#walrus $WAL
AI systems fail when their training data is quietly changed, censored, or replaced. Most storage layers can’t prove that data stayed exactly the same over time.
Walrus fixes this by making training data verifiable, immutable once committed, and always retrievable. Models can prove what data they trained on instead of trusting a storage provider.
When AI decisions carry real consequences, data integrity isn’t optional.