Polygraf AI closes $9.5M Seed Round led by Allegis Capital
Abstract
This article explores the profound impact of artificial intelligence (AI) on data mapping workflows within the context of personally identifiable information (PII) discovery and classification. Through a comparative analysis of the current market leaders Polygraf AI, Microsoft Azure, Amazon Comprehend, and Google Cloud Platform (GCP), we demonstrate the improvements in accuracy, contextual comprehension, and domain-specific entity recognition that AI has brought to the field of data governance. Through real-case studies and performance benchmarks, we conclude that Polygraf AI’s method provides higher accuracy for detecting complex PII types, especially in technical contexts with device IDs, masked strings, and global identification formats. This research illuminates how AI is transforming data compliance practices today and provides fact-based guidance to organizations ready to move their data security practices forward.
Enterprise AI is moving from experimentation to accountability. As organizations scale AI in production, the focus is shifting from raw capability to efficiency, cost control, and operational sustainability. Energy usage,
Enterprise AI has entered a more pragmatic phase. CFOs are questioning initiatives they can’t cost-control or risk-model, while CISOs are blocking LLM deployments that require sensitive data to leave the
With the new $9.5M Seed round led by Allegis Capital, Polygraf AI is building the next generation of enterprise AI security—powered by its proprietary Small Language Models—helping organizations identify AI-driven
At Polygraf, we envision a future where AI augments human capabilities without compromising safety, privacy, or ethical standards. Trust in our commitment to building this future with you.
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