Negligent Insider Risk Calculator

Why negligent insider risk needs measurement

Negligent incidents often begin with routine work: forwarding a file, clicking a link, or misconfiguring a share. This calculator converts those daily conditions into a repeatable score, so security teams can compare groups over time. Inputs reflect common drivers—training coverage, policy awareness, phishing susceptibility, workload pressure, remote exposure, hygiene, MFA adoption, monitoring, near‑miss history, access breadth, data sensitivity, and privileged presence.

How the score supports prioritization

The overall score is a weighted average of normalized contributions, scaled to 0–100. Higher values indicate a higher probability that mistakes will occur and cause meaningful harm. Separating likelihood and impact helps you pick the right control: coaching and safe defaults for likelihood, and least privilege, classification, and PAM for impact. The “top factors” table highlights what most strongly raises the score in your scenario. To operationalize results, set a target reduction per quarter, then map actions to owners and dates. Recalculate after control changes to confirm improvement, not just new assumptions. And share outcomes with leadership.

Suggested data sources for inputs

Use evidence where possible. Training and policy awareness can come from LMS completion and short knowledge checks. Phishing susceptibility can be estimated from simulation click rates or reported messages. Workload pressure can be approximated by ticket volume per analyst, overtime, or queue age. Remote exposure can reflect percentage of remote days and device posture compliance. Near‑miss counts can be drawn from helpdesk, DLP alerts, lost‑device reports, or mis‑send tickets.

Interpreting levels and tracking trends

Use the level bands to standardize reporting: Low (0–24), Moderate (25–49), High (50–74), and Critical (75–100). Track scores monthly per team and annotate major changes, such as onboarding waves, new collaboration tools, or MFA enforcement. A falling likelihood score without a matching impact reduction suggests mistakes are decreasing, but access or sensitive data is still concentrated—prompting access reviews and segmentation.

Using weights responsibly

Weight sliders let you reflect local realities, but they should be tied to observations. If your environment shows repeated phishing‑led compromises, increase the phishing weight slightly and evaluate whether training and phishing‑resistant authentication reduce the score. Keep most weights near 1.0 to avoid bias. When presenting to auditors, export CSV or PDF to document assumptions, inputs, and the resulting recommendations for the assessed user population.

FAQs

1) What does “negligent insider” mean here?

It refers to unintentional actions that create exposure, such as mis-sending data, weak authentication choices, unsafe sharing, or clicking malicious links—without malicious intent.

2) How often should I run this calculator?

Run it monthly per team, and after major changes like new collaboration tools, MFA enforcement, remote policy shifts, mergers, or onboarding spikes.

3) What’s a good way to estimate near-misses?

Combine helpdesk mis-send tickets, lost-device reports, DLP alerts, and security coaching logs. Use a consistent time window so trends remain comparable.

4) Should weights be adjusted for every team?

Only when you have evidence that a factor behaves differently. Keep most weights near 1.0, document why you changed them, and validate changes by re-running after controls improve.

5) How do I lower risk quickly without slowing work?

Focus on safe defaults: enforced MFA, least privilege, streamlined sharing rules, device posture checks, and targeted micro-training for the highest contributors.

6) Can I use this for audit reporting?

Yes. Export CSV or PDF to capture inputs, assumptions, and outcomes. Pair results with remediation actions and dates to show governance and continuous improvement.