AI Security

What AI Security is

The AI attack surface includes training data poisoning, model theft, adversarial inputs, prompt injection, insecure plugins and leakage of secrets through outputs.

How it works

Practices include threat modelling against published frameworks, least-privilege tool access, input and output filtering, provenance checks on models and datasets, red teaming and incident response plans that cover model behaviour.

Why it matters

AI features are being deployed faster than security review can keep up, and they connect language interfaces directly to internal systems.

Common uses

• →Pre-deployment security review
• →Agent permission architecture
• →Vendor model assessment