Test explainability by asking the AI feature to justify three of its own outputs and checking each against the source. A usable answer cites specific evidence you can open, shows the steps from evidence to conclusion, and states its limits. A fluent paragraph with no traceable source is not an explanation.
Why explainability matters in security
Security decisions are audited, disputed, and revisited. If an AI feature marks a vulnerability as low priority and nobody can say why, the decision cannot be defended later. Explainability turns a model output from an opinion into something an engineer can verify in a minute.
The three step test
- Pick three outputs. One you expect to be right, one you expect to be wrong, one you are unsure about.
- Ask why. Use the product's own interface, not a side chat.
- Verify each reason. Open the cited file, function, advisory, or version, and confirm it says what the feature claims.
If the reason cites something that does not exist or does not say that, you have found an invented justification. Record it and ask the vendor how they detect it.
What a good explanation contains
| Element | Example |
|---|---|
| Evidence | A named package version, file path, or advisory |
| Chain | The steps from evidence to the verdict |
| Confidence | A statement of how sure the feature is |
| Limits | What it could not check |
An explanation that only restates the finding title is not enough.
Beware the confident invention
Language models can produce plausible but false statements. The OWASP Top 10 for LLM Applications 2025 includes misinformation as a named risk. A feature that always sounds certain deserves more checking, not less. Prefer tools that mark uncertain results as uncertain.
Keep explanations with the decision
Store the explanation next to the finding or ticket, with the date and tool version. If your team later reverses a decision, you can see what the tool said and what changed. This is also helpful in audits, where reviewers want to see how decisions were made.
Ask about human override
Can an engineer override the tool's decision and record why? Overrides are valuable data. A pattern of overrides on one type of finding tells you where the feature is weak.
Try it yourself
Click through it at your own pace, or open it full screen.
Open the reasoning behind a verdict and run the three step test against it.
Where Safeguard fits
Safeguard provides reachability analysis and vulnerability context, and a free scan at gold.safeguard.sh/scan lets you check outputs on your own project.
Quick check
Is a chat answer an explanation?
Only if it cites evidence you can verify.
What if the tool cannot explain?
Treat its output as a suggestion and require human review.
Sources
- OWASP Top 10 for LLM Applications 2025: https://genai.owasp.org/llm-top-10/
Last reviewed: 19 October 2026.
