What is AI Infrastructure Security?
AI Infrastructure Security is the practice of protecting the technical components that build, run, and connect AI systems. This includes model endpoints, vector databases, training pipelines, embedding stores, and agent orchestration frameworks against unauthorized access, tampering, and abuse.
What it is
AI Infrastructure Security covers the attack surface that exists below the model itself. When an organization trains, hosts, or chains AI models together, it creates a set of technical components that can be compromised independently of the model weights or the application layer above them.
The main components in scope are:
- Model endpoints: APIs that serve inference results. These can be queried to extract model behavior, abused for denial-of-service, or manipulated through crafted inputs.
- Vector stores and embedding databases: These hold semantic representations of sensitive documents. A misconfigured vector store can leak proprietary data to any caller.
- Training and fine-tuning pipelines: Code, data, and compute resources that shape model behavior. Tampering here can introduce backdoors or biases before a model ever reaches production.
- Agent frameworks and orchestration layers: Systems that let models call tools, browse the web, or execute code. Each tool call is a potential privilege escalation or data exfiltration path.
Why it matters
Traditional application security does not map cleanly onto AI components. A vector store is not a relational database. A model endpoint is not a REST API in the conventional sense. Existing controls for secrets management, network segmentation, and access control still apply, but they must be extended to cover AI-specific behaviors such as prompt routing, retrieval-augmented generation pipelines, and multi-agent communication.
A compromise at the infrastructure level can undermine every safety control applied at the model or application layer. Guardrails on outputs mean little if an attacker can modify the training data or intercept retrieval results before they reach the model.