platform. They highlight how proactive adversarial testing — not reactive patching — keeps detection systems, verification workflows, and user trust one step ahead of attackers.

What’s next

Over the next year, Reality Defender’s red teaming program will focus on the next major challenge in detection: real-time, multi-modal deepfakes that combine voice, video, and contextual signals in interactive settings. As generative tools become faster and more accessible, we’re expanding our simulations to include low-latency, conversational attacks and coordinated cross-channel impersonations.

All testing is conducted in secure, isolated environments and within strict ethical guidelines. Each exercise directly informs new model updates and provides intelligence that helps our partners prepare for the same evolving threats. We’re also continuing close collaboration with research and industry partners to ensure our models are validated against a broad range of adversarial techniques.

Our goal remains simple: build AI systems that are demonstrably resilient, not just reactive.

Test our deepfake detection models yourself. Developers and security teams can start building with Reality Defender’s API today — with 50 free scans per month — and see how robust, real-world detection performs under their own workflows.

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