Local-first AI notetakers vs. cloud-based: Enterprise security & data control

TL;DR: Where meeting audio ends up after a call is a compliance question most teams answer late. Granola captures device audio, transcribes in real time, then deletes the audio automatically, so the data that persists is text. The result: SOC 2 Type 2 certification completed in three months and a research repository your security team can scope precisely.

Most teams spend significant time choosing AI notetaking tools based on features and price, and far less time asking where meeting audio actually ends up after the call ends. That gap in due diligence creates compliance exposure that typically surfaces only when Legal or Security raises the question first.

Understanding the architectural difference between cloud-based and device-level AI notetaking determines whether your meeting data is a controlled, auditable asset or a liability scattered across third-party servers that your team never explicitly approved.

Defining local-first vs. cloud AI notetakers

How local-first AI notetaking works

A local-first or device-level AI notetaker typically captures audio directly from your computer's system audio layer. Transcription runs as audio arrives, converting speech to text, and the raw audio file is usually deleted immediately after the transcript is generated. In this architecture, no audio file is written to a vendor's cloud storage, and no recording sits on a third-party server awaiting a retention policy trigger.

Granola's architecture works this way. It captures and transcribes device audio in real time, then deletes the raw audio after transcription is complete. The resulting transcript and your enhanced notes are retained. The audio is not.

Granola runs on your laptop or phone and uses your device's audio to generate notes, so there is nothing to install into the call itself and capture does not depend on the meeting platform. Let the other participants know you are using Granola to transcribe the call.

Cloud AI notetaker setup

Many cloud-based AI notetakers join your meeting as a visible bot participant. These tools typically capture audio continuously, send it to vendor servers for automatic speech recognition, separate speakers through diarization, run NLP models to extract summaries, and may store data on cloud infrastructure. Speaker diarization typically analyzes voice patterns to distinguish participants. Voice-derived data from such processes may constitute biometric identifiers under certain state privacy laws, potentially adding a layer of regulatory complexity beyond standard data retention.

Protecting participant data: Key choices

Data flow mechanics: Local vs. cloud

On-device data processing flow

Granola's data flow runs in four stages. First, it captures device audio from your system audio layer. Second, it transcribes that audio in real time. Third, it deletes the audio file automatically. Fourth, it processes the text transcript through AI enhancement to enrich your notes.

The AI-enhanced notes feature takes any rough notes you jotted during the meeting and uses transcript context to expand them. Your notes stay in black. AI additions are visually distinguished. You decide what stays. This human-in-the-loop process means AI is enhancing your judgment, not replacing it.

Cloud data processing: Security risks

Where audio files are stored on vendor infrastructure, they remain in scope for retention schedules, access reviews and breach assessments for as long as they are kept. Granola transcribes audio and then deletes the audio file, so the data that persists for review is text.

Where is your research data truly stored?

With device-level capture and zero audio retention, the only data that persists is text. Your transcript and enhanced notes are stored separately, not alongside a full audio recording. This distinction matters for enterprise data residency requirements and for the scope of what is auditable in a compliance review. You can review Granola's security, privacy, and data FAQ for specifics on storage, encryption, and subprocessor arrangements.

Protecting enterprise data & participant trust

Sensitive data exposure points

Research interviews routinely surface competitive intelligence, unreleased product feedback, personal health context in healthtech research, and candid stakeholder opinions. Retention, access and deletion policies for that material are what your security team will want documented, and Granola's Enterprise plan gives administrators configurable org-wide retention periods for transcripts.

Where audio is routed through a vendor's infrastructure, subprocessor arrangements and access controls form part of what your security team reviews. Granola's security, privacy and data documentation sets out its subprocessor arrangements, encryption and retention controls for that review.

Vendor AI training data risks

Most major AI API providers contractually exclude enterprise API traffic from model training. Standard enterprise API agreements state that data sent via the API is not used to train or improve models unless you explicitly opt in. This has become a standard practice for commercial products and enterprise deployments.

The risk is not always at the API level. It surfaces at the vendor product level, where consumer-grade or free-tier accounts may carry different terms. Granola's Enterprise plan opts your entire organization out of model training by default, contractually preventing AI providers from using your meeting transcripts to improve their models.

Compliance requirements: SOC 2, GDPR, and HIPAA

Expediting SOC 2 for AI tools

When your security team evaluates a new AI tool, their first question is usually what data the vendor retains and what that means for your SOC 2 scope. SOC 2 Type 2 audits require documenting controls around every category of sensitive data you retain, and depending on organizational complexity and the auditor, the process typically spans 12-18 months. Data minimization shortens that window: when you reduce the volume of sensitive data your systems retain, there is less to audit, fewer controls to demonstrate, and a smaller surface area for findings. In practice, a tool that retains no audio means audio-related controls drop off the audit entirely, which is a concrete answer to give your security team before they ask.

Granola's zero-audio-retention model is why the company completed SOC 2 Type 2 certification in three months. Less sensitive data to audit means fewer controls to demonstrate, fewer findings to remediate, and a faster path to the certification your security team requires before approving a new tool. Granola is SOC 2 Type 2 certified and GDPR compliant.

GDPR data pacts for AI notetakers

Security teams reviewing AI tools for research use will ask about GDPR data minimization. The question they're really asking is: does this tool collect more personal data than the research workflow requires?

GDPR requires organizations to collect only the personal data they actually need and to retain it for the shortest time necessary. In practice, that

means your security reviewer will want to know what the tool stores, where, and for how long before they approve it. Tools that store full audio recordings of research participants may create data retention obligations under GDPR. A tool that transcribes and deletes audio immediately collects only text, which can be narrower in scope and easier to manage under storage limitation requirements. Granola's Enterprise plan includes org-wide auto-deletion periods, giving administrators direct control over transcript retention to help address GDPR storage limitation requirements. That means when your security team asks "what's the retention policy," you have a specific, configurable answer rather than a vendor's blanket promise.

