Assess and enhance the resilience of a LLM used for market sentiment analysis against cyber threats

Executive Summary

Use Case:

Large Language Model (LLM) resilience against cyber threats using Fairly AI-Compliance-in-a-box Simple Start Plan

Purpose:

To assess and enhance the resilience of a LLM used for market sentiment analysis against cyber threats

Details:

• Conduct Red-Teaming exercises and training to simulate various cybersecurity threats (e.g., data poisoning, evasion attacks, model extraction attempts) against selected LLM model

• Use Fairly’s Asenion product to analyze models’ responses to these threats and detect vulnerabilities against policies

• Provide a comprehensive report detailing identified vulnerabilities and set up policies and controls to continuously monitor and enhance model resilience

Outcome:

Improved cybersecurity posture, strengthened LLM capable of resisting various cyber threats

‍

Overview

The GameStop saga, which took place in January 2021, was a unique event characterized by a coordinated effort by retail traders, primarily from the subreddit r/WallStreetBets, to buy shares and options of GameStop (GME). This led to a "short squeeze" that caused the stock price to surge dramatically, creating significant volatility and risks in the market.

With the financial world evolving at an unprecedented pace and incidents like the GameStop saga revealing the profound influence of non-traditional market forces, there's a clear need for modern, AI-based tools to manage market sentiment analysis. However, before diving into the development of such tools, implementing an AI governance, risk and compliance platform like Fairly's is critical.

‍

Why you need a Governance, Risk & Compliance (G.R.C.) solution for AI

1. A Proactive Approach to Risk Management: Before deploying any AI-based solution, like a market sentiment analysis tool, it's crucial to have a system in place to manage and mitigate potential risks associated with AI. Fairly's platform provides just that, offering a comprehensive solution to manage the unique risks associated with AI models.

2. Modernizing Model Risk Management: Spreadsheets and traditional GRC systems such as SAS and Archer, while familiar, are increasingly inefficient and error-prone when managing complex AI models. Fairly's platform offers a modern, streamlined, and automated solution that can handle the complexities and scale of AI model risk management.

3. Regulatory Compliance: Fairly's platform is designed to comply with regulations such as OCC 2011-12 and FRB SR 11-7, ensuring your organization stays within the legal and regulatory framework.

4. Bias and Fairness Monitoring: AI models can inadvertently propagate bias, leading to unfair outcomes. Before deploying an AI-based market sentiment analysis tool, your organization must be equipped to identify and mitigate such biases. Fairly's platform offers robust bias detection and fairness monitoring capabilities.

5. Traceability and Auditability: AI models, by their nature, can be complex and opaque. To ensure accountability, it's crucial to maintain comprehensive logs of model development, validation, and performance. Fairly's platform provides strong traceability and auditability features, making it easier to review and inspect AI models.

6. Model Validation: Fairly's platform supports rigorous validation of AI models, ensuring they perform as expected and helping to avoid costly and potentially reputation-damaging errors.

7. Scalability: As AI adoption increases, the number of models to govern could grow significantly. Fairly's platform is scalable and can effectively manage an increasing number of models, whereas spreadsheets would struggle.

‍

Process for ensuring LLM resilience

Here is how the Fairly AI-Compliance-in-a-box Simple Start Plan would help to ensure the safe, secure, and compliant adoption of this LLM:

1. Red-Teaming: This step involves an independent group mimicking potential cyber attackers' actions to challenge the LLM. The aim is to simulate various cybersecurity threats, such as data poisoning, evasion attacks, and model extraction attempts, and assess the AI model's resilience.

2. Analysis Using Fairly’s Asenion Tool: The Asenion tool is used to analyze the LLM's responses to the threats simulated during the red-teaming exercises. It checks for any vulnerabilities and policy violations.

3. Reporting and Policy Setup: Post-analysis, a comprehensive report is prepared detailing the identified vulnerabilities and policy violations. This report serves as the basis to implement or refine policies and controls that continuously monitor and enhance the LLM's resilience.

4. Continuous Monitoring and Improvement: With the implemented policies and controls, the LLM is continuously monitored to identify vulnerabilities or policy violations. Regular red-teaming exercises and Asenion analysis ensure ongoing improvement of model resilience.

‍

Outcome

In this use case, the organization aims to improve the resilience of its LLM against various cyber threats. The continuous monitoring and improvement process ensures that the model evolves to better resist such threats, thereby improving the overall cybersecurity posture of the organization.