AI code security in a regulated industry needs four things: control over where source code goes, a mode that works without sending code to a model, a durable audit trail of what the tool did, and findings that map to the controls your auditors use. Ask for each in writing and test it in a trial.
Why is this different for regulated teams?
An unregulated startup can try a code assistant on Friday. A bank, a health provider or a defense supplier has to explain, later, where code and data went, who approved the tool and what it changed. The questions are about evidence as much as accuracy.
NIST has a companion to its Secure Software Development Framework for generative AI and dual use foundation models, SP 800-218A, finalized in July 2024. It is aimed at producers of AI models, producers of AI systems that use them, and the acquirers of those systems. If you buy these tools, you are an acquirer, and it is worth reading before you write your criteria.
Buyer criteria, with how to test each
1. Where does source code go? Ask for a data flow diagram. Does code leave your network? To whom? Is it stored, and for how long? Test it: run a scan with network monitoring on and compare to the diagram.
2. Can it run without an LLM? Some environments cannot send code to any model. Ask whether the product has a mode with no model calls, whether findings are lower in that mode, and which features turn off.
3. Can you choose the model? Look for a bring your own model option, including on premises or local models, so your model risk team can approve a specific one.
4. What does it log? You need records of which scans ran, what they found and what an agent changed, with timestamps and an identity. Ask whether you can export them.
5. Does it reduce noise? A finding in a function your code never calls costs the same triage time as a real one. Ask whether reachability analysis exists, for which languages, and whether unknown cases are treated conservatively.
6. Can it change code safely? If it proposes fixes, ask whether a human approves before a merge, and whether changes arrive as pull requests that go through your normal review.
7. How does it map to your frameworks? Ask which controls findings and evidence map to, which are decided by a scan and which still need your evidence. Be wary of any vendor that says a tool makes you compliant.
8. What are the limits, in the vendor's own words? Ask for a list of things the product does not do. A confident answer is a good sign.
What Safeguard offers on these points
We can speak to verified features only. Safeguard supports Full AI, Zero AI, or bring your own model, so teams can choose how models are used. Its CLI has a local runner mode that pulls the repository on your machine, scans locally and pushes only encrypted results. Reachability analysis is part of the platform, and its documentation classes findings as reachable, conditionally reachable, unreachable or unknown, with unknown treated as potentially reachable. Safeguard's documentation says it does not hold a FedRAMP authorization, and we make no certification claims here.
What do these tools not do?
None of this replaces secure design, code review or testing by people. Static and reachability analysis can miss logic flaws. A model can be wrong. A tool that helps you meet a control does not satisfy the control for you.
A trial plan
- Choose one representative repository with known issues.
- Run the tool in its most restricted mode first.
- Compare findings with and without the model.
- Export the logs and read them as an auditor would.
- Ask your compliance team to review the data flow and sign it off.
Quick check
Does a regulated team have to avoid LLMs entirely?
Not necessarily. It depends on your regulator, your data and your model risk process. Prefer a tool with a no model mode and a choice of models, so policy can decide.
What is the first thing to ask a vendor?
Where does our source code go, and can you show it. Everything else builds on that answer.
Sources
- NIST SP 800-218A: SSDF Community Profile for generative AI
- NIST: Secure Software Development Framework
- Safeguard docs: reachability analysis
- Safeguard docs: compliance
Last reviewed: 18 October 2026.
