AI Deepfake Detection Platforms for Enterprise: Reality Defender vs. Sensity AI vs. GetReal Security

No vendor in this category publishes standard list pricing for enterprise deployments. Treat any number you see quoted secondhand as unverified until you get it in writing from the vendor.

The three platforms take meaningfully different technical approaches, and that difference should drive most of your shortlisting decision more than any feature checklist.

Reality Defender describes an ensemble approach that runs a large number of detection models in parallel across a submitted piece of media and produces a combined authenticity score, rather than relying on a single classifier per media type. This is designed to reduce the risk of any single detector being fooled by a new generation technique, at the cost of needing to keep a wide model roster current as generative tools evolve.

Sensity AI's architecture is built around multilayer forensic analysis: pixel-level visual artifact detection, voice spectral analysis, and file-level forensics such as metadata, codec, and timestamp inconsistencies. Sensity also emphasizes attribution and threat intelligence, meaning the product is built to trace where a manipulated asset originated and how it has spread across platforms, not only whether a single file is fake. That intelligence layer is a genuine differentiator for investigative and law enforcement workflows, but it means the product is oriented toward analysis of content that already exists rather than screening a live interaction as it happens.

GetReal Security's architecture pairs deepfake detection with continuous identity verification, meaning the platform is not only asking whether a video or audio stream shows signs of synthetic manipulation, it is also trying to continuously confirm that the person on a call is who they claim to be, using identity signals alongside forensic media analysis. This dual approach is why GetReal positions itself specifically around live meeting and call protection rather than batch analysis of submitted files.

Reality Defender operates as a cloud SaaS platform with enterprise contract options; the vendor does not present a documented on-premise deployment path in its public materials.

Sensity AI explicitly supports both cloud and on-premise deployment, which the vendor positions around government and law enforcement customers who have data residency or chain-of-custody requirements that a shared cloud environment cannot satisfy.

GetReal Security's public materials describe a cloud-based platform with real-time integration into meeting and voice systems. The company's site does not publish specific infrastructure detail on whether an on-premise or private-cloud option exists, so buyers with strict data residency requirements should confirm this directly in a sales conversation rather than assuming either way.

Reality Defender's public materials reference a family of named products rather than a single generic API: a scanning product, a general API product, and products aimed specifically at call center and video meeting protection. The company also describes turnkey integrations into existing applications for developers who want to embed detection directly rather than use a standalone product.

Sensity AI offers API and SDK access for programmatic integration plus a web application for manual, ad hoc analysis, which fits an investigative workflow where an analyst uploads a specific piece of suspect media rather than an automated pipeline screening every inbound call.

GetReal Security is the most explicit of the three about named third-party integrations: it lists native connections to Microsoft Teams and Cisco Webex, with Zoom coverage added on the company's public roadmap, plus a large list of identity and access management integrations including Okta, Microsoft Entra, and CyberArk. That IAM integration depth reflects GetReal's identity-verification-plus-detection positioning; it is built to sit inside an existing identity stack rather than operate as a standalone scanning tool.

If your use case is protecting live executive video calls, GetReal's named conferencing integrations and Reality Defender's meeting-focused product both deserve a closer look. If your use case is analyzing media after the fact, evidentiary review, or content moderation, Sensity's API, SDK, and web app model fits that workflow more directly.

Reality Defender's API and SDK model requires engineering time to wire into whatever workflow triggers a scan (an inbound call, an uploaded file, a KYC step), plus an ongoing process for a fraud or security analyst to triage flagged results and decide what action follows a high-risk score. The named products aimed at call centers and meetings reduce integration effort compared to a raw API but still require someone to own alert response.

Sensity's workflow is closer to an investigative tool than an automated screening layer: an analyst submits media and reviews a forensic report, which fits a smaller team doing case-by-case verification (legal, trust and safety, law enforcement) better than a high-volume automated fraud pipeline unless the API is wired into a broader case management system.

GetReal's continuous identity verification model is designed to run passively during live meetings once integrated with a conferencing platform and IAM system, which lowers per-call analyst effort but raises the upfront integration lift, since it needs to be connected to both your meeting platforms and your identity provider to deliver its stated value.

In all three cases, budget real analyst time for false positives. No vendor here publishes a verified false-positive rate for production enterprise traffic, and a detection alert on an executive call is exactly the kind of event that needs a fast, defensible human decision, not an automated block.

