Only 29% of organizations are prepared to secure agentic AI deployments — yet most have already added AI coding agents to active development pipelines. The procurement decisions that authorized those tools almost never included a structured security review. AI coding agents are typically evaluated the same way a linter or a test framework would be: on output quality and developer experience. That mismatch is where security debt accumulates before the first pull request is merged.
Why Vendor Selection Is a Security Decision
A 2026 paper from Cornell University's arXiv — "Rethinking Software Engineering for Agentic AI Systems" — makes the problem precise: autonomous coding agents are stateful, tool-connected systems that read files, write code, call external APIs, and interact with package registries. Every one of those capabilities is a potential data channel. Traditional SaaS procurement questionnaires — focused on uptime, SOC 2 attestation, and data region — do not surface the questions that matter for this class of tool.
The data confirms the exposure risk. A 2026 AI due diligence assessment found that 65% of enterprises report concern about data leakage when using AI coding assistants, and that 97% of organizations that suffered an AI-related breach lacked adequate AI access controls. Those two numbers describe the same failure from opposite angles: security requirements are not defined at vendor selection, so they cannot be enforced after deployment.
Supply chain risk compounds the stakes. The 2026 ReversingLabs Software Supply Chain Security Report documented a 451% surge in malicious npm packages in 2025, with specific targeting of developer tooling and AI-assisted pipelines. An AI coding agent that integrates with unvetted package registries can introduce supply chain compromise at machine speed. Cloud Security Alliance research found that 45% of AI-generated code samples introduce OWASP Top 10 vulnerabilities — a rate that compounds when the agent is also selecting dependencies without structured human review.
Seven Questions Before You Authorize a New AI Coding Agent
These belong in your vendor questionnaire — not your post-deployment checklist. Teams that have already built an AI code governance platform evaluation framework will recognize these as the security controls tier of that assessment.
Does the vendor use submitted code or prompt data to train or fine-tune models? What is the default setting, and is the opt-out contractually enforceable rather than a UI toggle?
What is the data retention period for code processed through the tool? Can you verify deletion independently, or only through vendor attestation?
How are secrets, tokens, and credentials handled if they appear in code context? Are they redacted before transmission, or are they processed and potentially logged?
What audit logs does the tool produce for agent actions — file reads, API calls, dependency resolution? Can those logs be exported to a SIEM in a parseable, timestamped format?
What external integrations — package registries, external APIs, third-party tool connections — does the agent support, and can each be restricted or disabled at the organization level?
Does the tool enforce SSO, SAML, and role-based access control for team and project-level administration, or is access managed per developer?
What is the vendor's coordinated vulnerability disclosure policy, and what is the committed patch window for critical security issues?
Questions 1–3 address data handling scenarios most likely to produce a reportable incident under GDPR or equivalent data protection frameworks. Questions 4–5 map to the audit trail requirements that surface in a SOC 2 or EU AI Act review. Questions 6–7 cover the access control foundations that were absent in 97% of organizations that suffered an AI-related breach. Document vendor answers in writing — verbal commitments without contract terms are not enforceable. Once a tool is approved, enforce your acceptable use policy for AI coding agents and track adoption against your AI code governance maturity baseline — vendor risk is typically the first control dimension that slips when teams scale AI tool usage quickly.
What re-entry.ai does about this: once an AI coding agent is deployed, governance enforcement moves to the pull request layer. Re-entry.ai scores every AI-generated pull request against a configurable policy set covering secret detection, dependency risk, data exposure patterns, and code attribution — providing the runtime enforcement that vendor documentation promises but cannot deliver on its own.
Start with questions 1 and 3 and run them against every AI coding agent already approved in your org — not only new candidates. Most approval decisions were made before the current threat landscape was well-documented. See what pull request-level enforcement looks like at re-entry.ai.