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Anand Paturi
Anand Paturi

The Financial Impact of Cyber Threats

Determining the financial impact of specific IT vulnerabilities is a good way to prioritize remediation and prevent attacks.

The WannaCry and NotPetya ransomware epidemics demonstrated how quickly malware can spread across the globe and cripple businesses. Their impact extended beyond traditional IT infrastructure into operational systems used to control industrial, manufacturing, and critical infrastructures. The scale of these incidents is forcing organizations to consider the financial impact and business exposure associated with cyber threats, to better mitigate risk.

As in most cases, these attacks were made possible by poor operational security procedures (or lack of them), because the vulnerabilities they exploited, and the patches that protected against them, had been disclosed months earlier.

What's lacking in many organizations is smart prioritization of vulnerability remediation efforts. One efficient way to accomplish this is by determining financial impacts posed by specific vulnerabilities that exist in the IT infrastructure.

Records-Based Exposure Models Are Inaccurate
Traditionally, financial impacts are estimated based on the number of personally identifiable information records that could be breached or exfiltrated during a cyber attack. This data is fed into business impact analysis systems or risk frameworks like FAIR to extract business exposure.

However, considering only data loss scenarios while determining financial effects does not provide a comprehensive assessment, because data breaches are not always involved in massive attacks. For example, the WannaCry and Petya ransomware outbreaks encrypted but did not exfiltrate data.

Therefore, when calculating the financial impact of cyber threats, you must consider the following factors: (a) revenue loss resulting from downtime, (b) staff time required for post-incident analysis, (c) infrastructure damage and the cost to implement compensation controls, and (d) post-attack notification and legal costs.

Threat-Centric Models Are More Reliable
To do this, organizations should assess their business exposure to individual cyber threats. This can be achieved through adversary modeling, using tactic, technique, and attack pattern frameworks.

CAPEC (Common Attack Pattern Enumeration and Classification) is an open attack pattern categorization framework that enables analysts to determine different attack patterns applicable to high-risk vulnerabilities. When a threat's attack patterns are mapped to assets on which the high-risk vulnerability is present, a comprehensive business exposure can be derived.

As an illustration, let's apply CAPEC to the EternalBlue exploit used in the WannaCry and NotPetya attacks to assess its financial impact on a generic organization.

The EternalBlue exploit starts with an integer overflow (CAPEC-92) that runs arbitrary code on target systems to cause a buffer overflow (CAPEC-100) in order to hijack a privilege thread in a system process. This ultimately leads to hijacking a privileged thread of execution (CAPEC-30). Though the likelihood of exploit for CAPEC-30 is low, its severity is critical and results in running unauthorized commands by gaining privileges or assuming identity.

Applying CAPEC analysis to EternalBlue reveals the tactics that adversaries can implement on target systems vulnerable to that exploit and their outcomes (i.e., gaining privilege and executing unauthorized commands).

Because the adversary modeling for EternalBlue is known, the operational impact of vulnerabilities (susceptible to EternalBlue) on the target organization is clear. With this knowledge, it's possible to estimate the business exposure resulting from those vulnerabilities.

For example, if vulnerabilities that are susceptible to EternalBlue exist on an organization's file server, then we know arbitrary commands can be executed to tamper with the files on that machine. Which, in the WannaCry and NotPetya attacks, involved encrypting the files to elicit a ransom payment.

Predicting Financial Losses
Next, based on the organization's business dependency on those files, we can use their unavailability as a factor to determine the financial loss that would result if they were compromised by a ransomware attack.

Traditionally, cybersecurity risk is calculated using this formula:

likelihood X impact = risk

The following modified version of this formula can be used to derive the financial effects based on a risk assessment:

likelihood X criticality X f (impact analysis) = financial impact

It can be applied against each vulnerability discovered during a risk assessment to estimate their individual financial impacts, which may be different.

Likelihood is a single point value derived as a probability measure based on cybersecurity intelligence or algorithms. This represents the likelihood of a given vulnerability will be successfully exploited. Some of the sources that can be used to derive likelihood include:

  • Threats applicable to the vulnerability
  • Patches available versus applied to the vulnerability
  • Tactic applicable after exploiting the vulnerability
  • Ease of exploitability

Criticality is a single point value that represents the criticality to the business of the asset on which the particular vulnerability exists.

And f (impact analysis) is a function that combines the monetary loss resulting from a successful cyberattack. The list of factors on which the monetary loss should be calculated is listed above. One of the leading criteria for determining monetary loss is the severity and impact of the tactic that is applicable to the vulnerability. For example, a remote command execution tactic would generate a high loss in terms of monetary value. 

By assessing the financial effects of individual cyber threats, organizations can more effectively prioritize remediation efforts, align security resources to protect their most critical assets, and allocate new investments to initiatives that will limit the business impact of attacks.

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Anand Paturi is Senior Research Scientist at RiskSense, a provider of cybersecurity risk management technology. He is an expert in Web application and database security, risk exposure and threat-centric vulnerability quantification, and enterprise risk analytics. Anand is ... View Full Bio
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