Run both on the same code and sort every finding into three piles: found by both, found by the AI reviewer alone, found by static analysis alone. Review each pile by hand. The result usually shows they overlap less than expected, which is an argument for using them together.
Two different kinds of tool
Static analysis applies rules or data flow models. It is deterministic, fast, and good at known patterns in known languages. An AI reviewer reads code more like a person: it can notice a missing authorization check, odd business logic, or a comment that contradicts the code. It is less repeatable and may miss simple things a rule catches every time.
Comparing them on "who finds more" misses the point. The better question is what each finds that the other does not, and what each costs you in noise.
Method
- Pick one repository, or three, covering different languages. Freeze a commit.
- Run the static analyzer on the full codebase. Run the AI reviewer on the same code, either whole files or a set of recent pull requests, depending on how the tool works.
- Add your seeded cases so you have known answers.
- Export every finding with file, line, weakness class and severity.
- Merge findings that point at the same location and problem.
- Have an engineer label each unique finding: real, not real, or unclear. Ideally two people, blind to the source.
Read the three piles
| Pile | Typical content | What to do |
|---|---|---|
| Both found it | Clear, common flaws | Likely duplicate work, consider keeping only one in the gate |
| AI only | Logic and authorization issues, plus some invented problems | Keep as advisory, measure precision |
| Rules only | Pattern matches, many repeating | Keep as the hard gate where precision is good |
The AI-only pile is where you learn most. Count how many are real. If half are real and those are the serious ones, the tool earns a place beside your analyzer. If most are plausible but wrong, it is a noise source.
Also check the rules-only pile for things the AI missed that were real. A miss on a simple, well-known pattern tells you not to let the AI replace the rule-based check.
Cost comparison
Record, for each tool, run time, cost per run if metered, and the human minutes spent triaging. A tool that finds slightly more but costs three times the triage time may lose on total effort.
Where each fits
A common arrangement:
- Static analysis and dependency scanning as the blocking gates, because they are repeatable and explainable.
- AI review as comments that a human weighs, especially on changes to authentication, authorization and data handling.
- Periodic re-comparison, because both kinds of tool change.
See manual code review versus SAST for the human side of the same comparison.
Be careful with benchmarks
Published comparisons often use public vulnerable apps. Tools may have been tuned on them, or a model may have seen them. Your own data answers your own question.
Try it yourself
Open the code scan demo and look at how rule-based findings are presented with location and severity. Think about what an AI reviewer would add or leave out for the same code.
Click through it at your own pace, or open it full screen.
Quick check
Should the AI reviewer replace static analysis?
Not on evidence like this. Static analysis gives repeatable results that suit gates. Use the AI reviewer for what rules cannot express.
How do I label findings fairly?
Hide which tool produced each finding, and use two reviewers. Resolve disagreements in writing.
Safeguard includes SBOM and dependency scanning plus policies and gates, so deterministic checks can sit in the merge path. A free scan is at gold.safeguard.sh/scan.
Last reviewed: 19 October 2026.
