45% of AI-generated code contains known security vulnerabilities — a rate that has held flat across two years of model releases, according to Veracode's Spring 2026 GenAI Code Security report, which evaluated over 150 LLMs across 80 coding tasks in four languages. If the models themselves are not closing the security gap, the governance infrastructure your organization builds around them must. The question most engineering leaders face now is not whether to invest in an AI code governance platform, but which capabilities are non-negotiable.
Why Your Existing Security Stack Falls Short
SAST scanners, dependency audits, and manual PR review were designed for human development cadences. An experienced developer opens three or four pull requests a day. An autonomous AI coding agent running in a feature branch can open dozens. At that volume, post-merge scanning catches vulnerabilities after they have already landed in your codebase, secrets are flagged after exposure, and manual review becomes a statistical rubber stamp rather than a real control.
The infrastructure gap is well-documented. JFrog's 2026 Software Supply Chain Security State of the Union — drawn from analysis of 18.2 billion artifacts — found that detections of malicious npm packages surged 451% year-over-year, yet only 40% of organizations have any detection capability in place. Secrets detection adoption sits at 28%. Worse, 48% of organizations require a week or longer to generate compliance audit proof per application — meaning the audit trail most teams rely on in a breach investigation is already a week behind.
Understanding the compounding cost of ungoverned AI coding agents helps calibrate the business case before beginning any platform evaluation — the exposure is not linear as AI-generated code share grows.
Five Capabilities That Define a Real Governance Platform
Governance platform marketing is saturated with vague claims about AI-aware security and intelligent code review. The following capability set separates platforms with real enforcement from those offering dashboards without controls.
1. Pull Request Risk Scoring
AI-generated code requires risk assessment at the PR level — not per-file static analysis. A governance platform must score each pull request as a composite: diff size, files touched, sensitivity of modified paths, dependency changes, and patterns specific to AI-generated output. Veracode's Spring 2026 assessment found Java AI-generated code achieves only a 29% security pass rate, and cross-site scripting protection falls to 15% across all languages. A risk score surfacing these patterns before merge gives reviewers a decision-ready signal, not a stack of raw findings. For a full breakdown of the vulnerability types AI-generated code introduces at scale, the concentration patterns are consistent and predictable — which makes them governable.
2. Real-Time Secrets and Credential Detection
Standard secrets scanners look for patterns in committed code. AI coding agents introduce a different problem: they generate plausible-looking configuration files, environment variable references, and example credentials that developers commit without review. JFrog found only 28% of organizations have secrets detection active — the lowest adoption rate of any named security control in their study. Any governance platform worth evaluating must intercept credential exposure before it reaches the repository, not after the merge.
3. MCP Server and Tool Inventory Governance
As AI coding agents increasingly operate through Model Context Protocol servers — file systems, external APIs, internal databases — the tool inventory itself becomes an attack surface. JFrog's data shows only 57% of organizations govern MCP usage through automated controls, with 18% having no active oversight at all. A governance platform must enforce a known-good tool inventory and surface deviations before they produce unauthorized output in pull requests.
4. Policy Enforcement at the Pipeline Gate
Governance policies that live in a wiki are not controls. AI code review policies without automated enforcement do not stop unauthorized merges. The platform must block pull requests that breach risk thresholds, require human sign-off on high-risk PRs, and alert on policy drift in real time. Enforcement is the difference between a governance program and a governance document.
5. Compliance-Ready Audit Logging
When a security incident traces back to AI-generated code, the first question from compliance is: what was the governance process? A platform must produce a timestamped, tamper-evident audit trail — which PR was flagged, what risk score it received, who approved it, and when. JFrog's State of the Union found 48% of organizations take a week or longer to produce this proof on demand. A platform that generates it automatically closes a gap that no manual documentation process can.
What re-entry.ai Does About This
re-entry.ai is a PR risk scoring platform built specifically for teams running AI coding agents. It assigns a composite risk score to every pull request — evaluating diff characteristics, sensitive file patterns, AI-agent-specific signals, and policy compliance — and surfaces that signal at the point of review, before merge. For engineering teams building a governed AI workflow, re-entry.ai provides the enforcement layer that standard security tooling was not designed to deliver. See how it works at re-entry.ai.
What to Do Now
Map your current coverage: identify which security controls operate before merge versus after, and where secrets detection, dependency scanning, and PR policy enforcement gaps exist.
Establish a baseline risk score on a sample of recent AI-assisted pull requests to quantify your current exposure before evaluating any platform.
Define policy thresholds: what risk score triggers a required human review? What triggers an automatic block at the CI gate?
Evaluate AI code governance platforms against the five capabilities above — not marketing claims. Require a live demo against your own repository data, not a sandbox.
Prioritize secrets detection first — it has the highest signal density, the fastest implementation path, and the most direct regulatory exposure in most compliance frameworks.
Two years of model releases have not moved the security needle on AI-generated code. Closing the governance gap requires infrastructure built for agentic development velocity, not another post-merge scan. Start with re-entry.ai.