AI Coding Tool Procurement Checklist for Enterprise Security Teams in 2026

Why This Checklist Exists

Most AI coding tool evaluations start with the wrong question. Teams ask "which tool is fastest?" or "which one integrates with our IDE?" before they've answered the question that actually determines whether procurement succeeds: "can our security team approve this?"

In regulated industries, that question has real consequences. A financial services firm sending proprietary trading logic to a third-party model. A healthcare company whose engineers use an AI assistant that logs patient-adjacent code. A defense contractor whose source code transits infrastructure they can't inspect. These aren't hypothetical risks — they're the scenarios that kill AI coding tool rollouts after months of evaluation.

This checklist is built for security teams and engineering leaders who need to evaluate AI coding tools against real compliance requirements in 2026. It covers data handling, isolation architecture, attestation, audit capability, and vendor risk — the dimensions that determine whether a tool passes a security review, not just a demo.

Work through each section before you issue an RFP or run a proof of concept.

Step 1: Define Your Threat Model Before You Evaluate Any Tool

Before you open a vendor's security page, write down what you're actually protecting. This shapes every question that follows.

Answer these internally first:

- What classification levels does your codebase contain? (proprietary algorithms, PII-adjacent logic, regulated data schemas, classified systems) - Who is the adversary? (external breach, insider threat, vendor-side data leakage, model training reuse) - What's the blast radius if sensitive code is exposed? (regulatory penalty, IP loss, contract violation, national security implication) - Do your engineers work at a single sensitivity level, or do they move between sensitive and non-sensitive work throughout the day?

Your answers determine which checklist items are non-negotiable versus nice-to-have. A defense contractor and a mid-market SaaS company have very different threat models, even if both want AI coding assistance.

Step 2: Data Handling and Retention Requirements

This is where most tools fail regulated industry reviews. The questions sound simple. The answers rarely are.

What to ask every vendor

Retention:

- Are prompts, code inputs, and model outputs logged? If yes, for how long and where? - Is there a zero-retention mode? Is it the default or an opt-in? - After a session ends, is any data persisted — in logs, caches, or telemetry pipelines? - Can you get a contractual guarantee of zero retention, not just a policy statement?

Training:

- Is your code or prompts used to train or fine-tune shared foundation models? - Is opt-out from training the default, or does it require configuration? - Can the vendor provide technical proof that training exclusion is enforced, not just stated?

Data flow:

- Where does code transit? List every system it touches from editor to model response. - Who has access to that data at the vendor, and under what conditions? - What happens to data if the vendor is acquired, breached, or receives a government subpoena?

GitHub Copilot, Cursor, and Sourcegraph Cody all offer enterprise privacy controls — but controls are not the same as proof. A policy that says "we don't retain your code" is not the same as an architecture that makes retention technically impossible.

Step 3: Isolation and Execution Architecture

Policy promises are not isolation. Ask about the technical architecture, not the marketing copy.

Isolation checklist

Compute isolation:

- Does the tool use shared compute infrastructure for model execution? - Is tenant isolation enforced at the hardware level, or only at the application layer? - Can the vendor demonstrate that one tenant's workload cannot access another's memory or execution context?

Trusted Execution Environments (TEEs):

- Does the vendor support TEE-backed model execution (Intel TDX, AMD SEV, or equivalent)? - When a TEE is used, is memory encrypted at the hardware level and inaccessible to the host OS? - Is TEE usage the default for sensitive workloads, or does it require manual configuration?

Session isolation:

- Does each session run in its own ephemeral environment? - Are environments torn down after each session with no residual state? - Is lateral movement between sessions architecturally prevented, not just policy-restricted?

Tools built for the general developer market are typically not designed with hardware-level isolation — they're retrofitted with enterprise controls after the fact. If a vendor can't answer the TEE questions above with specifics, that tells you something important about where their architecture started.

Step 4: Attestation and Auditability

Your security team needs to verify what happened, not just trust that it did. Attestation is how you get from "we promise" to "here's the proof."

Attestation checklist

Cryptographic attestation:

- Does the vendor produce cryptographic attestation records for confidential sessions? - Can those records be exported into your existing security stack (SIEM, audit log system)? - Do the records prove that execution occurred inside a verified enclave — not just that the vendor claims it did?

Audit trail:

- Is there a full audit trail of agent actions — not just final outputs, but intermediate steps? - Can your security team inspect streaming agent thoughts, tool calls, and file-level diffs? - Are audit logs tamper-evident and exportable in a format your team can actually use?

