It’s not hard to build consensus that an organization is exposed to cyber risks. What tends to be difficult, however, is establishing the financial impact of those risks that contain many variables and unknowns . As a result, it can be nearly impossible to engage in meaningful dialogues and decision-making processes about cybersecurity investments and operational priorities based on estimations instead of real data. Cyber risk quantification (CRQ) offers a solution.
CRQ is an analytical process that attaches model based estimated costs to cyber risks. It enables stakeholders in cybersecurity and business management to discuss cyber risks by translating them to business impact and financial terms . They can work together using an agreed upon financial reference point for a given risk.
The simple answer is that cyber threats pose serious dangers to companies. A bad cyber attack can cost millions to remediate. It can disrupt operations and damage a company’s reputation. Businesses must defend themselves and prepare to respond to attacks.
The problem here is that there are many different types of cyber threats and other associated risks. A company could suffer a data breach, a phishing attack, a ransomware attack, a denial of service (DoS) attack and so forth. Each poses its own unique consequences. Each comes with its own potential financial impact. Some will be very high. Others, relatively low. To protect its digital assets effectively, a company must be able to set a priority of defense: which risks come first? Which systems and digital resources represent the greatest potential losses?
Cyber Risk Quantiaction allows for measuring the return on investment (ROI) of cybersecurity programs, justifying and prioritizing investments, evaluating the success of resource allocation, and optimizing insurance to build a databacked cybersecurity plan.Cyber risk quantification enables stakeholders to communicate cyber risk in financial terms. IT managers, security managers, business executives and the board can all discuss cyber risk using the same baseline.
With a stochastic modeling approach, based on threat intelligence, stakeholders can collaborate knowledgeably and confidently on optimizing cyber insurance and risk transfer placements, identify gaps between risk mitigation options and cyber insurance spending—maximizing the organization’s risk management decisions and strengthening business resilience.
A top down approach delivers a further benefit with its speed and repeatability. In contrast to risk consultants, who can render an analysis that quickly becomes obsolete, an automated solution can quickly re-assess risks and assign dollar values to them. This enables organizations to respond to risks on a timely basis, adjusting their approach as risk quantifications evolve over time. And, implementing this approach gets the organization out of the need to make a big investment in cyber risk data collection.
The process can also help an organization analyze the financial impact of third-party risk. This is an underappreciated area of cybersecurity. Some of the most serious threats come from partner firms. The costs associated with such risks are essential to calculate if an organization wants to maintain a strong security posture.
Benchmarking one’s cyber security against peer companies in the same industry is another reason why businesses are embracing CRQ. When risks are translated into dollars, it becomes relatively easy to compare one company with another, e.g., my biggest risk would cost me a million dollars to remediate, versus my peers, whose biggest risk is half that amount. A finding like this should prompt an organization to investigate the difference in value and find a way to narrow the gap through better controls, response processes and the like.
The process involves advanced modeling techniques that analyze cyber risk data as well as information about financial losses that have arisen due to cyberattacks. The data itself comes from a wide set of sources, including cyber insurance claims from comparable companies. The process then incorporates an analysis of these various data streams and evaluates them in the contexts of real world global cyber event frequencies, along with their financial impact and the company’s firmographic and technographic profiles. After processing hundreds of thousands of simulated cyber events, the model arrives at accurate risk quantification metrics. When done properly, the broken down data gives tremendous insight into systemic and targeted attacks and failures, and how they can specifically affect an organization from a financial perspective.
The process then incorporates an analysis of these various data streams and evaluates them in the contexts of real world global cyber event frequencies, along with their financial impact and the company’s firmographic and technographic profiles. After processing hundreds of thousands of simulated cyber events, the model arrives at accurate risk quantification metrics.
Kovrr’s Quantum is a solution for cyber risk quantification. It works by leveraging global threat intelligence and financial impact data from cyber incidents. It gives stakeholders the ability to drill down into cyber event examples. With this functionality, users can examine risk vectors associated with attacks that are common in their particular industry. Users can also enact simulated scenarios, tuned to their industries, to understand where their cyber security risks are concentrated. Quantum offers details of an attack scenario’s financial impact.
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