Securing HIPAA research data

Granola does not currently hold a HIPAA Business Associate Agreement. You can review Granola's current HIPAA status directly.

Why local-first protects your research data

Protecting confidential research data

Research interviews turn on details that are easy to lose: the offhand comment about a competitor, the hesitation before answering a pricing question, the admission that a workflow "sort of works but not really." Granola generates notes from your device's audio so you can stay in the conversation and follow up on those moments rather than writing them down, and you should tell participants you are using Granola to transcribe the call.

Other organizations weigh the same considerations beyond research contexts. Daversa Partners adopted Granola across 136 of 150 employees, with president Laura Kinder citing the firm's requirements around how data from confidential searches is stored and accessed.

Whether the conversation is a CEO search or a product discovery interview, the value is the same: a reliable record afterwards, so you can give the conversation your full attention while it is happening. Tell participants you are using Granola to transcribe the call.

The SOC 2 framework covers five Trust Services Criteria: security (always required), availability, processing integrity, confidentiality, and privacy. A vendor that stores audio files may create a larger compliance surface than one that retains only text. For security teams evaluating a new AI tool, the question is not just "is it SOC 2 certified?" but "what data does that certification actually cover?" Granola's certification covers a zero-audio-retention architecture, which means the audit scope is text-based data with tightly scoped access controls, not a growing archive of audio.

Safeguarding interview data privacy

Research teams running discovery interviews for product roadmaps need a repository that persists beyond individual tenures, surfaces patterns across dozens of sessions, and gives stakeholders searchable access to underlying evidence. Granola's folder-level query capability lets you ask "What are the top UX friction points our enterprise customers mentioned this quarter?" and get citations from specific conversations, not just keyword hits.

"Granola nails exactly what I need: clean, reliable meeting transcripts and smart follow-up summaries without any fluff. I use it for nearly every call to stay focused on the conversation instead of scribbling notes." - Verified user on G2

Informing your AI notetaker choice

Do local-first tools offer enterprise features?

The objection that "secure tools lack collaboration features" does not hold for Granola's current product. Business and Enterprise plans add cross-folder queries with source-linked citations, Slack and Notion integrations, CRM sync with HubSpot, Affinity, and Attio, and Zapier connectivity. The Granola pricing overview details what each tier includes. Model Context Protocol (MCP) support for connecting meeting notes to compatible AI tools and coding assistants is available on all plans, including the Basic plan.

"With Granola I don't have to worry anymore about taking meeting notes, I can just write down things I really care about and let Granola take care of the rest. Love that I can easily share my notes with my colleagues as well, and that we can all chat with the meeting transcript so everyone can see the full context of the meeting, even if they weren't there." - Jess M. on G2

Feature constraints to weigh honestly

The device-level trade-off is audio playback. Granola deletes raw audio after transcription, so you cannot replay the recording. You have a full transcript, enhanced notes, and chat-based query capability across both. Teams requiring audio for legal verification or tone analysis should weigh this limitation before committing.

Enterprise cost of local-first AI

Granola's Business plan is $14 per user per month. Enterprise starts at $35 per user per month and adds SSO, org-wide model training opt-out, priority support, and usage analytics. Running 10 research interviews per week means roughly 40 sessions per month. For Business plan pricing, that is $0.35 per captured session. For teams where a single compliance finding costs more than a year of tooling, the trade-off is worth modeling.

Run your next customer interview with device-level capture. Download Granola for Mac, iOS, or Windows, connect your calendar, and see how device-level capture works. Share the security documentation with your compliance team to evaluate Granola's SOC 2 Type 2 architecture.

FAQs

What is a local-first AI notetaker?

A local-first AI notetaker captures and processes audio at the device level, transcribing it on the fly rather than uploading the recording. Notes and transcripts are then stored in the vendor's infrastructure, so it is worth checking each vendor's published retention, encryption and subprocessor details.

How does Granola capture audio?

Granola runs on your laptop or phone and uses your device's audio to generate notes, so it works the same way across meeting platforms and in-person conversations. Let the other people on the call know you are using Granola to transcribe it.

Is Granola SOC 2 Type 2 certified?

Yes. Granola achieved SOC 2 Type 2 certification in July 2025, completing the audit in approximately three months due to its zero-audio-retention architecture.

Does Granola store my meeting recordings?

No. Granola transcribes audio in real time and deletes the audio file automatically after transcription. Only the text transcript and your enhanced notes are retained.

Key terms glossary

Device-level audio capture: Audio processing that occurs at the operating system layer on your local machine rather than via a cloud-based bot joining your meeting as a participant.

Zero-audio-retention: An architecture in which raw audio files are deleted immediately after transcription, leaving only text-based data in storage.

SOC 2 Type 2: A security certification from the AICPA that verifies an organization's controls over security, availability, processing integrity, confidentiality, and privacy across a defined audit period.

GDPR data minimization: A core GDPR principle requiring that personal data be limited to what is necessary for the stated processing purpose. In practice: collect only what you need and delete it when it is no longer needed.

Speaker diarization: The process of identifying which speaker said what in a multi-person recording. When voice samples are used as biometric identifiers under state privacy laws, diarization data may carry additional regulatory obligations.

Human-in-the-loop enhancement: A workflow in which a user jots rough notes during a meeting, and an AI model enhances them using transcript context, preserving the user's judgment about what matters rather than generating a fully automated summary.

Model training opt-out: A contractual or settings-based control that prevents a vendor or its AI subprocessors from using your data to train or fine-tune AI models.