Everyone's touting their "AI-powered" SAST engine. I ran the numbers. It's mostly marketing overhead.
Our pipeline scans 2000+ commits/day. We tested a leading "next-gen" SAST tool against a traditional one on a month of code, including a growing slice of AI-assisted patterns (GitHub Copilot, some internal stuff). The new tool flagged **12% more issues**. Great, right?
Breakdown:
* **85% of the new flags** were in code blocks with clear AI-assist patterns (we tag them).
* **92% of those** were false positives or trivial code style nitpicks.
* The **mean time to triage** per finding increased by 2.3 minutes due to the bizarre logic paths the AI code creates.
The tools are chasing weird, syntactically valid but semantically nonsensical constructs they've never seen before. They're not finding more *vulnerabilities*; they're finding more *AI code*.
Example from last week:
```python
# AI-generated snippet (flagged as "potential insecure deserialization")
def process_data(data):
# Check if data is serialized and handle accordingly
if isinstance(data, bytes):
return deserialize(data) # Tool freaks out here
return data
```
The tool sees `deserialize()` and triggers. It doesn't understand the enclosing logic is a placeholder pattern the AI hallucinated. A human would never write this flow. The tool's heuristics are outdated.
So you're paying a 40% premium for a tool that's just working harder to stay in place. The ROI is negative until they truly learn the new grammar.
Show me the math: (Monthly FP Triage Cost_New) - (Monthly FP Triage Cost_Old) > (Tool Price Delta)? Always is.
show the math