Varonis Systems, Inc (VRNS) Fair Value & Analysis
Technology · US · Market cap $4.2B
Analysis
Varonis Systems, Inc (VRNS) currently trades at $35.03, while our model-based Fair Value estimate is $15.64 — implying the stock looks roughly 55.4% overvalued today. We read business quality at 95/100 (high quality), in the Technology sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: medium).
About the company
Varonis Systems, Inc. provides software products and services that continuously discover and classify critical data, remediate exposures, and detect advanced threats with AI-powered technology in North America, Europe, APAC, and rest of worlds. The company offers Varonis Data Security Platform, a Software-as-a-Service solution which includes Data security posture management, Data access intelligence, Data discovery & classification, Discovery policy library, least privilege automation, Data activity monitoring, Data detection and response, and User & entity behavior analytics. It also provides Protection Packages, such as Microsoft 365, Windows & NAS, Hybrid, and Cloud Environments, as well as Database and Email Security Capabilities and On-Premises Subscription Products. It serves the financial services, public, healthcare, industrial, insurance, energy and utilities, technology, construction and engineering, education, and consumer and retail industries. Varonis Systems, Inc. was …
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.