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Using a Bubble Chart to Prioritize AI Use Cases Across Cost, Risk & Value

Sunil Soares, Founder & CEO, YDC | October 2, 2024

Here is a bubble chart that the YDC team developed to prioritize AI use cases at a bank (x-axis: cost, y-axis: risk, z-axis/bubble size: opportunity).

We used a qualitative scale 1-5 across all three axes. Use cases spanned the gamut from Automation of Customer Communications, Microsoft Copilot for Office Productivity, ACH Labeling, Fraud Analytics, Credit/Loan Retention Modeling, Customer Service & Current Expected Credit Loss (CECL) Modeling.

Since their CFO did not feel they had the luxury to “invest ahead” on AI, we decided to focus on low risk/low cost use cases initially before embarking on higher risk/higher value use cases.

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Fairness & Accessibility

Component

Component ID: 5.0

Mitigate bias and manage AI accessibility.

List of Controls:

  • Bias
  • Accessibility
Mitigate Bias
Control
View Details
ID: 5.1

Ensure that AI systems are fair and manage harmful bias.
Component
Sub-Control
Regulation
  
Source
Address Fairness and Accessibility EU AI Act -Article 10(2)(f)(g) – Data and Data Governance (“Examination of Possible Biases”)

Vendors

Detect Data Poisoning Attacks
Control

View Details

ID: 10.4.1

Data poisoning involves the deliberate and malicious contamination of data to compromise the performance of AI and machine learning systems.

Component
Control
Regulation
Source
10. Improve Security10.4 Avoid Data and Model Poisoning AttacksEU AI Act: Article 15 – Accuracy, Robustness and Cybersecurity 

Vendors

Improve Security
Component

Component ID: 10

Address emerging attack vectors impacting availability, integrity, abuse, and privacy.  

List of Controls:

  • Prevent Direct Prompt Injection Including Jailbreak
  • Avoid Indirect Prompt Injection
  • Avoid Availability Poisoning
    • Manage Increased Computation Attack
    • Detect Denial of Service (DoS) Attacks
    • Prevent Energy-Latency Attacks
  • Avoid Data and Model Poisoning Attacks
    • Detect Data Poisoning Attacks
    • Avoid Targeted Poisoning Attacks
    • Avoid Backdoor Poisoning Attacks
    • Prevent Model Poisoning Attacks
  • Support Data and Model Privacy
    • Prevent Data Reconstruction Attacks
    • Prevent Membership Inference Attacks
    • Avoid Data Extraction Attacks
    • Avoid Model Extraction Attacks
    • Prevent Property Inference Attacks
    • Prevent Prompt Extraction Attacks
  • Manage Abuse Violations
    • Detect White-Box Evasion Attacks
    • Detect Black-Box Evasion Attacks
    • Mitigate Transferability of Attacks
  • Misuse of AI Agents
    • Prevent AI-Powered Spear-Phishing at Scale
    • Prevent AI-Assisted Software Vulnerability Discovery
    • Prevent Malicious Code Generation
    • Identify Harmful Content Generation at Scale
    • Detect Non-Consensual Content
    • Detect Fraudulent Services
    • Prevent Delegation of Decision-Making Authority to Malicious Actors

Identify Executive Sponsor

View Details

ID : 1.1 

Appoint an executive who will be accountable for the overall success of the program.

ComponentRegulationVendors
1. Establish Accountability for AIEU AI Act 
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