Cyber Risk: Executive Summary for the CRO/COO
Executive Overview
Cyber risk continues to impose significant operational, financial, regulatory, and reputational consequences across industries. However, the materials reviewed here demonstrate that while structured quantification frameworks, particularly FAIR, are widely promoted, most organizations face severe practical constraints when attempting to implement quantitative cyber‑risk measurement at scale. The research shows that organizations frequently believe they are operating quantitatively, but in reality, rely on qualitative assessment blended with selective numerical outputs, a dynamic described as a “quantitative veneer” (Slapničar et al., 2025).
This gap between claimed quantification and actual measurement capability is one of the largest risks in cyber‑risk governance today. As a result, CROs and COOs must adopt a more hybrid, adaptive, and evidence‑anchored approach to risk quantification, one that is robust to data scarcity, fast‑moving threat landscapes, and organizational realities.
Key Findings from the Research
Quantification frameworks provide structure, but also impose significant complexity
The FAIR taxonomy provides clear scenario‑definition components (threat, asset, method, effect), but it also highlights practitioner‑level dilemmas: too much precision becomes impractical, while too little undermines analytical value. FAIR explicitly warns that determining “how much precision is enough” depends heavily on organizational maturity, and that overly precise scenarios may be counterproductive (FAIR Institute, 2025).
Implication for executives:
A strict FAIR implementation may exceed organizational capability, especially in environments with rapidly evolving threats.
Organizations consistently overestimate their quantitative maturity
Empirical interviews across multiple multibillion‑dollar enterprises confirm that while organizations claim to use risk‑based (quantitative) methods, they “measure cyber risk qualitatively with a ‘quantitative veneer’” (Slapničar et al., 2025).
This results in:
Misalignment between risk measurement and risk decision‑making
A false sense of analytical confidence
Underestimation of systemic or emerging risks
Difficulty prioritizing cyber investments relative to other enterprise risks
Implication for executives:
A governance model relying solely on FAIR‑style quantitative outputs may be unreliable if the underlying measurements are not truly quantitative.
Scenario‑based models must be adaptable, not static
The FAIR taxonomy requires careful scenario scoping, but the document acknowledges that scenario precision varies with organizational maturity and can strain resources (FAIR Institute, 2025).
In a rapidly evolving threat landscape, static scenario catalogs degrade quickly.
Implication for executives:
Scenario frameworks must be continuously refreshed, integrating operational intelligence and business‑process changes.
Hybrid “qualculation” is emerging as the realistic standard
Slapničar et al. (2025) propose that organizations inherently blend qualitative and quantitative reasoning, a process termed “qualculation,” and that this blended approach is the highest feasible standard for modern cyber‑risk management given current data and organizational constraints.
Implication for executives:
Rather than pursuing perfect quantification, organizations should intentionally design for structured hybrid analysis.
Recommendations for the CRO/COO
Based on the FAIR framework and the research findings, the recommended approach shifts away from “FAIR as the primary method” toward a multi‑method, maturity‑aligned, and scenario‑driven strategy.
Recommendation 1: Adopt a Hybrid Quantification Model (“Qualculation‑Driven”)
Use FAIR‑inspired structure for scenario clarity, but do not rely on FAIR‑style quantitative precision where the organization lacks data or maturity.
This is explicitly aligned with Slapničar et al.’s empirical findings that qualculation is more realistic than strict quantification (2025).
Recommendation 2: Build a Dynamic Scenario Program Rather than a Fixed FAIR Library
Leverage FAIR taxonomy structures selectively, for defining threats, assets, and effects, but allow lower‑precision scoping in fast‑changing environments.
FAIR’s own materials warn against excessive scenario precision and emphasize alignment with organizational maturity (FAIR Institute, 2025).
Recommendation 3: Focus Governance on Transparency Over Numerical Precision
Executives should demand clarity on:
What assumptions feed numerical outputs
Where estimates are qualitative vs. quantitative
How scenario definitions evolve over time
This directly responds to the “quantitative veneer” problem identified by Slapničar et al. (2025).
Recommendation 4: Prioritize Capability Building Before Pursuing Deep Quantitative Models
Organizations should first develop:
Asset inventories
Incident reporting discipline
Control‑effectiveness evidence
Internal loss data
Only then should more rigorous FAIR‑style quantification be pursued, consistent with the maturity‑based guidance in the FAIR taxonomy (FAIR Institute, 2025).
Recommendation 5: Use Quantitative Outputs as Decision Inputs, Not Decision Drivers
Executives should treat model outputs as scenario indicators, not precise economic forecasts, consistent with the research showing limitations in measurement accuracy (Slapničar et al., 2025).
Strategic Implications
Shift from precision to adaptability.
Focus on transparency, not numerical sophistication.
Adopt iterative, scenario‑based assessments instead of static FAIR implementations.
The result is a more resilient, credible, and operationally aligned cyber‑risk management program, capable of navigating the realities identified in your research corpus.
Sources:
Carannante, M., & Mazzoccoli, A. (2025). An analytical review of cyber risk management by insurance companies: A mathematical perspective. Risks, 13(144).
FAIR Institute. (2025). FAIR Institute Cyber Risk Scenario Taxonomy (February 2025).
Franco, M. F., Mullick, A. R., & Jha, S. (2024). QBER: Quantifying cyber risks for strategic decisions.
Kure, H. I., Islam, S., Ghazanfar, M., Raza, A., & Pasha, M. (2021). Asset criticality and risk prediction for an effective cyber security risk management of cyber physical system.
Lopez, O., Denuit, M., Ghossoub, M., Trufin, J., Kher, J., Maillart, A., Raes, E., Rapior, H., & Skoubani, M. A. (2025). Cyber risk quantification, stress scenarios, mitigation, and insurance.
Pal, R., Duan, K., & Sequeira, R. (2025). A theory to estimate, bound, and manage systemic cyber-risk.
Rabitti, G., Khorrami Chokami, A., Coyle, P., & Cohen, R. D. (2025). A taxonomy of cyber risk taxonomies.
Slapničar, S., Axelsen, M., & Eulerich, M. (2025). Cyber risk management: An illusion of a risk-based approach.