Many AI code security tools assume you are comfortable with:

• cloud-hosted scanning
• external control planes
• data leaving your environment for analysis

For regulated, government, and high-accountability organizations, that assumption is a dealbreaker.

They need AI code security that works inside their boundary.

#Why deployment model becomes a security decision

When you scan code, you handle:

• source code
• secrets
• dependency manifests
• internal policy definitions
• audit evidence

If that data leaves your environment, you are introducing:

• data residency risk
• compliance exposure
• supply chain trust questions
• operational dependency on external uptime

For critical systems, the scanning layer itself becomes a security boundary.

#The hybrid model: what stays where

#On-prem / private cloud

• the scanning engine
• policy definitions
• evidence storage
• audit logs
• SCM/CI integration endpoints

#External (if any)

• model updates and rule feeds
• telemetry for product improvement (optional, opt-in)

The principle is simple:

policy enforcement and evidence stay inside.

#Integration points

A practical on-prem deployment connects to:

• GitHub Enterprise / GitLab / Bitbucket (SCM)
• Jenkins / GitHub Actions / GitLab CI / Azure DevOps (CI)
• Slack / Teams / Email (notifications)
• SIEM / GRC tools (evidence export)

All within your network boundary.

#What to require from a vendor

If you are evaluating AI code security for sensitive environments:

• Can the scanner run fully on-prem?
• Can policy be defined and enforced locally?
• Can evidence and logs stay in your infrastructure?
• Can you control updates and change windows?
• Is there a hybrid option for less sensitive repos?

These are not infrastructure preferences. They are governance requirements.

#The Cyblox view: deploy where control matters

Cyblox GenAI Code Security supports:

• fully on-prem deployment
• hybrid models (sensitive repos on-prem, others cloud)
• customer-controlled policy and evidence
• integration with existing SCM and CI
• controlled update models

More at: /solutions/security/genai-code-security/.

#Closing thought

AI code security is not just about finding bugs.

It is about governing how AI-generated code enters your systems.

If you cannot control where that governance runs, you do not fully own the security boundary.

If AI-generated code is entering your repos, add pre-merge security, secrets, licensing, and policy checks—with review evidence—without changing how developers work.