Product · AI governance
Shadow AI is shadow IT that writes code.
Your teams are already using AI to build internal tools, scripts, and services, and that software reaches production. TruStacks is the governed path for what those agents ship: every change is proposed as a signed, policy-checked pull request that a named human approves, on your own infrastructure, with the audit trail generated as a side effect of the work.
The pattern repeats
A decade ago it was unsanctioned SaaS. Now it compiles.
Shadow IT happened because the sanctioned tools were slower than the work. Shadow AI is the same story with a shorter fuse. An engineer who cannot get an approved path will use an unapproved one, and the difference this time is that the output is not a spreadsheet in a personal Dropbox. It is code, in your repository, on its way to your cluster.
#1
Source code is the single largest category of sensitive data involved in generative-AI data-policy violations, ahead of regulated data, intellectual property, and credentials.
Netskope Cloud and Threat Report 2026 · Enterprise cloud-traffic telemetry, published January 2026
47%
47% of enterprise genAI users still reach those tools through personal accounts, bypassing enterprise controls.
Netskope Cloud and Threat Report 2026 · Enterprise cloud-traffic telemetry, published January 2026
78% → 47%
The share of enterprise genAI users reaching those tools through personal accounts fell from 78% to 47% over twelve months.
Netskope Cloud and Threat Report 2026 · Enterprise cloud-traffic telemetry, published January 2026
223 / month
The average organization now records roughly 223 generative-AI data-policy violations per month.
You cannot ban your way out of this.
Prohibition is what produces shadow AI in the first place. And there is evidence the sanctioned path can win: over twelve months, the share of enterprise genAI users on personal accounts fell from 78% to 47% as activity moved onto managed infrastructure. That is a trend rather than proof of cause, but it points somewhere useful.
So the question is not whether your engineers should build with AI. They already are. It is whether what they produce passes through a control you could show an auditor.
Ban it
The work moves to personal accounts and unreviewed pipelines. You lose the visibility you were trying to protect.
Allow it, unmanaged
Agent output arrives at agent throughput. In Veracode’s 2026 benchmark, syntax correctness exceeded 95% while only about 55% of generation tasks produced secure code. Code that compiles and looks right is what review is worst at catching.
Govern the path
Agents propose. A signed policy bundle decides. A named human approves. The evidence is a by-product rather than a project.
Code-security figures: Veracode, Spring 2026 GenAI Code Security Update. This is 45% of tested code-generation tasks under a security-focused benchmark. It is not a measurement of all AI-written code in production.
The control questions
What your auditor will ask, and where the answer lives.
AI governance frameworks differ in vocabulary and converge on the same handful of questions about oversight, traceability, and change control. These are the ones TruStacks is built to answer.
The clock is no longer theoretical. The EU AI Act’s high-risk track became fully operative on 2 August 2026, carrying mandatory risk management, data governance, logging, and human-oversight obligations. ISO/IEC 42001 and the NIST AI Risk Management Framework ask their own versions of the same questions.
- Which AI-proposed changes reached production, and who approved each one?
- Every change is a pull request with a named human approver. The merge is the record. There is no path by which an agent ships something nobody signed off on.
- What rules were in force when that change was approved?
- A signed, versioned Rego bundle. You can check out the exact policy that was in force on the day of any given merge and read it, because it is an artifact in version control rather than a setting in a vendor console.
- Can a team weaken a control to get their change through?
- No. Customer overlay rules can only ratchet stricter than the constitution, never looser, and a linter proves it at compile time. A rule that tries to loosen a baseline requirement fails to build.
- What can the agents reach?
- Not production. Agents hold no production credentials and open pull requests against a separate platform repository. Your ArgoCD or Flux deploys what your people merged.
- Where does the code go?
- Nowhere. Source stays in your environment. TruStacks emits configuration; you run it on your own infrastructure.
To be clear about scope. TruStacks governs what AI agents propose to ship into your software delivery pipeline. It is not a substitute for an organization-wide AI governance program, and it does not monitor model behavior, manage an agent registry, or assess model risk. It covers one surface thoroughly: the path from an agent’s proposal to your production cluster. How we handle your data sets out what leaves your environment, which is the next question most security reviewers ask.
Give your teams the fast path. Keep the control.
Run the agent crew locally in about two minutes and watch it open a policy-checked pull request against a real repository. No signup, no credit card.