Adjusting Cloud Risk Scores

The Forcepoint ONE SSE cloud risk score is assessed by a weighted formula calculating all of the attributes that the app has/doesn't have along with Forcepoint ONE SSE's automated trust rating.

These weights can be adjusted at any time by an admin on the Cloud Score Weights page. You can access this page by clicking it from the navigation tree under Discovery or from clicking the link displayed under the question mark next to the applications risk score.

For each attribute you can adjust the weight to 1 or more. The higher the number the more that particular attribute will be weighted in the cloud risk score. Meaning if you weight SOC2 compliance higher than other attributes, then it means applications that possess SOC2 compliance will have a higher (better) cloud risk score than applications that don't have SOC2. You can also adjust the slider for how much Forcepoint ONE SSE's automated Trust Rating factors into the overall weighted formula for the Cloud Risk Score.



Forcepoint ONE SSE assesses the following Attributes across 4 categories:

Compliance Certifications

  1. Cobit
  2. CSA STAR
  3. FedRAMP
  4. FINRA
  5. FIPS 140-2
  6. FISMA
  7. GAAP
  8. HIPAA
  9. HITRUST
  10. ISAE3402
  11. ISO 27001
  12. ITAR
  13. ITIL
  14. PCI DSS 3.2
  15. Privacy Shield
  16. SOC 1
  17. SOC 2
  18. SOC 3
  19. SOX
  20. SP 800-53
  21. TRUSTe

Security

  1. Admin Action Logging
  2. Data Sharing: File Sharing
  3. Data Sharing: Granular Access Controls
  4. DLP Integration
  5. Heartbleed Patched
  6. MFA
  7. Password Policy
  8. Penetration Testing
  9. SAML SSO
  10. SLA Present
  11. Trusted Certificate
  12. User Audit Trail
  13. User can upload data

Encryption

  1. Strong (AES 256)
  2. Data Segregation by Tenant
  3. TLS 1.2
  4. Tenant Managed Keys

Legal

  1. Data Ownership
  2. Data Retention on Termination
  3. DMCA
  4. GDPR: Data Protection
  5. GDPR: Report data breaches
  6. GDPR: Right to erasure
  7. GDPR: User Ownership

Trust Rating

Forcepoint ONE SSE's trust rating is assessed automatically via machine learning taking into account factors such as public signals, ownership information, usage information, etc compared across other application within their categories.