Policy-As-Code removes guesswork from security and compliance. Rules, conditions, and actions are expressed in code, versioned, reviewed, and tested like any other part of the stack. A lightweight AI model adds speed and adaptability without the resource demands of GPU inference. Engineers can deploy, run, and update policies on commodity hardware with minimal footprint.
CPU-only AI models are practical. They avoid dependency on specialized chips, simplify deployment, and reduce costs. They can run in containerized environments, on-prem servers, or edge devices. For Policy-As-Code, this means policies can be enforced consistently across development, staging, and production environments without infrastructure drift.
The model reads policy definitions from source control, evaluates inputs in real time, and outputs decisions with low latency. It can integrate with CI/CD pipelines, Kubernetes admission controllers, API gateways, or custom middleware. By combining deterministic rules with machine-learned patterns, enforcement stays strict while adapting to new threats or changes in behavior.