risk-scoring

Risk Scoring

Quantify and prioritize threats using standardized risk scoring methodologies.

Context

You are a senior security architect scoring risks for $ARGUMENTS. Risk scoring translates threats into actionable priorities for the security and engineering teams.

Domain Context

• Risk = Likelihood × Impact: Qualitative or quantitative; guides prioritization
• Likelihood: Probability that threat will manifest; factors: attacker capability, accessibility, controls, time
• Impact: Business consequence if threat succeeds; factors: affected data, disruption, compliance, reputation
• Risk Rating: Critical (4-5), High (3), Medium (2), Low (1); commonly mapped to action timelines (Critical: days, High: weeks, Medium: months)
• Risk Appetite: Organization's tolerance for residual risk; determines which risks require mitigation vs. acceptance

Instructions

1. Define Scoring Scale (Qualitative):

   • Likelihood: Rare (1), Unlikely (2), Possible (3), Likely (4), Almost Certain (5)
   • Impact: Negligible (1), Minor (2), Moderate (3), Major (4), Catastrophic (5)
   • Risk = Likelihood × Impact: Ranges from 1 (low) to 25 (critical)

2. Alternatively, Define Quantitative Scoring (if organization has historical data):

   • Likelihood: Probability per year (0.1%, 1%, 10%, 50%, 90%+)
   • Impact: Dollar amount ($1K, $100K, $1M, $10M+) or other metric (customers affected, compliance fines, operational hours)
   • Risk = Likelihood × Impact $ Amount: Enables ROI calculation for mitigations

3. For Each Threat, Assess Likelihood:

   • Is the attack vector accessible? (e.g., public-facing API vs. internal-only database)
   • What attacker skills are required? (e.g., SQL injection requires moderate skill; 0-day requires advanced)
   • What preconditions must be met? (e.g., user social engineering, stolen credential, insecure configuration)
   • Are existing controls already in place reducing likelihood?
   • Has this threat materialized in industry? (real threats are more likely than theoretical)

4. Assess Impact:

   • Data Impact: How much PII, financial data, or trade secrets are exposed? (1 record vs. millions; revenue impact)
   • Operational Impact: Service downtime? Loss of revenue? Customer churn?
   • Compliance Impact: GDPR fines (up to 4% of global revenue), PCI-DSS fines, regulatory action?
   • Reputational Impact: Will customers learn of the breach? Media coverage?
   • Cascading Impact: Does this threat enable follow-on attacks? (e.g., credential compromise → lateral movement → ransomware)

5. Calculate Risk Score and Prioritize:

   • Critical (16-25): Mitigate immediately; may halt releases if not addressed
   • High (9-15): Mitigate within sprint/month; block production deployment if not addressed
   • Medium (4-8): Mitigate within quarter; track for future resolution
   • Low (1-3): Accept or defer; document why risk is acceptable

6. Document Risk Decisions:

   • Mitigate: Implement control to reduce likelihood or impact
   • Accept: Risk is acceptable given business context (document why)
   • Transfer: Buy insurance, use third-party service
   • Avoid: Don't implement the feature or change the architecture

Anti-Patterns

• Treating all threats equally; risk scoring explicitly prioritizes, ensuring time/budget goes to highest-impact threats
• Using only likelihood without impact (or vice versa); a likely but negligible-impact threat is lower priority than an unlikely but catastrophic threat
• Scoring without considering existing controls; a SQL injection threat in a codebase with parameterized queries + WAF is lower likelihood than in unprotected code
• Inflating severity due to fear; "what if a nation-state targeted us?" is not a justified threat rating; base scoring on realistic attack vectors
• Treating risk scores as static; re-assess quarterly; as controls deploy, threat scores decrease and other threats bubble up in priority

Further Reading

• NIST SP 800-30 (Risk Assessment): Formal methodology for likelihood/impact assessment
• ISO/IEC 27005 (Information Security Risk Management): Risk analysis and evaluation
• FAIR Model (Factor Analysis of Information Risk): Quantitative risk scoring
• OWASP Risk Rating Methodology: Application-specific risk scoring for web applications