Here is a small sample of the great talks available to SIRA members. Become a paid SIRA member for only $50/year to show your support for the SIRA mission and get access to the full library of past SIRAcon and webinar recordings.
Does your organization use open source software? Do you understand the risks inherent in these dependencies and how they are being managed in your environment? After watching Equifax be compromised by an OSS vulnerability, how are you sleeping at night?
Presentation Slides: Down the open source rabbit hole.pptx
Many risk assessments use qualitative approaches which are resistant to detailed analysis. This session introduces an open source library for the R language for performing a repeatable quantitative risk management at a strategic level which organizations can use to start making real progress in increasing their risk management capabilities.
Presentation Slides: Severski - Evaluator.pptx
It is critical to measure the right things in order to make better-informed management decisions, take the appropriate actions, and change behaviors. But how do managers figure out what those right things are? Questions will be posed to help you set objectives for measurement in your organization.
Presentation Slides: Measuring what Matters.pptx
One of the classic complaints in performing risk analysis is the lack of data, or worse, the lack of "actuarial-quality data". This talk will explore data sources and walk through use cases of gathering the data, parsing and aggregating disparate data sources and continue through extracting and applying the information into your next risk analysis.
Presentation Slides: Data is everywhere.pdf
Probability estimates are the cornerstone of any good risk assessment in which data is sparse or expensive to come by, and are often thought of as one of the best ways to supplement existing information with subject matter expertise. Many risk analysts, however, can run into issues when trying to integrate the opinions of many subject matter experts into a risk management program. Some of these problems are: seemingly contradictory probability estimates, bias that can creep into results and the challenge of collecting and using large amounts of data.