AI security in practice

AI security, in theory, is a topic broad enough to include almost anything. In practice, inside a company, it comes down to a specific, manageable set of controls — applied across three concrete layers: the data that feeds into or passes through models, the models and agents themselves, and everyday corporate usage.

Treating the topic this practically, instead of as an abstract philosophical problem, is what lets a company move from policy discussion to real control implementation.

Controls over data

The point of greatest immediate risk is the data going in and out of interactions with AI models — whether in a prompt, or in training or fine-tuning an internal model. DSPM (data security posture management) helps map where sensitive data exists and how it moves within the environment, including flows that involve AI. DLP adapted to this specific context stops that data from leaving through an uncontrolled path.

Controls over models and agents

Models, and increasingly AI agents operating with autonomy — taking actions, not just generating text — introduce a new risk surface: a compromised or manipulated agent can execute real actions in the environment, not just produce an incorrect response. That demands permission control and action-scope discipline as rigorous as what's applied to any privileged identity.

Monitoring these agents' behavior — what they're accessing, executing, and changing — needs to exist with the same level of visibility already expected of any automated system with elevated privilege.

Controls over corporate usage

The final layer is usage governance: which generative AI tools are approved, who can use them and for what purposes, and how that's audited over time. Without this governance layer, even the best technical controls over data and models leave out the largest share of real usage — the kind that happens by individual decision, outside any formal corporate tool.

Three layers, one architecture

Structuring these three layers of control — data, models, and corporate usage — within a coherent Data & AI portfolio, prioritized by each environment's real risk, is what UNIQ helps companies do.