Cyber Security
To maximize engagement and interaction, seats for the course will be limited and available on a first come, first served basis – to avoid disappointment, book your place today by clicking on our register tab.

Nate Vanderheyden
Vice President, U.S Banks Cyber & Information Security
Morgan Stanley


Yogesh Mudgal
Operational Risk – Global Head of Enterprise Tech/Cyber Architecture &
Engineering Risk



Registration and breakfast

Chair’s opening remarks

Reviewing the evolution of cyber threats and technology capabilities to stay ahead

Increased sophistication of threat actors
Keeping ahead of advancing tactics
Developing capabilities to recognize threat actor activity
Sharing threat intelligence across the industry
Increased risk with economic uncertainty and global tensions
Evolution of incentive from financial to disruption
Increased use of NLP impact to threat
Reviewing progress in quantum computing

Managing increased digital threat and protection of digital identity with increased social engineering capabilities

Countering cyber enabled threat actors
Increased vulnerability with digital advances
Evolution of approaches
Multichannel outreach attempts
Enabling detection and prevention capabilities
Advancing endpoint protection software
Implementing detection and prevention controls
Understanding user behaviors to identify anomalies

Morning refreshment break and networking

Vulnerability management and response tactics to drive cyber resilience

Patching requirements
Managing volume of threats
Resource management to stay ahead of change
Daily threat intelligence
Impact of Russia/Ukraine conflict on threat landscape
Responding to evolution of threats
Developing situational awareness
Leveraging threat based security monitoring
Enhancing incident response and crisis management practices
Opportunities with fully automated response technology
Developing and enhancing cyber resiliency

Protecting the organization from external threats and ensuring due diligence and
oversight of all suppliers

Managing third party/supplier incident
Effective due diligence on suppliers
Managing risk leveraging vendor services
Interaction and engagement with third parties
Cybersecurity assessment of vendors
o Data protection programs
Evidencing controls and security at RFP stage
Benchmarking acceptable levels of security
Understanding interdependencies across supply chains


Nate Vanderheyden,  Vice President, U.S Banks Cyber & Information Security, Morgan Stanley

Lunch break and networking

Managing concentration risks with increased use of cloud services and ensuring resilience of security measures

Developing a risk-based approach
Reviewing breaches within public cloud infrastructure
Migrating and leveraging large cloud service providers
Developing trust in security measures
Infrastructure and maintenance reassurances
Misconfiguration risks
Continual assurance and data validation
Vulnerability management tests and assessments
Integrating cloud security processes with existing security processes
Recognizing cloud vulnerabilities


Yogesh Mudgal,Operational Risk – Global Head of Enterprise Tech/Cyber Architecture &
Engineering Risk,

Leveraging intelligence and converting into actionable metrics through public and private partnerships

Delivering enterprise level intelligence and translating to the board
Quantitative and qualitative measurement
Developing measurement frameworks
Understanding intelligence inputs and outputs
Increased intelligence sharing with continued geopolitical unrest
Working with federal government in an incident
Requirements for responses to government bodies in cyber evento Identifying proper response to an incident
Responses to incidents to mitigate impact

Afternoon break and networking

Attracting and retaining the best cybersecurity talent

Education teams on the importance of security
Understanding new threats facing the organization
Instilling speculation in teams
Balancing training and disciplinary action
Educating on downstream impacts
Instilling cybersecurity hygiene
Implementing a strong cybersecurity culture
Effectiveness of training beyond single day
Modifying job descriptions and scope of degrees
Identifying untapped sources of talent

Reviewing approaches and response to ransomware attacks and mitigation techniques

Managing disruption of ransomware attack
Evolution of ransomware variants
Response approaches to attacks
Protecting data after an attack
Reviewing whether ransom should be paid
o Regulatory and legal implications
Protecting reputation through risk mitigation

End of Masterclass

ML Model Validation


  • 8:15 Registration opens

  • 9:00 The Masterclass will begin

  • We aim to conclude the Masterclass around 5:00

  • Refreshments will be available throughout the day


Registration opens


The Masterclass will begin


We aim to conclude the Masterclass


Refreshments will be available throughout the day

To maximize engagement and interaction, seats for the course will be limited and available on a first come, first served basis – to avoid disappointment, book your place today by clicking on our register tab.
Shamial Afzal

Nicholas Schmidt


Vijay Nair
Head of Advanced Technology for Modelling
Wells Fargo

Krystelle_Bilodeau-bio-photo-2023-120x120 (1)

Agus Sudjianto
Head of Corporate Model Risk
Wells Fargo

The focus of this workshop is to provide the latest development in model validation for Machine Learning with special emphasis on evaluation of conceptual soundness and
outcome analysis. Key topics for conceptual soundness includes model causality, explainability and interpretability, as well as dealing with over-parametrerized/under-specification problems commonly occurred in machine learning models. Designing inherently interpretable machine learning models will be discussed in-depth considering the importance of the methodology for high-risk applications as well as its role for model benchmark. Outcome analysis will cover advanced topics beyond the standard performance analysis such as: identification of model weakness through error slicing, prediction reliability (conformal prediction), model robustness, model resilience under changing environment and model fairness.

Session 1:

Introduction of ML Model Validation: Conceptual Soundness

Introduction to ML and ML Model Validation
Feature Selection and Causality
Post-hoc Explainability

Session 2:

Inherently Interpretable Models

Deep ReLU Networks as Locally Interpretable Models
Exact interpretability for Deep ReLU Networks
Functional ANOVA and Globally Interpretable Models
Constructing Explainable Boosting Machine

Session 3:

Outcome Analysis: In and Out of Distribution Testing

In Distribution Testing: Model weakness and Reliability
Evaluating Reliability of Model Prediction using Conformal Prediction and Slicing
Out of Distribution Testing: Robustness and Resilience
Evaluating Model Robustness against Input Corruption

Session 4:

Model Fairness

Model Fairness and De-biasing
Group Fairness Metrics and Their Calculation: Independence, Separation and
Methodology for Bias Mitigation
Additional Hands-On Exercise and Wrap Up