Buyer questions
Why Standard Security Tests Fail for AI
AI systems fail across security, truth, fairness, privacy, and accountability at the same time. These are the questions an audit has to answer.
Can the system be manipulated?
Prompt injection, jailbreaks, poisoned retrieval, and multi-turn manipulation can make a safe-looking AI behave unsafely.
Can it expose private data?
Customer records, hidden prompts, vendor data, and cross-user context can leak through behavior rather than a normal database breach.
Can users challenge a bad output?
High-impact AI needs human escalation, correction paths, and a way to recover from model mistakes.
Does it treat similar users consistently?
Mirrored prompts should not reveal hidden viewpoint favoritism, selective skepticism, or uneven refusal behavior.
Are AI claims disclosed clearly?
Customers should know when AI is involved, what it can and cannot do, and when a human is available.
Can the release be trusted over time?
A grade only covers a defined release. Model changes, RAG changes, prompt changes, and new tools require review.
These questions came from the old research. The audit makes them operational.
The original Algodai work focused on never-forgetting systems, mercy, appeal, and restoration. The commercial audit turns that into concrete checks for security, correction, accountability, and release governance.