Disclosure Risk Estimator Calculator

Use these example values to test how controls, recipient scope, and redaction quality change the score.

The calculator converts each input into a normalized factor risk from 0 to 100, then applies weighted scoring. Higher values indicate higher disclosure exposure.

• Inverse controls: NDA coverage, redaction quality, access controls, and review cycles reduce risk when values increase.
• Channel and encryption: Safer sharing methods and stronger encryption lower their component risk values.
• Log scaling: Recipient count and retention period use logarithmic scaling to avoid over-penalizing large operational ranges.
• Modifiers: Conditional points adjust results for risky combinations, such as public links without encryption.

Tip: Customize the weights in the PHP array if your legal or security team uses a different policy framework.

1. Enter the document or contract name to identify the assessment.
2. Set sensitivity, personal data density, and recipient volume.
3. Specify how many recipients are external to your organization.
4. Record current controls, including NDA coverage, encryption, logging, and access maturity.
5. Choose the planned sharing channel and expected retention period.
6. Add urgency, prior incidents, and review cycles for context.
7. Click Estimate Disclosure Risk to display the result card above the form.
8. Use the CSV or PDF buttons to save the assessment for records.

Operational Value of Structured Disclosure Scoring

Disclosure risk estimation gives legal teams a repeatable way to compare documents before release. Instead of relying on intuition alone, this calculator quantifies exposure by scoring sensitivity, recipient volume, control strength, and distribution methods. A weighted score helps contracts staff prioritize files needing stronger safeguards. The result is faster triage, clearer escalation, and better records when reviewers must justify why a disclosure decision was approved across departments consistently.

Core Risk Drivers in Contract Release Decisions

The strongest driver is usually content sensitivity. A highly restricted draft with personal data, pricing strategy, or unresolved claims language carries greater risk than a public summary. Recipient count also matters because distribution scale increases error probability. The calculator uses logarithmic scaling for recipients and retention days, reflecting operational reality. Moving from two recipients to twenty usually changes risk more than moving from two hundred to two hundred twenty in practice for teams safely.

Control Strength and Evidence Quality

Controls reduce risk when they are measurable and consistently applied. NDA coverage, encryption quality, access control maturity, and audit logging are treated as inverse risk factors. Higher control values lower the score because they reduce unauthorized access, accidental forwarding, and weak accountability. Redaction quality is especially important for contract exhibits and annexes. Small redaction errors can expose identities, rates, or clauses that trigger disputes and regulatory review during negotiations under deadlines internally.

Why Conditional Modifiers Improve Realism

Scenario modifiers capture combinations that often cause incidents. External sharing with weak NDA coverage raises risk because legal recourse becomes uncertain. Public links without encryption add severe exposure because access can spread beyond intended recipients. The calculator also rewards stronger governance practices, such as multiple review cycles and full logging, because layered review catches missing redactions, wrong versions, and approval gaps before release decisions become permanent and costly consistently materially.

Governance Use Cases and Continuous Improvement

Teams can use this output in approval workflows, vendor diligence, board reporting, and post incident reviews. A practical process is setting threshold bands: low scores proceed normally, moderate scores require legal approval, high scores require legal and security review, and critical scores require executive exception. Saving CSV or PDF reports builds a defensible record of assumptions, scores, and mitigations. Over time, organizations should tune weights using incidents, policy changes, and periodic review metrics each quarter regularly.

1. What does the disclosure risk score represent?

It represents a normalized estimate of disclosure exposure on a 0 to 100 scale using weighted content, control, recipient, and channel factors. Higher scores indicate greater handling risk before document release.

2. Can this calculator replace legal review?

No. It supports legal review by standardizing input factors and documenting assumptions. Final release decisions still require legal judgment, policy checks, and, when necessary, security or executive approval.

3. Why do higher control values lower the score?

Controls such as NDA coverage, encryption, redaction quality, and audit logging reduce unauthorized access and accountability gaps. The calculator treats them as inverse risk variables to reflect their protective effect.

4. Why are recipient count and retention days log scaled?

Log scaling prevents very large operational values from dominating the score unfairly. It captures rising exposure while keeping the model practical across small, medium, and enterprise distribution volumes.

5. When should I use scenario notes?

Use notes for context that affects judgment but is not directly scored, such as pending approvals, special confidentiality clauses, court deadlines, or temporary access restrictions for outside counsel.

6. How often should teams review the scoring weights?

Review weights at least quarterly or after any incident, policy change, or audit finding. Updating weights keeps the estimator aligned with real exposure patterns and current governance priorities.