Agent behavior visibility:

- If an AI agent takes an unexpected action — writes to an unintended file, calls an external API — is that visible in the audit trail? - Can you reconstruct exactly what the agent did during a session, step by step?

This matters especially in agentic workflows. When an AI agent isn't just suggesting code but executing tasks — committing files, opening PRs, searching codebases — you need to know exactly what it did and in what order. A black-box agent is not approvable in a regulated environment.

Step 5: Compliance Framework Alignment

Name the frameworks your organization must satisfy, then ask vendors to demonstrate alignment — not just claim it.

Framework-specific questions

SOC 2 Type II:

- Does the vendor have a current SOC 2 Type II report available under NDA? - Does the report cover the specific services you'll be using, not just corporate infrastructure? - Are the trust service criteria relevant to your use case (security, availability, confidentiality)?

FedRAMP (for federal and state government):

- Is the vendor FedRAMP authorized? At what impact level (Low, Moderate, High)? - If not yet authorized, are they actively pursuing it? What's the timeline? - Do they operate on FedRAMP-authorized cloud infrastructure (AWS GovCloud, Azure Government, etc.)?

HIPAA (for healthcare):

- Will the vendor sign a Business Associate Agreement (BAA)? - Does the BAA cover AI model execution, not just data storage? - Are PHI-adjacent code and prompts handled under HIPAA-compliant controls?

General:

- Can the vendor provide compliance evidence artifacts you can include in your own audit documentation? - Is compliance built into the architecture from the start, or added as a configuration layer?

The gap between "built for compliance" and "retrofitted for compliance" is significant. Tools designed for general developer productivity and then hardened for enterprise use often carry architectural gaps that surface during detailed security reviews.

Step 6: Data Sovereignty and Residency Controls

For organizations with strict data residency requirements — EU-based companies under GDPR, US agencies with data localization mandates, multinationals navigating conflicting jurisdictional rules — this section is non-negotiable.

Sovereignty checklist

- Can you specify which geographic regions or cloud environments execute your workloads? - Can you restrict execution to a single country or jurisdiction? - Is data residency enforced at the infrastructure level, or is it a configuration that could be overridden? - Can you define how long data persists and where? - Does the vendor support private pipeline options so sensitive traffic never transits shared infrastructure?

If a vendor can't answer these questions with specifics about their infrastructure topology, treat that as a gap. "We support data residency" is not the same as "you control which region executes your workloads and we can prove it."

Step 7: Vendor Risk and Contractual Protections

Security architecture matters. So does what happens when something goes wrong.

Vendor risk checklist

Contractual:

- Does the contract include data processing agreements that cover AI model execution? - Are breach notification timelines specified (72 hours is standard under GDPR)? - What are the vendor's liability limits in the event of a data exposure incident? - Does the contract explicitly prohibit the vendor from using your data for model training without consent?

Business continuity:

- What happens to your data if the vendor shuts down or is acquired? - Is there a data export or deletion guarantee with a defined timeline? - What's the vendor's uptime SLA, and does it cover the specific services you'll use?

Security posture:

- Does the vendor conduct third-party penetration testing? How often, and are results available? - What is their vulnerability disclosure policy? - Do they have a dedicated security team, or is security handled by engineering?

Step 8: Engineering Workflow Fit

Security approval is necessary but not sufficient. A tool that passes every security check but creates friction for engineers will see low adoption — which defeats the purpose of the procurement.

Workflow checklist

- Does the tool support the languages, frameworks, and IDEs your engineers actually use? - Can engineers switch between sensitivity levels within a single session, or do they need separate tools for different work types? - Does the tool support agentic workflows beyond autocomplete — planning, code review, research, architecture? - Can agents commit, push, and open PRs directly, or do engineers have to copy outputs into separate tools? - Is there a project knowledge base so agents retain context across sessions without leaking that context to other projects? - Can multiple engineers collaborate in the same environment in real time? - Can security teams inspect agent behavior without disrupting engineering workflows?

A tool your engineers will actually use and your security team can actually approve — those two requirements aren't in conflict if you choose the right architecture.

How to Score and Compare Vendors

Once you've worked through the checklist, use a scoring matrix to compare vendors objectively. Weight each category according to your threat model. For a defense contractor, isolation and attestation may each carry 25%. For a healthcare company, HIPAA alignment and data retention may dominate.

