What is AI Security?

Traditional cybersecurity secures infrastructure: firewalls, access controls, encrypted connections, vulnerability scanning on known attack surfaces. AI systems introduce a new layer — the model itself — that sits on top of that infrastructure and creates a different category of risk. A well-secured server running a poorly secured AI agent is still a vulnerable system.

Prompt injection is the most well-documented AI-specific attack vector. An attacker embeds instructions inside content the model will process — a document the AI is asked to summarise, a webpage it retrieves, a customer message it handles. Those instructions can cause the model to ignore its system prompt, exfiltrate information, or take unintended actions. In a simple question-answering chatbot, the blast radius is limited. In an AI agent with access to email, CRM, files, or external APIs, a successful prompt injection can cause significant damage.

Data exposure through model outputs is a separate issue. A model trained on or given access to sensitive company data can leak that data through normal operation — in responses to users who should not see it, through verbose error messages, or through outputs that reconstruct information from training. This is not a breach in the traditional sense; no system was hacked. The model simply answered a question it should not have been able to answer.

For businesses deploying AI agents — whether built internally or through third-party platforms — AI security means reviewing what data the model has access to, how outputs are filtered before reaching users, what tools and APIs the agent can call and with what scope, and what the failure mode is when the model produces unexpected output. Private AI deployments, where the model runs on your own infrastructure, address the data exposure dimension by keeping sensitive information off third-party servers entirely.