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A bank has an external facing web application that is deployed as a microservice using AWS container and serverless services. After running the application on AWS for a year, we have encountered various web application vulnerabilities that have led to the exploitation of misconfigured AWS resources and coding issues.

We are looking for a solution that can proactively protect our web application from public attacks. The solution should follow the OWASP Top 10 security standards. Additionally, the solution should proactively monitor and ensure that all cloud resources adhere to cloud security best practices and the bank's internal standards.

In summary, we need a solution that can:
1. Protect the application from attacks by following OWASP Top 10 guidelines
2. Proactively monitor our AWS resources to ensure proper configuration as per cloud security best practices and the bank's standards
3. Help avoid exploitation of misconfigurations and coding vulnerabilities in the future

A bank's IT division wants to modernize their three-tiered web application from their on-premises servers to the AWS cloud. Their current on-premises setup includes Web servers that serve the website content and a MySQL database that stores the content.

They have some issues with their application today:
Heavy traffic from marketing campaigns crashes the application
Manual application updates deployment risks to system availability
Have to take the site down in the midnight to deploy updates
No tool to monitor infrastructure and application status

To provide a better experience for customers, they want to move to the AWS cloud to:
Handle heavy traffic without crashing
Automate updates deployment without any downtime
Proactive monitoring infrastructure and application status

A bank’s HR division wants to Develop a model application to support the Bank Talent Acquisition (TA) team in finding suitable candidates from the existing CV database. The application will be built using modern technology, leveraging AWS cloud services, and harnessing the power of generative AI for conversational interaction with TA users.

The application should have the following capabilities:
1. Automate data processing to handle unstructured data in various formats (PDF, Word, and Excel) for identification, sorting, and categorization into usable data.
2. Provide a simple front end for end users (TA) to input search queries, view response results, and list the top 5 Candidates in order of matching score. Candidate with higher suitability will displayed first, while less suitable ones will highlighted differently. TA users should be able to view aggregates such as the number of submitted CVs falling under enterprise development, average percentage of individuals with particular skills, etc.

In today's interconnected digital landscape, businesses operate within complex ecosystems where data flows seamlessly across various platforms. The challenge is to leverage this wealth of data to enhance Customer Lifetime Value (CLV) systematically. Participants in this hackathon will explore innovative solutions to optimize CLV within a data ecosystem, fostering customer loyalty and sustained revenue growth

In this hackathon, participants will leverage ecosystem’s fictionalized datasets to explore techniques for systematically enhancing CLV. Example focus areas include predicting high-value customer segments, optimizing cross-platform advertising, and crafting personalized recommendations to improve retention.

Successful solutions will creatively harness the provided data to foster loyalty and sustained revenue growth. Participants can showcase machine learning, statistical modeling, or other analytical approaches within their proposals. However, business viability and intuitive interpretation of models are key evaluation criteria. They can simulate data to demonstrate challenges and build the prototype.

In the ecosystems, it’s challenging to obtain a holistic view of customer profiles (a single customer ID) across different subsidiaries and a view of customer interactions across different channels leading to fragmented data, inefficiencies in communication and missed opportunities for personalized engagement and cross-sell opportunities

In this hackathon, participants will creatively create a comprehensive and accurate profile of each customer by aggregating data from various touchpoints. Participants can showcase how they design and create data models, build ETL pipelines and enrich data from different data sources by leveraging different database systems, big data platform and programing languages for data processing and transformation. They can simulate data to demonstrate challenges and build the prototype.

The goal of this challenge is to design and develop a robust data tokenization system capable of preserving the original format and statistical distribution of sensitive data while ensuring data security and compliance.

The system should support the tokenization of the following data types:
   - Name
   - Email address
   - Phone number
   - Credit/debit card number
   - Date of birth
 - Identification number (e.g., Social Security Number, National ID)
   - Numeric data (e.g., account numbers, transaction IDs)

The tokenization process must ensure that the original data format and statistical distribution are maintained after tokenization.

Everyday, there are projects/initiatives/developments which are initiated in the bank. Some of them are complex enough to require serious efforts to create the right design of the solution. The design includes but is not limited to software architecture, integration architecture, data architecture, with consideration about security, maintainability, scalability, availability, etc. This task is often done in collaboration between development teams and architecture team. However, due to limited capacity of the centralized architecture team, some projects might need to wait and could not be processed in expected time.

To solve this problem, the bank wants to leverage the power of generative AI and open knowledge on Internet (for example: architecture blueprint shared by AWS, etc.) and on Intranet to help teams and architects generate the first draft of designs based on the description of the requirements