AI safety
The research and engineering discipline focused on making AI systems behave reliably, refuse harmful requests, and fail gracefully under unexpected inputs — covering both training-time alignment and deployment-time guardrails.
AI safety is the umbrella discipline for "make sure the system does not cause harm." It covers training-time alignment (RLHF, constitutional AI, instruction tuning), deployment-time guardrails (input filters, output classifiers, tool-call gates), and operational practices (red teaming, monitoring, incident response).
For agent operators in 2026, the practical AI safety stack: pick a frontier model from a vendor with serious safety investment, layer guardrails on inputs and outputs, gate irreversible tool actions, run red-team probes in CI, and monitor for jailbreak patterns. None of these alone is enough; the layered approach is what works.
The biggest gap in practice is incident response. Most teams have well-tuned models and good guardrails; few have a clear playbook for what happens when the agent does something it should not have. Build that playbook before launch, not after.
Frequently asked
What is the difference between AI safety and AI alignment?+
Alignment is specifically about getting models to pursue intended goals. Safety covers alignment plus robustness, misuse prevention, and incident response. Alignment is a sub-discipline of safety.
Do I need an AI safety program if I just use ChatGPT?+
For internal personal use, no. For any deployment that touches customers, money, or sensitive data, yes — even if you only use the vendor's API. Vendor safety covers the model; your safety program covers the deployment.