As AI models grow more sophisticated, their reasoning can sometimes produce dangerous outputs that weren't anticipated during training. Current testing methods might miss these edge cases until it's too late.
Build an API or IDE plugin that analyzes AI outputs against known risk patterns, like unsafe logical leaps or hidden biases. It would flag concerning responses during development and testing.
AI developers and enterprise teams deploying LLMs would pay for this as insurance against reputational damage and compliance failures.
Start by creating a simple web interface where users can paste model outputs to check against a basic ruleset of known problematic patterns.
The biggest risk is keeping the detection rules comprehensive enough as models evolve, requiring constant updates.