According to a recent LinkedIn post from GetReal Security, the company is emphasizing a forensic-based approach to deepfake and content authentication. The post highlights remarks from VP of research Tina Nikoukhah, who focuses on tracing the “digital fingerprints” and mathematical operations that content undergoes as it moves between sources.
The post contrasts this method with so‑called black-box classifiers that label media as real or fake without explaining their reasoning. By positioning its technology around explainability and evidence-based analysis, GetReal Security appears to be targeting use cases such as image authentication, secure video calls, and high-stakes corporate meetings, where auditability and trust are critical.
For investors, this focus suggests a strategy aimed at differentiated, higher-value segments of the cybersecurity and AI-content integrity markets rather than commoditized detection tools. If successfully commercialized, such capabilities could support premium pricing, deeper enterprise adoption, and recurring revenue opportunities in sectors sensitive to fraud, compliance, and reputational risk.
The post also directs readers to a blog explaining the difference between black-box detection and forensic-based technology, indicating ongoing thought leadership efforts. This content strategy may help the company strengthen its brand in the emerging deepfake defense space, potentially improving its competitive position as regulations and corporate governance standards around synthetic media continue to evolve.