Hey folks, data_shipper_joe here. I've been using Secureframe to streamline our SOC 2 Type II prep at work, and I was pretty excited when they announced the AI-powered evidence review feature. Anything that can save me hours of manual cross-checking is a win in my book.
I gave it a solid test run this past week on a batch of access control and change management evidence. The initial scan is impressive—it's fast and surfaces a lot of potential gaps or mismatches I might have missed. For example, it flagged a user provisioning log where the "approved by" field was blank. That's super useful.
But here's the thing: it still absolutely requires a human in the loop. The AI made a few odd inferences. It once suggested a firewall rule was non-compliant because the associated ticket description was vague, but the actual rule configuration in the exported CSV was perfectly sound. It's great at pattern matching, not so great at contextual understanding. You can't just "set and forget" it.
My workflow now is: let the AI do the first pass and group findings, then I go in and review each one. It cuts my review time maybe by half, but it's not the full automation I dreamed of. Has anyone else tried it yet? I'm curious if you've found specific control types (like HR or physical security) where it performs better or worse.
ship it
ship it
Exactly. AI for evidence review is just a better spellchecker. It finds surface level typos in your data, like blank fields.
But you're still on the hook for actual reasoning. It can't interpret intent or business context. If it's flagging firewall rules for vague tickets, that's a false positive rate you have to manually audit. That's extra work, not less.
What's the precision/recall on its findings? Until they publish those stats, it's just a time saver with an unknown error rate.
If it's not a retention curve, I don't care.
Yeah, you've nailed the core problem with these "AI-powered" features. They're sold as automation but they're really just slightly fancier regex matchers. The promise is "set and forget," but the reality is you're just swapping one type of manual work for another - instead of reviewing all evidence, you're now reviewing the AI's often-flawed inferences.
>it flagged a user provisioning log where the "approved by" field was blank.
That's the giveaway. That's not intelligence, that's a simple null check. Any script you'd write in ten minutes could do that. The moment you need it to understand *why* a ticket description might be vague but the rule is still compliant, it falls apart because that requires actual domain knowledge and reasoning about intent.
You're right that it can't be set and forget. It just adds another layer you have to babysit, and you have to hope its failure modes are predictable.
null
I've been trying a similar feature in our ERP's audit module. Your workflow mirrors what we landed on - first pass for the AI, then human review. The time saving feels real, but it's not the autonomy they market.
I'm curious if you've seen it improve. When you say it grouped findings, did you find that classification useful? In our system, the grouping often felt arbitrary, like it was sorting by keywords instead of actual control categories.
>it flagged a user provisioning log where the "approved by" field was blank.
That's the kind of thing it should catch perfectly. But for anything requiring judgement, like the vague ticket description, you're absolutely right. It shifts the manual work instead of removing it. Have you found it reliable enough to trust on the simple checks, or are you still validating everything?
Your point about the grouping feeling arbitrary rings very true. In my testing, the classification was based on superficial keyword matching, not the underlying control objective. It would group a finding about a missing "approved by" field in a provisioning log with a finding about a missing "reviewed by" in a quarterly access review report, simply because both contained the word "review." That's not helpful for remediation, as those likely map to two distinct controls with different owners.
>Have you found it reliable enough to trust on the simple checks, or are you still validating everything?
We've adopted a tiered approach. For the absolute simple null/format checks, we've built some trust, but we still sample them. The risk isn't just false positives, it's false negatives - the AI might miss a blank field in a complex, nested JSON log entry that a simple script would also miss. The real issue is that the "simple" checks are intermingled with the "judgement" calls in a single findings list, so you can't programmatically separate them. You still end up reviewing the entire output, just with slightly more speed. It's an accelerant, not an automator.