None of the three vendors publish standard enterprise pricing on their public websites as of this writing. Reality Defender advertises a limited free tier (reported at roughly 50 scans per month) for individuals or light evaluation use, with enterprise pricing handled through a sales conversation. Sensity AI directs prospective customers to request a trial or contact sales, with custom enterprise pricing and no published self-serve tier for smaller teams. GetReal Security's site routes all pricing inquiries to a sales demo request with no published figures.

This is normal for this category given the deal sizes and per-customer integration work involved, but it also means any pricing figure you see quoted in a comparison article, including this one, should be treated as unverified until the vendor confirms it in writing for your specific deployment scope. Ask each vendor directly for pricing tied to your expected call or scan volume, number of integrated conferencing platforms, and whether on-premise deployment (where offered) carries a different pricing structure than cloud SaaS.

Reality Defender Strengths: broad media type coverage under one platform, an ensemble detection approach intended to reduce single-model blind spots, and named products aimed specifically at call center and meeting fraud rather than a generic API alone. Limitations: no documented on-premise deployment option in public materials, and as with every vendor here, no independently verified detection accuracy figures for production enterprise traffic.

Sensity AI Strengths: forensic depth on visual manipulation, attribution and threat intelligence capability to trace where manipulated content originated and how it spread, and both cloud and on-premise deployment for organizations with strict data handling requirements. Limitations: not built as a real-time live-call screening product, audio detection is positioned as secondary to visual analysis, and there is no published self-serve pricing tier for smaller teams evaluating on a limited budget.

GetReal Security Strengths: the only one of the three built explicitly around continuous identity verification combined with deepfake detection during live meetings, the deepest named list of video conferencing and IAM integrations, and a clear roadmap of expanding conferencing platform coverage. Limitations: less public technical detail on underlying detection architecture and infrastructure options compared to the other two, and, like its peers, no independently verified accuracy benchmark.

Choose Reality Defender if you are a mid-size to large fraud prevention or trust and safety team that needs broad media type coverage (video, audio, and image) under a single API and wants named products for call center and meeting protection without building your own detection pipeline from a raw API.

Choose Sensity AI if your primary need is forensic investigation and attribution rather than live-call screening, for example a legal, law enforcement, trust and safety, or content moderation team that needs to verify specific pieces of suspect media and produce a defensible forensic report, and especially if on-premise deployment is a hard requirement for data handling reasons.

Choose GetReal Security if your top priority is protecting live executive video calls and meetings and you already have an identity provider (Okta, Entra, or similar) you want detection tied into, since GetReal's integration depth is built specifically around that continuous identity-plus-detection model rather than after-the-fact file analysis.

None of these three is a universal best choice. A team running a high-volume call center fraud operation, a digital forensics unit handling evidentiary review, and a security team protecting a handful of C-suite video calls each have a different correct answer here, and it is not the same vendor in all three cases.

Deepfake detection software is not always the right fix, and a vendor conversation should not be your first move in every case. If your actual exposure is executive impersonation over a phone call or a wire transfer request that arrives through an unverified channel, a procedural control, a callback verification policy to a known number, a shared verbal code word for high-value transfer requests, or a mandatory second-channel confirmation for any change to payment instructions, closes most of that risk at close to zero cost and does not depend on a detector correctly flagging a novel synthetic voice sample it has never seen before.

Detection software also is not a substitute for basic authentication hygiene. If your organization has not yet closed off SIM-swap-enabled account takeover, or if your finance team still approves wire changes from an email request alone, fix those gaps first; a deepfake detection platform sitting on top of an unverified approval process still leaves the underlying process unverified.

Small teams without a dedicated security or fraud operations function should also weigh the ongoing triage burden honestly. A detection platform that flags suspicious calls or media is only useful if someone owns responding to those flags in a defined time window. If nobody currently owns that response process, standing up the process and a lightweight verification protocol is a better first step than buying detection tooling that will generate alerts nobody acts on.

For background on the broader threat these platforms address, see our coverage of enterprise deepfake fraud defense, the BlueNoroff deepfake Zoom crypto ClickFix campaign, and defending against AI voice cloning vishing used for CFO wire fraud, all of which describe attack patterns these platforms are built to catch.

Run the same checklist across all three vendors before you compare their sales pitches. A vendor's own demo will always show its best case; your PoC should test the case that actually matters to your organization.