What are the most common cyber risk modeling techniques and tools?

FAIR (Factor Analysis of Information Risk) is a widely used framework that quantifies risk using factors such as threat events, asset value, and vulnerability. It provides a structured approach to evaluate and prioritize risks based on potential impact and likelihood.

FAIR(tm) is a standard describing a prescribed series of calculations usually executed in Monte Carlo simulations. The primary focus is financial impact. This item is a repeat of item 5 below, and so should be consolidated into one. I disagree with the statement "can help you simplify and summarize your cyber risk profile". The output of a FAIR(tm) analysis may be the input into a process for simplifying and correlating dependencies of different risk factors but that is not a core function of the FAIR(tm) standard itself.

These graphical models can display potential attack paths against a system or asset. By analyzing attack trees, organizations can identify critical vulnerabilities and design effective security measures.

Probabilistic methods such as Monte Carlo simulations or conditional probability formulas can be incorporated into an attack tree model to change it from a qualitative exercise into a quantitative one.

Monte Carlo models can vary in complexity, from simple factor based models, to complex attack-tree based methods. They can be used to build a large collection of potential scenarios, giving rich detail about the sort of scenarios which are likely to cause significant impacts to the company. The extra detail in these models can be useful in targeting specific risk controls and measuring risk appetite.

A probability distribution graph is a common output of a Monte Carlo simulation. It shows the distribution of the probabilities calculated over the range of the simulation. The probability distribution shows where probabilities skew to the right or left and where the bulk of probability falls within the simulation range. It is a powerful way to communicate "what the risk looks like."

These probabilistic models help assess complex, interdependent risks by incorporating expert knowledge and data-driven insights. Bayesian networks enable organizations to evaluate potential threats and vulnerabilities, considering uncertainty and multiple variables.

Like attack trees, Bayesian networks are graphic models identifying dependencies and correlations. The model is a collection of conditional probabilities that allow us to ask the question, "What's the likelihood of A if B does (or does not) occur?" The inference occurs when we take the first iteration's output (posterior belief) and use it as the input for the next iteration (new prior belief). This allows us to refine our estimates as new data is available.

The FAIR framework offers a structured, quantifiable method for modeling cyber risk. It breaks down risk into two key components: risk scenarios and risk factors. Risk scenarios describe specific cyber events, like data breaches or ransomware attacks, while risk factors analyse the frequency and impact of these events. FAIR allows us to model both quantitatively and qualitatively, offering clear metrics for comparing cyber risks. By using FAIR, we can prioritise mitigation efforts, focus on the most significant threats, and effectively communicate findings with stakeholders, making it a practical tool for cyber risk decision-making.

Excel tools such as @risk are useful only for simple Monte Carlo models and can struggle to scale. Modelling tools such as Kovrr provide the detail of attack-tree modelling with a Monte Carlo scale analysis. In many of these cases it is important to consider the effort needed in calibrating a cyber model can be considerable. Think through the frequency of calibration, data sources and data modelling requirements.

CyberRiskModels.com has partnered with Boise State University's Cyber Risk and Resilience Program (CORe) to develop its Cybersecurity Risk Quantification course, which includes a complete set of tools and models. Both the course and toolset will be released as an Open Education Resource in 2024. It includes a fully functional FAIR(tm) model, various Monte Carlo simulations, Bayesian and Bayesian Inference, a Risk Tree, a Conditional Probability Calculator, and a Cyber Attack Scenario Model. Built by a cyber professional for cyber professionals, it provides everything you need to ask and answer your most common risk-related questions.

O Risk-Based Approach (RBA) na gestão de riscos cibernéticos foca na identificação e mitigação de ameaças a partir dos principais ativos e processos críticos. Ao priorizar riscos com base em sua probabilidade e impacto, as organizações podem alocar recursos de forma eficiente, garantindo que áreas essenciais estejam protegidas e que a resiliência operacional seja mantida. Essa abordagem dinâmica permite uma resposta ágil às mudanças no ambiente digital, otimizando a segurança cibernética.

It's important to analyze how cyber risk modeling strategies fit a particular organization. Sometimes, we need focused solutions that address a particular vulnerability. With Generative AI, I think the best tool is Microsoft CoPilot for Security. Located in MS Azure environment, it can be used by IT security professionals for a variety of scans/tests including data protection, logical access review, cloud security, incident reporting, etc.