This paper explains how organizations can quantify financial risk tied to sensitive data using discovery, sampling, and advanced risk-modeling techniques. Page 1 stresses the importance of knowing where data resides and its potential breach impact. Charts show cost-per-record estimates and IBM breach-cost breakdowns. The paper contrasts linear versus log-normal modeling, showing why traditional averages underestimate real-world variability. It cites Cyentia research on breach probability by industry and company size. Guidance includes prioritizing high-risk data, reducing legacy exposure, improving visibility, and aligning remediation with financial risk reduction goals.

Join for free to read