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Risk Score: Security Signals

Overview

This factor evaluates how exposed a target is to real-world phishing and account takeover risk. It incorporates breach/leak signals, public social signals that encourage targeting, and security posture/alert signals from integrated tools (endpoint protection, MDM, SIEM, EDR, etc.). 

Click from the following to jump to your desired section:

  1. Dark Web Exposure (Leak Detection)
  2. Social Media Presence
  3. Security Tooling Integrations
  4. KillPhish
  5. Security Signals Scoring Example

 

Dark Web Exposure (Leak Detection)

The following is the Finding and effect of PII exposure:

Finding Effect
PII Exposure (e.g., SSN, phone number, address) +10 to +60, based on severity

For more information concerning dark web exposure, see our article on Dark Web Monitoring

 

Social Media Presence

The following contains various Social Media Presence Findings, and their respective effects:

Finding Effect
Job Title Exposure (job/role is clearly discoverable) +10
Work email posted publicly +10
High social activity linked to work content +10 to 25
Phishing-enable signals (e.g., "Just got a new laptop" or "on vacation" +10 per signal

 

Security Tooling Integrations

The following contains various Security Tooling Integration findings, and their respective effects:

Finding Effect
Threat Detected +5 to +20 based on severity
Suspicious Sign-in Signals +5 to +20 based on severity
No endpoint protection installed +15
OS not patched / critical CVEs present +10
Unsupported OS version +10
Unencrypted drive +10
No MDM agent detected +5 to +10

MFA disabled / not enforced; 

Shadow IT software installed

+10 each

Multiple failed login attempts; 

Public Wi-Fi detected repeatedly; 

Auto-login / saved passwords detected

+5 each

 

KillPhish

The following contains the Real Phishing Report finding and its respective effect:

Finding Effect
Real Phishing Report -10

 

Security Signals Scoring Example

Context for the following user:

  • User credentials found in dark web scan.
  • User title and email found on public LinkedIn profile.
  • Password reuse detected.
  • Social profile contains "on vacation" language. 

The following contains the above findings with their respective value, decay, and adjusted score:

Finding Value Decay Adjusted Score
Credentials found in dark web dump (150 days ago) +60 12.5% +52.5
Job title and email found on public LinkedIn profile +5   +5
Reported phishing email through KillPhish -10   -10
"On vacation" detected on social media +10   +10
Raw Security Signal Score     57.5

Security Signals Factor Total: (52.5 + 5 - 10 + 10) * .2 = 11.5

Note: It is possible to have a negative score for this factor if the target has accumulated a number of reported phishing emails through KillPhish. After weighting the score, it will range between -4% and 20%. 

 

Interpretation the Score

This user, with a score of 57.5, represents a moderate risk. 

Their credentials have appeared in a recent breach, and their public-facing presence makes them more easily identifiable and targetable. 

 

Breakdown of What the Score Reflects

Indicator Interpretation
Recent credential breach Indicates the user's login information is actively circulating in threat actor ecosystems, elevating risk of account takeover.
Public job title and email Makes the user easier to target with social engineering or spear phishing attacks, especially if they occupy a sensitive role. 
Phishing-enabling signals Can be weaponized in highly personalized phishing attacks. 
Reported real phishing Shows the target can detect when they are being phished. 

 

Recommended Actions

These are the following recommended actions for the above user:

  • Require immediate reset with unique credentials across all corporate systems. 
  • Ensure multi-factor authentication is enforced accross all logins.
  • Encourage the user to review and minimize job-related personal information that is shared publicly.
  • Deliver microlearning on password hygiene and credential management. 
  • Schedule exposure scans every 30 to 60 days. 

 

 

 

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