top of page

Quantum Computing Risk in Cyber Security


Basel explains that quantum computers, if they get big enough and powerful enough, can break the encryption schemes widely used today to ensure secure financial data and transactions. This makes quantum computing one of the most significant cybersecurity threats facing the financial system, potentially exposing all financial transactions and much of our existing stored financial data to attack.


While it is not yet clear when quantum computing technology might be adopted on a large scale, its potential as a cyber threat to the financial system is already a cause for concern. Malicious actors can intercept and store classically encrypted sensitive data with the intention of decrypting it later when quantum computers are powerful enough to do so.


Recognizing these potential risks to its systems and data, the financial sector needs to preemptively implement robust quantum communication and data protection technologies. Given the long-term sensitivity of financial data and the complexity of central bank IT systems, a transition phase must be initiated well in advance so that quantum-resistant encryption schemes can be implemented.

We believe that the recent discovery of the new material for building superconductors LK 99, at room temperature, could accelerate the quantum leap.

The objective of the course is to expose the impact and risks of computing and quantum technologies in the cybersecurity of financial institutions, to achieve the objective we explain what quantum computing, quantum algorithms, quantum mechanics and quantum and post-quantum cryptography, lattice based are. , to defend against possible quantum threats.

The course explains in detail the interaction between geopolitics, reputational risk and cybersecurity, the possible scenarios that are expected with the progress of quantum computers.

The use of artificial intelligence to strengthen the cybersecurity of banks is explained. The XOI methodology for measuring cyber risk is briefly explained.

The global vision of CyberRisk, cyberattacks and losses suffered by financial institutions, methodologies and good practices on cybersecurity in business processes are exposed, and some technical standards for management and control are explained, such as NIST, Cobit 5. and ISO 27001.

Cyber Risk Appetite, Cyber Risk Limits and Cyber Risk Tolerance methodologies for the governance and control of Cyber Risk are exposed.

Traditional methodologies such as logistic regression and other, innovative, machine learning methodologies are exposed, such as: decision trees, naive bayes, KKN, LASSO logistic regression, random forest, neural networks, Bayesian networks, Support Vector Machines, gradient boosting tree, etc

The use of artificial intelligence and in particular machine learning and deep learning to strengthen cybersecurity is explained, advanced models are shown to detect anomalies, transactional fraud, phishing, cyber attacks, intrusions and malware.

Advanced deep learning is explained, with convoluted neural networks for facial recognition, the powerful Generative Adversarial Network (GAN) to detect adverse attacks from machine learning algorithms, recurrent neural networks for the classification of CyberRisk events and the multilayer perceptron for detection of intrusions and anomalies.



This program is aimed at directors, managers, consultants, regulators, auditors and risk analysts, operational risks, cyber risks, as well as those professionals who are implementing cybersecurity measures. Professionals who work in banks, savings banks and all those companies that are exposed to cyber risks.


Price: 8.900 €


  • Europe: Mon-Fri, CEST 16-20 h


  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h






Level: Advanced


Duration: 40 h



Presentation: PDF

Examples in Python y R



Quantum Computing Risk in Cyber Security


Modular Agenda

Cybersecurity in Basel III

Module 1: Cyber Resilience

  • Cyber risks in banking

  • CyberRisks in Latin America and Europe.

  • Cyber Resilience Standards and Guidelines

  • Case Study 1: Recent Regulatory Initiatives: Australia, Germany and the US Minimum Requirements

  • Cybergovernance

    • Cybersecurity strategy

    • Management roles and responsibilities

    • Recognition of the importance of the board of directors and senior management

    • Variety of supervisory approaches regarding the second and third lines of defense (3LD)

    • Case Study 2: Roles and responsibilities of chief information officers (CISOs) in cyber governance

    • Cyber risk awareness culture

    • Architecture and standards

    • Cybersecurity Workforce

    • Case Study 3: Frameworks for professional cybersecurity training and certification programs

  • Risk management, testing, and incident response and recovery approaches

    • Methods for monitoring cyber resilience

      • Risk specialists assess information security management and controls

      • Jurisdictions are increasingly engaging with industry to address cyber resilience

    • Testing of information security controls and independent assurance

      • Mapping and classification of business services should inform testing and assurance

      • Penetration Test

      • Taxonomy of cyber risk controls

    • Response and recovery and exercise tests

      • Service continuity assessment, response and recovery plans, and continuous learning

      • Joint public-private exercise

      • Case Study 4: “Exercise Resilient Shield”

    • Cybersecurity and resilience metrics

      • Cybersecurity and resilience metrics

      • Emerging Resilience Indicators

  • Communication and information exchange

    • Overview of cross-jurisdictional information sharing frameworks

    • Sharing information between banks or peers

    • Case Study 5: FS-ISAC: key features and benefits

    • Sharing from banks to regulators

    • Sharing between regulators

    • Case Study 6 - Bilateral exchange of cybersecurity information between the Hong Kong Monetary Authority (HKMA) and the Monetary Authority of Singapore (MAS)

bottom of page