Humata vs Adobe Acrobat AI Assistant for a 200-user enterprise - which is more secure?

Principal ETL engineer at a 350-person financial services firm. Our docs team is a similar size. We've been running POCs on both of these for internal research workflows.

**Core Comparison**

1. **Real Enterprise Pricing**
Humata pitches a custom enterprise quote, but our sales cycle ended at about $18/user/month for 200 seats. Adobe was cheaper, but only because it's an add-on to existing Acrobat Pro licenses. If you're not already paying for those, Adobe's true cost is $24.99/user/month minimum. You're really buying the whole Acrobat suite.

2. **Data Residency & Pipeline Control**
Adobe wins cleanly here for us. Our ETLA specifies all processing, including AI, stays in our designated AWS region. No shared pools. Humata's architecture was murkier; their enterprise FAQ says "data processed in US or EU," but couldn't commit to our single-region requirement for sensitive client data.

3. **Administrative Visibility**
Adobe's admin console is decades-old but complete: granular audit logs, policy assignment, and usage dashboards that fed directly into our SIEM. Humata's admin portal was basically a user list and a billing date. We had to request manual CSV exports for access logs, which is a non-starter.

4. **Where It Breaks (File Size & Types)**
Humata choked on technical PDFs over 100MB with vector diagrams. Adobe processed anything Acrobat can open, including scanned images, because it's just an extension of the same engine. For pure text-heavy research PDFs under 50MB, Humata was marginally faster.

**My Pick**

I'd go Adobe for a 200-user enterprise, but only if your use case is general internal document Q&A. If your team is a small group of analysts purely querying academic papers and you need raw speed, Humata might work, but you need to tell us your exact compliance framework and average file size.

That's a critical observation on admin visibility. It mirrors what we see in observability platforms. An admin portal that's just a user list is a red flag, because it means you can't audit *how* the tool is being used, only *who* has access.

You can't build proper guardrails or detect misuse without those granular logs. The fact Adobe's console fed your SIEM is a huge point in its favor for compliance and incident response.

Did Humata's team give any roadmap on when they'd bring their admin controls up to enterprise spec, or was it a fundamental architecture gap?

Thanks for breaking it down like that. The data architecture angle makes a lot of sense. When you say we need to scrutinize the ETLA for AI/ML data handling, is that something a typical procurement team would catch, or should an infra person be directly involved in that review? I'd hate to assume it's covered in a standard clause and be wrong.

The ETLA point is critical. A standard procurement team will miss the nuances of AI data handling clauses. They look for broad security certs, not processing pipeline specifics.

I've seen three incidents where a procurement-signed ETLA had generic "data processing" terms, but the vendor's AI feature used a separate subprocessor not listed in the annex. You need an infra or security engineer to map the actual data flow the feature uses against the document.

That's a reasonable starting point, but framing it as a "shared responsibility model" is a bit generous. In my experience, vendors love to lean on that phrase to offload the hard parts. The real question is where the line gets drawn in the ETLA for something like an AI feature. Is Adobe responsible when their model training pipeline ingests your confidential document metadata, or is that now your problem because you clicked "agree"? The devil isn't just in the details, it's in who gets blamed.

Precisely. The phrase has become a compliance fig leaf. I once reviewed an ETLA for a similar AI tool where the "shared responsibility" section defined their responsibility as "providing a secure environment" and ours as "ensuring data is not used in a non-compliant manner." This created a perfect loophole: if their model training ingested PII, it was *our* fault for not redacting it before upload, despite no technical means to do so pre-ingestion.

For the AI component, the line is drawn at the data pipeline's ingress and egress points. You need explicit clauses that state:
* No data processed by the AI assistant is used for model training or improvement without explicit, documented opt-in per use case.
* The exact subprocessors for the AI feature are listed, including any third-party model APIs.
* All data, including intermediate embeddings or prompts, is purged from their systems upon your deletion request or contract termination, not just at rest.

Without those, the shared model tilts entirely in the vendor's favor.

That's a really solid breakdown. The shared responsibility model you mentioned is something I've been trying to understand better when evaluating these tools. When you say to scrutinize the ETLA for AI/ML data handling, what specific clauses should a non-lawyer look for? Is it mostly about finding where they define "subprocessor" for the AI component?

Your point on Adobe's admin console is spot on. That old, battle-tested interface is a pain to use but it exposes everything. It's built for actual enterprise audits, not just seat management.

Humata's CSV export requirement for basic logs is a non-starter at scale. You can't automate compliance with manual processes. It suggests their backend wasn't designed with enterprise observability in mind from day one.

If their sales can't commit to a single region, that's usually a hard architecture limitation, not a policy choice. It means their AI processing likely runs on shared, multi-tenant inference endpoints they can't isolate.

You've hit the nail on the head with that loophole. I've seen the same thing, and the "no technical means to do so pre-ingestion" is the killer. Vendors design a black box process, then hold you accountable for its output.

Your third bullet on data purging is the one that gets ignored. In these AI pipelines, you need to verify it includes *all derived data*. I've had to add explicit language for vector embeddings and temporary processing artifacts. If the clause only says "customer data," their legal can argue the AI-generated structures aren't covered.

Exactly. And "customer data" definitions are often stuck in 2015 - they think of raw files, not the structured data their AI creates. If you don't explicitly list embeddings, feature vectors, and inference logs, you'll find they keep a shadow dataset for "service optimization" that you can't purge.

We had a similar fight over chat logs in another platform. Their legal team argued a conversation transcript wasn't "customer data" because it was generated by the system, not uploaded. Took six months to fix the contract.

Adobe's legal boilerplate is thick, but it's predictable. A newer player's terms will have more creative gaps.

This exact point about derived data is why our security team insists on a data lineage diagram in the ETLA appendix for any AI feature. If the clause says "customer data" but the diagram doesn't show the flow for embeddings, you have a mismatch you can point to during negotiation.

Adobe's predictability means you can usually get them to add "including all intermediate representations and system-generated artifacts" to their definition. With a newer vendor like Humata, you're often starting from scratch, and they may not even have the internal taxonomy to define those data types yet. The six-month contract fix you mention is the real cost there.

The data lineage diagram point user621 makes is the logical extension of this. Without that visual mapping, the "all derived data" clause is open to interpretation during an audit. You can argue embeddings are included, but they can point to the diagram and say the flow shows raw text to output, with no intermediate step defined.

I'd push for the contract to reference the diagram explicitly, making the appendix a binding exhibit. That closes the loophole where legal definitions and technical architecture can diverge.

Absolutely agree on focusing on the ETLA for AI handling. The bit about "shared pool" vs dedicated tenant is the real crux of it. I've seen an enterprise Adobe deal where the AI features for document search were on a shared inference backend, despite the core storage being tenant-isolated. The ETLA was vague until we pushed for an amendment.

That forced us to map where the data actually *thought* - from our tenant storage, out to their AI pool, and back. Without that clarity in writing, you're trusting a dashboard toggle that might just be a UI layer over a multi-tenant pipeline.

The dashboard toggle vs backend reality is the key check. We audited a vendor where the toggle only governed *storage* location, not compute. Data still routed through a shared inference cluster in us-east-1.

Demand the data flow exhibit. If they can't provide it, assume multi-tenant.