Data retention controls (20% weight) — GitHub Copilot: Partial. Cursor: Partial. Origin: Full (infrastructure-enforced zero retention).

Hardware-level isolation / TEE (20% weight) — GitHub Copilot: No. Cursor: No. Origin: Yes (Intel TDX).

Cryptographic attestation (15% weight) — GitHub Copilot: No. Cursor: No. Origin: Yes (per-session exportable records).

Audit trail completeness (15% weight) — GitHub Copilot: Limited. Cursor: Limited. Origin: Full (agent actions, tool calls, diffs).

Compliance framework coverage (15% weight) — GitHub Copilot: Partial. Cursor: Partial. Origin: Built for regulated environments.

Data sovereignty controls (10% weight) — GitHub Copilot: Limited. Cursor: Limited. Origin: Full.

Engineering workflow fit (5% weight) — GitHub Copilot: High. Cursor: High. Origin: High (agentic workflows inside secure environment).

Vendors that score well on the security dimensions — isolation, attestation, retention, sovereignty — are typically purpose-built for regulated environments. Tools designed for general developer productivity tend to score well on workflow fit but leave gaps in the security columns.

Origin is built specifically for this scoring profile: TEE-backed execution via Origin Gateway, cryptographic attestation, zero data retention by architecture, and full audit trails — combined with agentic workflows that keep engineers productive without pushing them out of the secure environment.

FAQs

What's the most common reason AI coding tool procurement fails in regulated industries?

Data retention and training reuse. Security reviews surface that the vendor's enterprise tier still logs prompts or retains outputs for some period, or that training exclusion is opt-in rather than default. The fix is to require zero-retention architecture as a baseline condition, not an optional feature.

Is a SOC 2 Type II report sufficient to approve an AI coding tool for regulated use?

Not on its own. SOC 2 covers the vendor's organizational controls, but it doesn't prove that your code is isolated at the hardware level or that model execution is cryptographically verified. Treat SOC 2 as a floor, not a ceiling. For sensitive workloads, you need attestation and isolation evidence that goes well beyond what SOC 2 provides.

What's the difference between a vendor privacy policy and zero-retention architecture?

A privacy policy is a legal document describing what a vendor promises to do. Zero-retention architecture is a technical design where retention isn't possible — sensitive inputs and outputs are never written to persistent storage, and enclaves are torn down after each session. The architecture makes the promise technically enforceable rather than contractually asserted.

Do AI coding tools need FedRAMP authorization for federal government use?

For federal agencies, FedRAMP authorization is generally required for cloud services that process federal data. The impact level (Low, Moderate, High) depends on the sensitivity of the data involved. State government requirements vary. If you're operating in a federal context, prioritize vendors who are FedRAMP authorized or actively pursuing authorization on a credible timeline.

How do Trusted Execution Environments (TEEs) differ from standard cloud isolation?

Standard cloud isolation separates tenants at the software or hypervisor layer, which means the cloud provider's infrastructure can theoretically access workload memory. TEEs use hardware-level memory encryption so that even the host OS and cloud provider cannot read the contents of the execution environment. That's the difference between isolation-by-policy and isolation-by-hardware.

Can AI coding tools with agentic capabilities be approved for use on classified or highly sensitive codebases?

It depends on the architecture. Agentic tools that execute multi-step tasks — committing code, searching files, opening PRs — require full audit trails of every action, not just final outputs. Without step-by-step visibility into agent behavior, security teams can't verify what the agent accessed or modified. Tools that provide streaming agent thoughts, tool call logs, and line-by-line diffs are far more likely to pass review than those that only surface final results.

How should we handle engineers who work on both sensitive and non-sensitive code in the same day?

Look for tools that support selectable security levels per session or per request — routing sensitive work through confidential compute (TEEs) via Origin Gateway and standard work through regular LLMs. This avoids forcing engineers to juggle separate tools for different sensitivity levels, which creates workflow friction and usually ends with engineers defaulting to the less secure option out of convenience.

What to Do Next

Run this checklist before you issue an RFP, not after. Vendors who can answer the isolation, attestation, and retention questions with specifics are the ones worth spending evaluation time on. Vendors who respond with policy documents and marketing language are telling you something important about their architecture.

If your team is evaluating AI coding tools for a regulated environment and needs cryptographic proof of isolation rather than policy promises, learn more at orgn.com.