Short, technical writing on proving a mainframe migration lost nothing — COBOL data fidelity, the failure modes nobody catches, and feeding legacy data to AI without poisoning it.
A script-driven copybook-to-JSON conversion can corrupt your records without throwing a single error. Here are the exact failure modes, why they hide, and what a parity proof actually has to check.
Read →Everyone's racing to put forty years of enterprise records into AI systems. Almost no one can prove the data those models see is faithful to the source. That gap is a liability — and it's measurable.
Read →A clause-by-clause walk through how packed decimals, overlaid unions, and variable-length arrays are stored, exactly how a naive converter mangles each, and how to verify it survived.
Read →The fastest migration path is also where fidelity quietly breaks. Three things a passing pipeline never proves — and why verification belongs at the conversion boundary, independent of the converter.
Read →Screenshots, row counts, and "the vendor said so" don't hold up. The four properties audit-grade evidence needs — and how a signed parity receipt maps to each.
Read →Lineage tells you where data came from; fidelity tells you whether it survived the trip. AI governance tracks the first and assumes the second. Here's the missing artifact.
Read →You don't need to audit the converter's internals if you can verify its boundary. Round-trip equivalence — decode, re-encode, compare bytes — catches errors you'd never think to look for.
Read →Independent verification and "the data never leaves our perimeter" aren't in tension. The engine runs in your VPC; only the receipt — hashes and verdicts, no record contents — leaves.
Read →Programs don't stall on the code rewrite — they stall when no one will sign off that the data migration preserved decades of undocumented logic. A signed parity proof removes the blocker.
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