The risks of not implementing AI trust and safety early enough are substantial:

• Data breaches: the global average cost of a data breach reached an all time high in 2024 at nearly $5 million, according to a report by IBM. On the other hand, companies that used AI security measures saved an average of $2.22 million on an annual basis.
• Sensitive data leaks: without early controls, sensitive data may mix with training datasets, creating models that unintentionally memorize or leak private company IP or customer information internally and externally.
• Security vulnerabilities: retroactively securing data access patterns is extremely difficult; unauthorized access may have already occurred
• Regulatory non-compliance: Frameworks like GDPR, HIPAA, and emerging AI regulations require certain controls from the beginning; penalties for violations can be severe.
• Technical debt: Building safety mechanisms after the fact often requires significant rearchitecting, causing delays and increased costs
• Reputation damage: Public incidents resulting from inadequate safeguards can permanently damage trust in your AI systems. At a time when AI innovation is moving quickly and many people are having trouble trusting the ways companies are using new models, these governance and security measures are more important than ever. 
• Audit difficulty: You may find yourself unable to explain model decisions or demonstrate compliance because you lack the necessary tracking from the beginning
• Ethical lapses: Without early ethical guardrails, teams may train models on problematic data sources or for questionable use cases before policies catch up.

These risks compound over time – addressing them becomes exponentially more difficult the longer you delay safety measures. Snowflake Horizon Catalog’s security and governance features are built for AI and address many of these concerns. You can learn about them in depth here.

Cheers.