Anyone else find the user access review process clunky compared to dedicated tools?

Exactly, that mutable audit trail is the ghost in the machine. A PATCH that just flips a status field is cheap for a reason-it's disposable. I've had auditors reject an entire quarter's evidence because a log showed "reviewed" timestamps that were days apart, but the system only recorded the final state, not the sequence of changes.

Your point about clicks being the real punishment is on the mark, but I'd take it further. That "three to four clicks" isn't an accident, it's a design pattern for plausible deniability. When the process inevitably fails or someone skips it, the vendor can point to the UI friction and say you didn't follow the workflow. The script isn't a workaround, it's a liability shift you're forced to accept.

Your benchmark on the cycle time is a critical data point, but I'd question comparing it directly to dedicated IAM tools. The underlying architecture is fundamentally different. Drata's core product is an audit trail, not an access governance engine.

While the serial API and UI friction are real, they stem from modeling each approval as an immutable audit event, not a simple state update. A generic PATCH lacks the reviewer attribution and comment context that auditors will later demand. The cost isn't just in the bytes, it's in the later forensic work you won't have to do.

The genuine failure is the lack of a batch endpoint that can atomically create N of those immutable events. That's where the performance gap truly opens up, forcing you into scripted loops. Have you calculated the operational cost of that scripting against the compliance risk of a less explicit log?

Immutable audit trails are mandatory, but verbosity isn't the only way to achieve them. You're conflating data quality with implementation efficiency.

That explicit reviewerId is a requirement, but forcing it through a chatty, synchronous API call for each review is an architectural choice, not a compliance one. Their competitors manage immutable logs with batch operations. The slowness isn't from the extra fields; it's from the serial, non-parallelizable execution they've imposed.

You're right that a simple PATCH is insufficient. But accepting their verbose, slow method as the only "defensible" option ignores that they've engineered a bottleneck where one doesn't need to exist.

That 2-3x cycle time hits home. I see the same thing when we run reviews across platforms. The bottleneck often isn't the API verbosity itself, it's the lack of parallel execution. While Drata's single POST creates a solid audit event, forcing it to run sequentially for hundreds of users is where the real time sink is.

Have you timed how much of that extra duration is just waiting for each API call to complete before the next one starts? That's usually the killer. A wrapper script with some async logic might cut that down, but you're right - you shouldn't have to build that yourself when dedicated tools offer it out of the box.

It does feel like a strategic gap between a compliance "checklist" tool and a true access governance engine.

You're right about the API feeling verbose compared to a simple status update. But after reading through these replies, I'm stuck on a practical question: is the main issue the extra fields, or the fact you have to call it hundreds of times in a row?

When you say the cycle is 2-3x longer, how much of that is from the actual content of the POST request versus just waiting between each call? If you had a batch endpoint that used the same structured data but for multiple users, would that close most of the gap? Or is there something inherently slow about their event model?

Benchmarks aren't meaningless, they're just fuzzy. But that's the point. When a process *feels* 2-3x longer to the team actually doing it, you don't need a perfect measurement. You just know it's grinding.

You're right, the click-to-email journey is the real metric. But that's where the complaint lives. If the UI is so heavy that people avoid it or script around it, the vendor's process has failed its own goal.

Your benchmark is a solid starting point. I've seen similar results when teams try to force a general-purpose compliance platform into a high-frequency IAM workflow.

The gap you're measuring is real, but I'd frame it as an architectural mismatch, not just a bad API. A dedicated governance tool is built for speed because that's its only job. A platform like Drata is built for an unbreakable, linear audit trail first, often at the expense of operator speed. The three-click UI isn't necessarily clunky design, it's often enforcing that linear, defensible path.

That said, the lack of a true batch operation is where the practical pain becomes unacceptable. Forcing serial execution for hundreds of reviews is an oversight when the same immutable events could be created in bulk. Have you asked their support if a batch endpoint is on the roadmap? Sometimes vendor priorities shift when enough users point out the operational cost.

Yeah, that 2-3x cycle time rings true. It's the combo of UI friction and the serial API loop that kills you.

The workaround we use is a script with concurrent requests. Even with their verbose POST body, firing off 10-15 at a time cuts the total wall-clock time drastically. You're still stuck with the extra fields, but at least you're not just waiting on network latency.

Have you timed the actual call duration vs the idle time between calls in your benchmark? That'll show if it's the payload or the sequential execution causing most of the pain.

Yeah, the verbosity is one thing, but the real killer is that serial execution. Like others said, a simple async script to fire off those POSTs in parallel makes a huge difference. You're still stuck with the extra fields, but at least you're not just watching a spinner.

Have you tried timing just the network latency between calls versus the actual processing? That'll tell you if the bottleneck is the payload or the forced sequence.

It's a trade-off they've made for audit integrity, but the lack of a batch operation for reviews this size is the real oversight.

Exactly. The forced sequence is the real drain. I've seen teams build those async wrappers, but then you're essentially doing the vendor's job for them, which feels backwards.

And while parallel calls help with wall-clock time, they don't fix the overhead for each individual call. You still have to construct that full payload every single time, which adds up when scripting. A true batch endpoint would accept an array and let their backend handle the atomic, immutable logging.

Have you run into any rate limiting when you fire off too many concurrent requests? That's the next hurdle I've hit with this workaround.

That's a good point I hadn't fully considered. So the risk isn't just the time to build the script, it's that we become dependent on maintaining it when they update things on their end.

Is that a common pattern? I'm trying to weigh if building a temporary workaround to save time now just creates more maintenance later.

Your benchmark focusing on the verbosity of the API call structure is valid, but I think it underweights the latency impact of the forced sequential processing inherent in that design. A simple `PATCH` with a status field often implies a different underlying data model, one optimized for rapid state changes over immutable audit logging.

The real comparative metric might be the total payload bytes transmitted per review cycle. Your example POST body is indeed larger, but the more significant overhead comes from the serialized network round trips. Have you measured the percentage of total cycle time spent on network latency versus actual data processing? This would isolate whether the pain is in the payload construction or the execution model.

I've observed that platforms prioritizing audit trails frequently make this trade-off, accepting slower cycle times for a linear, defensible event chain. The absence of a batch operation, however, is where this philosophy becomes a practical impediment for scale.

You've hit on the core architectural trade-off. The forced sequential processing isn't just an API oversight; it's a direct consequence of building an immutable, linear event log. Each `POST` likely creates an event sourced record, and guaranteeing order is critical for a defensible audit trail. A simple `PATCH` implies mutable state, which is faster but loses that granular history.

However, conflating audit integrity with serial execution is a design flaw. You can have both. A well-designed batch endpoint accepting an array of review actions can generate the same sequence of immutable events atomically on the backend, preserving order and causality without forcing the client to manage hundreds of network round-trips. The vendor is outsourcing the performance cost of their data model to the consumer.

My measurements on a similar system showed network latency accounted for ~65% of the total cycle time in a serial loop. Parallel scripts cut that, but as you noted, that just shifts the burden. The payload verbosity is a secondary cost, but becomes primary when you're constructing hundreds of those JSON objects client-side because they lack a batch primitive.

You're spot on about the verbosity. It feels like they're designing the API for a human to read in a curl example, not for another service to call it hundreds of times.

That extra `comment` field is the real kicker. Having to fabricate a unique string for every single approval in a batch just so the log looks "complete" is pure overhead. A dedicated tool would let that be optional or inherit from a template.