Anomaly Detection: The AI Security Guard
Definition: The process of identifying outliers or unusual patterns in datasets that deviate significantly from normal behaviour.
Coding Use Case: Finding Bugs
In Vibe Coding, you can use the AI as an Anomaly Detector for your code.
- Prompt: “Scan this file. Point out any logic that looks inconsistent with the rest of the project.”
- Result: “Line 45 uses
varbut the rest of the file usesconst.” (Stylistic Anomaly) - Result: “Line 90 checks for
nullbut the type definition says it’s never null.” (Logical Anomaly)
Data Use Case: Cleaning Your Inputs
If you are building an app, use AI to detect anomalies in user input.
- Prompt: “Write a function that flags any user bio that looks like spam or AI slop.”
The “Vibe Check”
Anomaly detection is essentially a “Vibe Check.” It asks: “Does this belong?”
- Outlier Analysis: When debugging, focus on the “outliers”—the one API call that takes 10x longer, or the one component that re-renders 50 times.
- AI-Assisted Logging: Ask the AI to write a script that “detects anomalies in my server logs.” It will write a regex or a statistical check (like Z-score) to find the weird stuff.
Expert Strategy
Don’t just fix errors; look for anomalies. An error is a crash. An anomaly is a future crash waiting to happen. Use AI to scan your “working” code for these ticking time bombs.