A risk leader at a financial institution told us on a call this year, plainly, that Copilot already gets his team about 80 percent of the way through a vendor security review. He was not wrong, and he was not looking for an argument. He was looking for an honest answer about the other 20 percent.
We hear a version of this on more calls every month, so here is the honest answer, including the parts that are genuinely good news for your team.
Quick answer: Yes, Copilot or ChatGPT can meaningfully help with vendor security reviews: summarizing a SOC 2, extracting exceptions, and drafting follow-up questions. What a general AI tool cannot do is review every vendor against your specific control framework consistently, keep an audit trail an examiner can walk, track findings to remediation, or roll vendor risk into your risk assessment. The 80 percent it covers is reading. The 20 percent it misses is the program.
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Credit where it is due, because pretending otherwise would cost us your trust for the rest of this post.
Summarizing a SOC 2. Drop a 90-page Type 2 report into Copilot and ask for the scope, the auditor, the period, the exceptions, and the complementary user entity controls, and you will get a decent first pass in a minute. That used to be an afternoon of reading. If your current process is a security person skimming for "no exceptions noted," Copilot is already an upgrade.
Drafting vendor questions. Ask it to write follow-up questions about an exception or a subservice organization and the draft will be serviceable. Editing beats a blank page.
Explaining unfamiliar terms. Bridge letters, carve-out versus inclusive method, complementary controls. It is a patient tutor for whoever on your team is newer to SOC reports.
If that is where your program is today, use the tool. We mean that. The problem is what happens next.
A real vendor security review is not "what does this SOC 2 say." It is "does this vendor meet the controls we require, and where is the evidence." That means mapping the report against your specific control set, the same one your own program runs on, and recording, control by control, whether it is in place, whether it was audited, and where in the report the evidence sits. A general AI tool has no idea what your control framework is, and it will not apply one consistently across 40 vendors and twelve months. You will get 40 differently shaped summaries, which is 40 judgment calls with no common yardstick.
Two examples of what the yardstick catches that a summary misses. Complementary user entity controls: some regulations expect you to pull the user entity controls out of the SOC report, document them, and show what you are doing about each one, and a summarizer will mention they exist without doing any of that work. And AI-specific depth: if a vendor's model touches or trains on member data, the review needs answers about bias and limitations, whether anyone tested that a crafted prompt could expose other members' data, and what the accuracy metrics actually are. Those questions do not appear unless the review framework demands them.
Ask yourself the exam question now, because your examiner will: how was this vendor reviewed, by whom, against what criteria, and where are the results? "An analyst pasted the SOC 2 into Copilot and read the summary" is not an answer that survives that conversation. There is no record of what the model was asked, what it returned, what a human verified, or what changed since last year's review. The work happened, and there is no evidence it happened. In a regulated institution, work without evidence rounds to zero.
Suppose Copilot surfaces a real exception, say a gap in the vendor's access reviews. Then what? A finding needs an owner, a risk decision, a follow-up question to the vendor, and a place in your reporting. A chat window has none of that. What we see in practice is that findings from ad hoc AI reviews live in someone's notes and quietly expire.
Even a perfect Copilot review is a point-in-time read of a document that was itself a point-in-time audit. The question that matters between reviews is whether the vendor's risk profile changed: a new AI feature turned on, a new fourth party behind the product, a shift in what data flows where. A chat session ends when you close it. A program watches for those signals and triggers a re-review when the use case changes, which is where much of the real vendor risk now lives.
SOC 2 reports are distributed under NDA. Before your team pastes them into any AI tool, someone needs to confirm where that data goes, whether it trains anything, and whether your agreement with the vendor even permits it. Enterprise Copilot tenants handle this better than free chatbots, but "better" still needs a written answer in your file. This is exactly the kind of question we cover in how to assess a vendor's AI features, pointed back at your own tooling.
The output of a vendor review should not be a summary. It should be a risk position: this vendor holds this data, these controls are weak, and the exposure is worth roughly this many dollars against our tolerance. That number is what lets a board compare vendor risk to every other risk on the books and fund the right fixes. A document summarizer, however good, stops at words.
Here is the part that usually lands: the right answer is not "AI or no AI." It is AI, pointed at your controls, inside a system of record. In Rivial Software, the AI-powered vendor review reads the SOC 2 the way your best analyst would, but against your own control framework, and records for every control whether it is in place, whether it was audited, and a note citing where in the report it found the evidence. A human does the verification pass, which we recommend every time. Findings become tracked items with owners. The review is timestamped and repeatable, so next year's review compares against this year's. And the vendor's risk rolls up into your risk assessment in dollars, where the board can actually see it.
The review that took days takes minutes, and this time there is an audit trail.
Whatever tooling you use, the fastest upgrade is deciding what you review against. Our free Vendor Cybersecurity Template gives you the control questions and structure for a consistent review, so every vendor gets measured with the same yardstick, whether a human or an AI does the reading. It is an instant download, no sales call required. If you later want the AI to do the heavy lifting inside a real program, you will already have the standard it should review against.
Here are the key takeaways from this blog:
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