How AI-Enriched Findings Reduce Developer Fatigue

The promise of AI in application security is better findings. The reality, when applied incorrectly, is more findings with less clarity.

At SecBez, we use AI as an enrichment layer that runs after deterministic detection. This distinction matters.

Detection vs enrichment

Detection answers: does this code contain a vulnerability?

Enrichment answers: what does this vulnerability mean for this team, and what should they do about it?

When AI handles detection directly, results become non-deterministic. The same code can produce different findings on consecutive runs. That breaks trust in CI pipelines where reproducibility is a requirement.

What AI enrichment looks like in practice

After a deterministic detector flags a finding, AI enrichment adds:

• Plain-language explanation — what the vulnerability is, written for the developer who introduced it, not for a security specialist.
• Contextual severity — is this an internal admin tool or a public-facing API? Context changes priority.
• Remediation guidance — specific code-level suggestions, not generic OWASP links.

Finding: SQL injection in user search endpoint
Detector: injection-sniper (deterministic)

AI Enrichment:
  Explanation: User-supplied input is concatenated directly
               into a SQL query without parameterization.
  Context:     This endpoint is publicly accessible.
  Remediation: Use parameterized queries via your ORM's
               where clause instead of string interpolation.

Why this ordering matters

Deterministic-first means:

1. Stable CI behavior. Same code, same findings, every run.
2. Auditable decisions. Every gate pass or fail traces to a rule, not a model prediction.
3. AI adds value without adding risk. Enrichment improves developer experience without affecting policy decisions.

If your security tooling uses AI, ask where in the pipeline it runs. Detection and enrichment are different jobs with different reliability requirements.