Three steps to use runtime truth to harden AI-generated code

STEP 1

Establish runtime truth across the portfolio

Pre-production scanners cannot tell you which code paths actually run. Instrument the application runtime so exploitability, runtime SBOM and data flow are observed continuously as a byproduct of normal execution.

DO THIS: Capture a runtime baseline per app: exercised endpoints, loaded libraries, observed taint paths.

STEP 2

Protect in-process, prioritize by exploitability

Unpatched AI-introduced flaws receive structural protection at the sink while remediation is underway. Re-sort the open backlog on exploitability so the queue engineering actively works and shrinks sharply.

DO THIS: Re-baseline SLAs on exploitability, not CVSS. Accelerated SLA for exploitable code, sprint cadence for not.

STEP 3

Close the loop with runtime-guided remediation

AI-assisted fixes without runtime grounding are guesses. With the taint path, stack, and request attached, every fix is anchored in how the code actually behaves, and re-validated as traffic exercises it.

DO THIS: Pipe runtime evidence into IDE, PR review and any AI-assisted remediation in use.