Defining AI Safety
AI safety focuses on the internal behavior of the model. It asks: How do we ensure a super-intelligent model doesn't behave in ways that are harmful to humanity? Safety research covers topics like value alignment, interpretability, and preventing catastrophic model failure. It is largely a concern for model labs (like OpenAI or Anthropic) and academic researchers.
Defining AI Security
AI security focuses on the environment around the model. It asks: How do we prevent an attacker from tricking the model (prompt injection)? How do we stop an autonomous agent from deleting our database? How do we keep our sensitive data from leaking into the model's training set?
Security is the layer that enables enterprise deployment. It includes:
- Runtime Policy Enforcement: Blocking unauthorized tool use.
- Threat Detection: Identifying prompt injection attacks.
- Data Governance: Filtering PII before it reaches the model.
Why the distinction matters for your company
If you are a CISO or an operations leader, "AI Safety" is a boardroom topic, but "AI Security" is an infrastructure requirement. You cannot wait for the industry to "solve" safety before you start governing the AI tools your employees are already using.
Tooling like Shield Web provides immediate AI security by discovering shadow AI use and enforcing prompt-layer controls, regardless of the safety profile of the underlying model.
