AI has worked its way into some of the most sensitive parts of everyday work in 2026. Meetings that once lived in notebooks or faded memories are now recorded, transcribed, summarized, and stored, often automatically, often by default, and often without teams fully stopping to think about what that means for privacy. That is why conversations about AI and privacy have become so loaded, especially when it comes to AI notetakers, which sit right at the intersection of convenience and risk.

I spend a lot of time writing about this space, and the pattern is always the same. Teams are not anti-AI. They are busy, stretched thin, and genuinely grateful for tools that remove admin from their day. At the same time, they are uneasy. They want to know where their conversations go, who can access them, how long they are kept, and whether those recordings are quietly being reused in ways they did not agree to. Those concerns are reasonable.

Here we will take a look at AI and privacy through a very specific lens, AI notetakers in 2026. Not abstract ethics and a hard sell. Just the real risks these tools introduce, how those risks show up in practice, and what teams should understand before they hit record and trust an AI system with their conversations.

Whether you currently use an AI notetaker, or are still thinking about how that factors into your daily working life, this will give you some solid takeaways to consider when choosing an AI notetaker, or switching to another supplier.

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TL;DR

AI notetakers save time, but they also turn live, informal conversations into permanent, searchable records that raise real privacy risks around consent, attribution, retention, model training, and secondary use.

Teams are right to question how recordings are stored, who can access them, how long they are kept, and whether data is reused beyond note creation.

Responsible adoption means checking lawful basis, minimisation, deletion rights, training policies, and default settings, then choosing vendors that make recording visible, limit access, document processing clearly, and give teams real control over retention and removal.

What makes AI notetakers a unique privacy risk

Not all AI tools create the same kind of privacy exposure. A design assistant or a data analysis model typically works on information that users choose to input deliberately and often after some level of review. AI notetakers are different from this. They operate on live conversations, spoken in real time, often containing unfiltered thoughts, sensitive context, and information that was never meant to be captured verbatim.

That difference matters because it changes both the nature of the data being processed and the expectations people have about control, consent, and downstream use.

Recording live conversations

The first and most obvious shift happens at the moment a meeting is recorded. Live conversations are not static documents. They are fluid, contextual, and often exploratory. People speak differently when they believe a conversation is temporary. They speculate, correct themselves, test ideas, and share information they might never put into writing.

When an AI notetaker records a meeting, it turns that transient exchange into a permanent artifact. This creates privacy risk even before any AI processing begins. Recording captures tone, intent, side comments, and moments that were never meant to be preserved outside the room. In many organizations, this is where discomfort starts, especially when recordings happen automatically or when participants are not fully aware of what is being captured. 

There is also a consent gap that teams often underestimate. In distributed or cross-company meetings, not everyone present may belong to the same organization or operate under the same policies. Recording a live conversation introduces questions about who has agreed to be recorded, how that agreement was obtained, and whether it would stand up to scrutiny later.

Transcription and speaker attribution

Transcription adds a second layer of risk by transforming speech into text. Spoken language is messy. It includes interruptions, overlaps, false starts, and informal phrasing that can read very differently once written down. A transcript freezes those moments in a way that can feel exposing, especially when attributed to named speakers.

Speaker attribution increases the sensitivity further. Identifying who said what creates a searchable record of individual contributions, opinions, and statements. In internal meetings, that can affect trust, particularly when power dynamics are involved. In external meetings, it raises questions about whether customers, partners, or candidates expect their words to be permanently attributed and stored.

Accuracy matters here, but so does interpretation. Misattribution, partial transcription, or loss of context can create records that do not reflect what was actually meant, yet still exist as authoritative-looking documents. From a privacy perspective this about reputational risk and fairness.

Storage, retention and searchability

Once a meeting is recorded and transcribed, it has to live somewhere. Storage is where many abstract privacy concerns become concrete. Teams often do not realize how long recordings are kept, where they are hosted, or who can access them by default.

Searchability amplifies this risk. The ability to search across months or years of meeting data is powerful, but it also means sensitive information can be surfaced long after the original context has faded. A casual comment made in a brainstorming session can resurface during audits, disputes, or internal reviews, detached from the conditions under which it was said.

Retention policies are therefore critical, yet frequently overlooked. Unlimited retention feels convenient, but it increases exposure over time. The longer data exists, the greater the chance it will be accessed, misused, or breached. Privacy conscious teams increasingly ask not just where data is stored, but how long it stays there and how easily it can be removed.

AI processing, summaries, highlights and action items

AI notetakers offer a lot more than transcription. They summarize discussions, extract highlights, generate action items, and sometimes infer intent or priorities. This layer of processing introduces additional privacy considerations.

Summaries are interpretations. They compress complex conversations into simplified narratives, which can change emphasis or omit nuance. Highlights and action items often surface decisions or responsibilities that participants did not explicitly frame that way. While useful, these outputs can create records that feel more definitive than the underlying conversation actually was.

From a privacy standpoint, the concern is not just that AI processes the data, but how those outputs are used and shared. Summaries are more likely to be forwarded, stored in project tools, or referenced later, extending the reach of the original conversation beyond its intended audience.

There is also the question of model access. Teams want to understand whether meeting data is processed in isolated environments, whether it is retained after processing, and whether it is ever exposed to systems beyond the immediate task of generating notes.

