Calculator
Formula used
The calculator builds three components—Exposure, Controls, and a Threat multiplier. Inputs are normalized to 0–1, combined using weights, then transformed to a 0–100 score with a logistic curve.
ControlStrength = Σ (c_j · control_j)
ControlIndex = 0.20 + 0.80·ControlStrength
Threat = clamp(1 + 0.35·A + 0.12·R + 0.18·H, 1, 2)
RawRisk = ((0.60·Impact + 0.40·Likelihood)·Threat) / ControlIndex
Score = 100 / (1 + e^{-k(RawRisk - m)})
- Low: Controls match exposure; monitor and review periodically.
- Medium: Noticeable gaps; prioritize high-leverage control upgrades.
- High: Misuse could occur and matter; reduce standing privilege quickly.
- Critical: Immediate action needed; tighten access and investigate signals.
How to use this calculator
- Choose privilege level, scope, sensitivity, and frequency.
- Add flags like shared accounts or third-party access.
- Rate controls: sign-in, monitoring, reviews, SoD, and JIT.
- Click Calculate Risk to generate score and guidance.
- Export CSV or PDF for audits, reviews, and tracking.
- Re-score after changes in role, tools, or incidents.
Example data table
| Scenario | Privilege | Sensitivity | Frequency | Controls | Expected level |
|---|---|---|---|---|---|
| Helpdesk user resets passwords, strong logging. | Moderate | Internal | Daily | MFA + monitoring + reviews | Medium |
| Admin with shared account, weak monitoring. | Admin | Restricted | Weekly | Limited controls | High |
| Contractor with broad access and anomalies. | Elevated | Confidential | Weekly | Partial controls | High |
| Automated job with JIT and session recording. | Elevated | Confidential | Continuous | Strong controls | Medium |
Exposure drivers in access misuse
Privilege, scope, and data sensitivity set the baseline impact surface. When a role can change configurations, read regulated records, or touch many systems, the potential blast radius grows. The calculator converts these attributes to normalized factors and combines them into an exposure index that helps compare roles consistently across teams. Systems touched are log-normalized up to 500, and frequency ranges from rare to continuous automation.
Control strength and detection readiness
Controls reduce likelihood by making abuse harder and easier to spot. Strong sign-in protection lowers account takeover risk, while monitoring coverage improves time-to-detect. Regular access reviews reduce stale entitlements, and separation of duties prevents one identity from completing sensitive end-to-end actions. Monitoring and SoD are rated on a 0–5 scale; reviews map from none to monthly. Optional PAM, session recording, and just-in-time elevation further reduce standing privilege and improve investigations.
Signals, changes, and threat multipliers
Risk is rarely static. A spike in anomalous signals—unusual locations, off-hours activity, or abnormal data queries—raises the threat multiplier. Recent role changes can create temporary permission drift, and prior policy incidents may indicate repeated control bypass attempts. The multiplier is capped to prevent overreaction while still highlighting urgent cases for investigation. Treat 0–3 signals as low noise, 4–6 as elevated, and 7–10 as priority review.
Interpreting scores for governance decisions
Use the overall score to prioritize remediation, not to label people. Medium scores often respond well to better logging, tighter reviews, and fewer shared accounts. High or critical scores usually require reducing privileged scope, introducing approvals, or adding time-bound elevation. Pair results with access evidence, ticket references, and control owners. Exported reports help document decisions for audits, change control, and security steering committees.
Operational tracking and continuous improvement
Recalculate after onboarding, tool changes, incident lessons learned, or policy updates. Track the exposure index and control strength alongside the final score to see what moved the needle. If custom weights are enabled, align them to your threat model and revisit quarterly. Aim for measurable reductions, such as a 10–20 point score drop after removing shared accounts and enabling stronger sign-in. Over time, declining scores demonstrate measurable progress in least-privilege maturity overall.
FAQs
1) What does the score represent?
It estimates the probability and impact of access misuse using your inputs, producing a 0–100 score. It supports prioritization and governance decisions, but it does not prove wrongdoing or guarantee safety.
2) How should I rate anomalous signals?
Count meaningful indicators from logs and alerts, such as impossible travel, unusual data pulls, or repeated denied actions. Use 0–3 for background noise, 4–6 for suspicious patterns, and 7–10 for urgent review.
3) Why do shared accounts raise risk so much?
Shared credentials reduce accountability, weaken investigation timelines, and often bypass least-privilege design. Moving to named identities with break-glass accounts and approvals typically lowers risk quickly and improves response quality.
4) When should I enable custom weights?
Use them when your environment has unusual drivers, like highly regulated data, heavy automation, or strict SoD requirements. Keep weights stable for trend reporting, then revisit quarterly when threats or controls materially change.
5) What actions usually reduce the score fastest?
Remove shared accounts, reduce privileged scope, add just-in-time elevation, and improve monitoring for privileged actions. Strengthening sign-in protection and increasing access review cadence often produce immediate, measurable decreases.
6) How do CSV and PDF exports work?
After you calculate, the tool saves the latest result in the session. The export buttons generate a one-row CSV and a summary PDF report using that saved result for easy sharing and documentation.