Cloud security posture, data discovery, and cyber risk quantification — in one platform.

Cyber risk quantification (CRQ) is the practice of translating cybersecurity findings — vulnerability scores, control gaps, exposure data — into financial measures like annual loss expectancy and value at risk. Instead of describing risk in severity stars or red/yellow/green status, CRQ outputs a dollar figure a board, CFO, or deal committee can act on. Theodolite uses the FAIR (Factor Analysis of Information Risk) methodology paired with Monte Carlo simulation to produce these estimates.

Cloud security posture management is the continuous monitoring and assessment of cloud infrastructure (AWS, Azure, GCP) for misconfigurations, compliance violations, and risk exposure. CSPM tools surface findings like over-permissive IAM roles, unencrypted storage buckets, and exposed services. Theodolite extends classic CSPM by translating findings into dollar-denominated risk so engineering work is prioritized by business impact, not CVSS score.

Data security posture management is the practice of finding sensitive data wherever it lives in cloud environments — across databases, object storage, containers, SaaS applications — and assessing how exposed it is. DSPM answers questions like "where is our PII?" and "is any of it exposed to the public internet?" Theodolite combines DSPM with CSPM and CRQ so data exposure findings carry a dollar value and remediation priority, not just a finding flag.

Most security tools cover one layer — CSPM scans infrastructure misconfigurations, DSPM finds sensitive data, CRQ platforms model loss expectancy. Theodolite unifies all three so findings from each layer compound: a data-discovery hit on PII in an exposed S3 bucket flows directly into the loss-expectancy model with a real impact value, and the resulting remediation work is prioritized by dollar risk reduction, not by tool-specific severity. Built by operators who lived the multi-tool problem first-hand.

Risk-based vulnerability management (RBVM) prioritizes vulnerability remediation by business impact rather than by raw CVSS score. A "critical" CVE on an isolated test server is less urgent than a "medium" CVE on an internet-facing system holding regulated data. Theodolite implements RBVM by feeding vulnerability scan output (Nessus, OpenVAS) into the same FAIR-based loss-expectancy model that drives the rest of the platform — so the remediation queue ranks every finding by dollar risk reduction per hour of work.