Risk Quantification & Beyond: Putting Risk in Business Context for Better Decisions

In this upcoming webinar, Michael Rasmussen of GRC 20/20 Research and Vince Dasta of Complyance explore how data and AI can transform risk quantification into continuous, actionable decision intelligence.

Date: September 29, 2026

Time: 11:00 – 12:00 CT

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Learning objectives

Quantifying risk in a way decision-makers can actually act on

A working session on risk quantification: the techniques and where they hold up, how the practice is changing as data and AI take on more of the estimating, and how to put a quantified risk in front of the people who have to decide what to do about it.

Hosted by:

Michael Rasmussen

GRC 20/20

Vince Dasta

Complyance

What to expect:

In this webinar, Michael Rasmussen of GRC 20/20 Research and Vince Dasta of Complyance will explore how organizations can strengthen risk prioritization by combining risk quantification with the broader business context needed for informed decisions. The discussion will examine where quantification delivers value, where numbers can mislead when viewed in isolation, and how organizations can connect quantitative analysis with objectives, performance, controls, obligations, dependencies, and other relevant risk intelligence.

The goal is not to move away from risk quantification, but to make it more valuable by transforming quantified risk into decision intelligence that helps organizations determine which risks to take, which to avoid, which to mitigate, which to transfer, and where to focus limited resources.

Participants will learn how to:

Apply risk quantification effectively using financial impact, probability, ranges, scenarios, and other quantitative techniques to improve the rigor and consistency of risk analysis.
Understand the strengths and limitations of quantification and recognize why a risk number, model, or score should inform a decision rather than become the decision itself.
Understand how the practice has evolved, from calibrated estimates, expert elicitation, and FAIR-style scenario workshops toward data- and AI-assisted approaches, and what each method is still best suited for.
Evaluate where AI and operational data can supplement or replace manual estimation, what that requires in terms of data quality, model transparency, and defensibility, and where human judgment remains essential.
Connect quantified risk to business objectives and organizational context so that prioritization reflects what is strategically, operationally, and financially important to the enterprise.
Bring multiple dimensions of risk intelligence together, including quantitative exposure, likelihood, impact, velocity, performance, controls, obligations, dependencies, resilience, and changing business conditions.
Move beyond static risk scores and heat maps toward dynamic, contextual risk analysis that supports more informed and defensible decisions.
Improve risk prioritization and resource allocation by identifying which uncertainties have the greatest potential to affect critical objectives, operations, commitments, and opportunities.
Enable better risk ownership and executive decision-making by delivering quantified risk intelligence in the business context decision-makers need to determine the appropriate response.

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