AIUC raised $40 million to audit AI agents against its AIUC-1 standard

TechCrunch reports that the Artificial Intelligence Underwriting Company, or AIUC, raised a $40 million Series A led by Ribbit Capital, with First Harmonic joining. The company was founded by Rune Kvist, an early Anthropic employee, and Rajiv Dattani, former chief operating officer of the evaluation lab METR. Total funding to date is $55 million, including a $15 million seed round.

What AIUC actually does

AIUC audits AI agents. It does not sell insurance in the ordinary sense, and it does not build the safety checks itself. It ran about 5,000 tests per audit, TechCrunch reports, checking how an agent behaves under jailbreak attempts, prompts designed to cause hallucinations, and data leak conditions. The output is a report of roughly 100 pages that names where the agent worked and where it failed.

The standard the tests measure against is called AIUC-1, which the founders modelled on SOC 2, the cybersecurity attestation many enterprise buyers already ask for before they will sign. The idea is that a buyer looking at three agents can compare three AIUC-1 reports the same way they compare three SOC 2 reports today.

Who is already using it

TechCrunch names four customers: Cursor, Lovable, Harvey, and ElevenLabs. Two of those, Cursor and Lovable, are coding tools that a lot of engineering teams already run inside their workflow. Harvey sells to law firms and ElevenLabs sells voice, so the audit standard is being asked for across quite different agent categories, not just coding.

Dattani, quoted in the piece, described the report as "Here's where it passes and where you can trust it. And here's where there's concerns." The framing is that an enterprise buyer sees a specific list of behaviours the agent handles well and a specific list it does not, before the buyer has to pay.

AI runs the tests, and a human approves the result

The tests themselves run with AI, and AI helps read the results, but TechCrunch reports that a human verifies the final audit. That matters, because the whole point of a third party audit is that a buyer trusts the sign-off, and a fully automated audit leaves the reader trusting a model's judgement of another model.

For a team about to release an agent to production, the practical consequence is that agent security is starting to look like the SOC 2 process from ten years ago. A prospective customer asked whether the vendor had one, the vendor either did or rushed to get one, and that rush ended up shaping what the vendor built. If AIUC-1 becomes popular with buyers, an agent vendor's list of required work gains a new item, and a $40 million company writes the questions for it.

Source

Source: TechCrunch, 15 September 2026.