Risk Categorization
This risk matrix provides a framework for assessing the potential impact of different cyber incidents based on their severity and likelihood. Use this matrix to prioritize their response efforts and allocate resources accordingly.
Impact / Likelihood | High (H) | Medium (M) | Low (L) |
---|---|---|---|
High (H) | Critical | Major | Moderate |
Medium (M) | Major | Moderate | Minor |
Low (L) | Moderate | Minor | Insignificant |
Severity Levels:
- Critical: Severe and widespread impact, potentially causing irreversible damage to the organization.
- Major: Significant impact requiring immediate attention and substantial resources for recovery.
- Moderate: Noticeable impact but manageable, requiring a focused response to prevent escalation.
- Minor: Limited impact with the potential for resolution without significant disruption.
- Insignificant: Negligible impact, unlikely to cause any noticeable harm.
Example Assessments based on incident categorization
- Unauthorized Access to Information (H, M):
- High Impact: Critical when sensitive information is compromised.
- Medium Likelihood: Frequent attempts but not always successful.
- Compromise (H, H):
- High Impact: Major or critical if a successful compromise occurs.
- High Likelihood: Constant threat due to evolving tactics.
- Intrusion Attempts (M, H):
- Medium Impact: Moderate impact unless successful.
- High Likelihood: Frequent attempts due to the nature of automated attacks.
- Denial of Service (H, M):
- High Impact: Critical during successful attacks.
- Medium Likelihood: Potential due to the prevalence of DDoS tools.
- Fraud (M, H):
- Medium Impact: Significant financial and reputational consequences.
- High Likelihood: Frequent attempts, especially through phishing.
- Information Gathering (L, M):
- Low Impact: Limited direct harm, but potential for indirect risks.
- Medium Likelihood: Occasional attempts for reconnaissance.
- Abusive Content (M, L):
- Medium Impact: Significant reputational damage.
- Low Likelihood: Less frequent but impactful when it occurs.
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