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GetReal Security

GetReal Security builds multimodal deepfake detection and continuous identity verification technology to help enterprises and governments counter AI-generated fraud and disinformation.

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70/100Meaningful Innovator

Overview

GetReal Security was incubated by venture studio Ballistic Ventures together with Dr. Hany Farid, a leading digital forensics and deepfake-detection researcher, and launched around 2023-2024. The company builds multimodal detection technology — analyzing audio, video, and image content together — to identify AI-generated and manipulated media, alongside continuous identity-verification tooling meant to counter synthetic-identity fraud.

Its $17.5 million Series A was led by Forgepoint Capital with participation from In-Q-Tel, Cisco Investments, and Capital One Ventures — a strategic investor roster spanning intelligence-community, networking, and financial-services interests that is independently verifiable and signals cross-sector concern about generative-AI-enabled fraud and disinformation. In 2026, GetReal was named a “Market Shaper” in Gartner’s Emerging Market Quadrant for deepfake-detection and startup vendors, an independent analyst-firm recognition rather than a vendor-authored claim.

GetReal’s differentiator is combining academic-grade forensic detection research with a continuous identity-verification product, rather than offering a single-purpose deepfake scanner, positioning it to address both media-authenticity and identity-fraud use cases as generative AI content becomes harder to distinguish from real media.

Innovation Matrix Assessment

Innovation Velocity 7/10

Since incubation in 2023-2024, GetReal has moved from research-driven detection technology to a funded commercial platform and independent analyst recognition within about two years.

Operational Value 7/10

Multimodal deepfake detection and continuous identity verification give security and fraud teams tools to address a threat category that most existing security stacks are not built to catch.

Market Momentum 7/10

Strategic investment from In-Q-Tel, Cisco Investments, and Capital One Ventures spans intelligence, networking, and financial services, an independently verifiable and unusually broad set of strategic backers for a company this young.

Category Disruption 7/10

Deepfake/synthetic-media detection is a genuinely emerging threat category driven by generative AI, and GetReal's multimodal approach is a meaningful architectural choice, though the field includes other credible competitors.

Real-World Efficacy 5/10

Gartner's Emerging Market Quadrant recognition is independent third-party validation, but no independently audited detection-accuracy benchmark or named customer deployment outcome was found in this review.

Enduring Relevance 9/10

As generative AI makes synthetic media and identity fraud increasingly convincing, detection and verification technology in this category will become more, not less, important over the next several years.

Why CISOs Should Care

As deepfake-enabled fraud (CEO voice cloning, synthetic video, fraudulent identity verification) grows, CISOs and fraud teams can use GetReal to add a dedicated detection layer that general-purpose security tools don't provide.

What Makes It Different

GetReal combines forensic-research-grade multimodal detection (audio, video, image analyzed together) with continuous identity verification, rather than shipping a narrow, single-modality deepfake scanner.

The Matrix Verdict

70/100 — MEANINGFUL INNOVATOR

GetReal Security rates as a Meaningful Innovator: an academically grounded, well-backed player addressing a fast-growing, genuinely novel threat category, with credible independent analyst recognition though still-limited public efficacy evidence.

Editorial Note: Claims vs. Verified Findings

Strategic investor participation and Gartner's Market Shaper recognition are independently verifiable; specific detection-accuracy percentages and customer-outcome claims were not found independently corroborated beyond the company's own materials.

Sources