AI Governance Assessment Case Study

At Casaba Security, our Assessment Program Managers (APMs) provide critical guidance to product teams as they apply guardrails, mitigate risks, and tune their LLM-based systems. Our expertise ensures that AI is used responsibly while maintaining the innovation that drives business growth. This case study demonstrates how we systematically approach AI risk assessment and mitigation.

The Challenge: Weather Prediction AI

Consider a product team developing weather prediction software that uses AI to assist news agencies. The system analyzes previous weather data and suggests predictions, creates graphics and charts, and produces summaries for 7-day and 30-day forecasts, along with recommended topics for news agencies to cover.

Before deployment, this team needed to identify potential risks and implement appropriate mitigations to ensure responsible AI use. This is where Casaba's assessment methodology proves invaluable.

Our Assessment Approach

Casaba begins every AI assessment with a series of clarifying questions designed to uncover potential vulnerabilities and risks. For this weather prediction system, our consultants focused on three critical areas:

Risk Identification

Our systematic risk assessment identified several potential harms in the weather prediction system:

Primary Risk: Prediction Inaccuracy During Severe Weather

AI-generated content can sometimes be incorrect. For weather predictions, this presents minimal harm in fair conditions but could have significant consequences during severe weather events like storms, tornados, and hurricanes.

Secondary Risks Based on System Design:

• Overreliance: Users might issue AI predictions without manual verification, potentially compromising accuracy and safety.
• Malicious Input: If the system accepted user data or allowed free-text queries, it could be vulnerable to prompt injection attacks or manipulation.
• Inappropriate Content: Free-text capabilities could enable requests unrelated to weather that might produce harmful responses.

Recommended Mitigations

Based on our assessment, Casaba recommended several mitigation strategies to the product team:

For Prediction Inaccuracy:

• Disable AI predictions during severe weather advisories, defaulting instead to official weather service alerts
• Implement clear warnings about AI-generated content during potentially hazardous weather conditions
• Ensure safeguards operate independently of AI systems to prevent cascading failures

For Overreliance:

• Frame AI outputs as suggestions requiring human review rather than definitive predictions
• Include guidance encouraging users to verify data before making decisions
• Provide clear access to source data that informed the AI's conclusions

For Systems with User Input:

• Implement robust input validation and filtering
• Establish guardrails that prevent the system from responding to off-topic or potentially harmful queries
• Deploy monitoring systems to detect potential abuse or manipulation attempts

Results

By implementing Casaba's recommendations, the product team was able to deploy their weather prediction AI with appropriate safeguards that balanced innovation with responsibility. The system now includes:

• Automated safety mechanisms that disable AI predictions during severe weather events
• Clear user interfaces that present AI outputs as suggestions requiring professional verification
• Robust input validation that prevents manipulation
• Transparent sourcing of data used in predictions

These measures significantly reduced the risk of harm while preserving the value and utility of the AI-powered weather prediction system.