Page 154 - Cyber Defense eMagazine August 2024

To address the glaring gaps in publicly available data and thus account for so much uncertainty, AI-data-
driven CRQM platforms use probabilistic models such as Bayesian networks, and probabilistic graphical
models. All these approaches can be applied with AI to explicitly represent uncertainty, as they assign
probabilities to different outcomes, which helps the AI system make informed decisions based on
uncertain data.

You Can’t Quantify What You Don’t Understand

The volume of information required to monitor the interdependencies between cyber physical systems,
networks, and the cloud has become too enormous to be processed by mere human intelligence. AI-
powered systems can be used to logically identify and automate the processing of data from
interconnected systems and analyze the data to deliver continuous outputs that are always up-to-date.

To underwrite a risk, one first needs to understand it. That is why risk data is the lifeblood of the insurance
industry. But in many cases, the datasets for operational technology remain incomplete. Or there might
be duplicate sources of data from different inputs. Having a precise process to reconcile and normalize
all that ingested information requires the creation of a data ontology for cybersecurity.

When AI is fed with enough dependable data about cyber risk, it can bring unprecedented accuracy and
speed to help understand risk. The underlying concerns include vulnerability detection, prioritization of
security tasks, and the cascading impact of cyber incidents on a network of interconnected critical
infrastructure.

By enabling a better assessment and quantification of cyber risk, especially for OT environments and
cyber-physical systems, AI also enhances risk transfer practices. On one end, companies get a more
thorough understanding of their cyber risk to decide what risk to accept, avoid, transfer, or mitigate. On
the other end, underwriters get more evidence-based data to align their cyber insurance parameters,
including their policy coverage and limits.

Taking Advantage of AI in the Cloud

AI can help us quantify cyber risk and define the best risk mitigation strategies. Cloud-based CRQM
platforms use AI algorithms to normalize and categorize ingested data from dozens of sources, including
internal data and raw signals from cybersecurity solutions for intrusion detection and vulnerability
management. In addition, natural language processing (NLP) is applied to analyze text and process cyber
incident information about victims and threat actors.

To show the scope of computing efforts this represents, CRQM platforms regularly perform millions of
Monte Carlo simulations on monitored sites to model the probability of different outcomes for a range of
processes that cannot be easily predicted. These simulations run what-if analyses on suggested
mitigation projects to identify the ones with the greatest positive impact on risk reduction. Machine
learning is also employed to model complex dependencies in the aggregation of risk based on impact
and frequency.

Cyber Defense eMagazine – August 2024 Edition 154
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