Secondary use risk and model training fears

One of the most persistent concerns around AI notetakers is secondary use. Teams worry that their conversations might be reused for purposes they did not intend, such as improving models, training future systems, or generating insights beyond the scope of the original meeting.

These fears are not unfounded. In the broader AI ecosystem, data reuse is common, and vendor messaging is often vague. Statements like “we do not train on your data” can hide important details about temporary processing, anonymization, or aggregated learning.

For privacy-conscious organizations, the key issue is control. They want clear, verifiable answers about whether meeting data contributes to model training in any form, whether opt out mechanisms exist, and how those commitments are enforced technically, not just contractually.

Secondary use risk also extends internally. Even if a vendor handles data responsibly, organizations must consider how recorded and processed meeting data might be reused by their own teams in ways that participants did not anticipate.

This is why AI notetakers feel different

Taken together, these factors explain why AI notetakers feel uniquely sensitive compared to other AI tools. They operate on unguarded human communication, create durable records from temporary exchanges, and layer interpretation on top of raw data.

The privacy risk is not inherent wrongdoing. It is exposure. Understanding that distinction is the first step toward evaluating these tools responsibly, rather than reacting with blanket acceptance or outright rejection.

The real concerns teams have, and why they are not irrational

When teams hesitate around AI notetakers, it is tempting to frame that resistance as fear of change. In practice, the concerns people raise are specific, situational, and often grounded in direct experience. Spend any time in practitioner communities and the same themes surface repeatedly, regardless of role or industry.

Internal meetings and psychological safety

One of the earliest points of tension around AI notetakers shows up in internal meetings, where conversation is exploratory and often unfinished. Teams repeatedly describe a shift once recording enters the room. People slow down. Language tightens. Ideas become safer before they become better.

That shift comes through clearly in a long running Reddit discussion about the ethics of AI notetaking tools at work. In the thread, the original poster describes introducing an AI notetaker to their team and being met with discomfort rather than interest. A colleague asked to be told before every meeting if recording was happening, saying they did not like being recorded at all, even if they would not actively block it.

The replies make it clear this reaction is common. One commenter wrote, “We ALWAYS ask participants if we can record them before any meeting. That’s basic professionalism.” Another said, “If I found out that you had recorded me without consent I would report you to HR. It’s a serious privacy violation.”

What stands out across the discussion is that very few people object to note-taking itself. The concern centers on permanence and loss of control once spoken language is captured and processed. One user explained, “I don’t want my words being fed into an AI to produce a ‘summary’ because I don’t trust it to produce an accurate one.” That unease sits with the risk of context being flattened and intent being misread.

Several commenters also described how recording alters group behavior over time. People said they were comfortable once recording was announced, but strongly opposed surprise recording, which many described as dishonest. Others raised concern about third party tools moving internal conversations outside approved systems, with one commenter asking whether anyone was really comfortable with “all your internal company discussions” being sent elsewhere.

Taken together, these responses point to the same underlying issue. Psychological safety depends on people feeling able to think out loud, change their position, and speak imperfectly. When meetings are routinely recorded, transcribed, attributed, and stored, that freedom narrows. Conversations become more careful and less open, which shapes what gets said and what never does.

Teams in these discussions describe fewer tentative ideas and more guarded participation once recording becomes routine. That change affects how problems are explored and how decisions form, which is why discomfort around internal recording cannot be dismissed as simple reluctance to adopt new tools.

Customer calls and external trust

Customer and client calls raise a different kind of risk because the people on the other side of the line are outside the organization’s control. They do not share internal policies, they have not agreed to internal tooling choices, and they often bring sensitive information into the conversation without knowing how it might be handled.

That gap in expectation shows up clearly in another Reddit discussion about why many professionals avoid note-taking tools. One executive assistant described a legal scenario where a client would accept a junior colleague joining a call but would immediately object to an AI notetaker being present. The conclusion was simple. People are more comfortable trusting another person than an automated system when the stakes are personal.

Elsewhere in the thread, commenters described organizations that actively remove or block AI note takers from meetings altogether, arguing that confidential conversations often drift into sensitive territory without warning.

The concern here is not recording itself, but loss of control once a conversation leaves the call. Customers may expect notes. They rarely expect their words to become stored, searchable, and processed by an AI system that others can revisit later.

Several participants noted that client conversations do not stay neatly within agenda boundaries. Routine calls can quickly move into personal, financial, or legal territory, which makes blanket recording feel risky.

Because of that, many organizations take a cautious approach. Some limit AI notetakers to internal use. Others require explicit approval for external calls. A few avoid them entirely in client facing settings, prioritizing trust over convenience.

That is why external calls remain one of the most sensitive contexts for AI notetakers. The issue is not permission on paper. It is whether the tool fits the expectations of the people on the other end of the line.

AI and privacy quote from reddit
Fonte: Reddit

HR, legal, and finance conversations

“If you don’t want anyone else to know what was said in those meetings then don’t put them into an AI transcriber.”

That sentiment comes up repeatedly when people working with confidential information talk about AI transcription tools. It reflects a practical boundary rather than a philosophical stance. In HR, legal, and finance settings, meetings are already treated as sensitive by default, and small uncertainties carry outsized risk.

In these contexts, people are acutely aware that words do not exist in isolation. Meaning depends on tone, timing, and intent, and all of that can be lost once speech is turned into a durable record. A performance conversation, a redundancy discussion, or a legal strategy session changes character when it becomes searchable and reviewable later by someone who was not present.

Concerns around reuse add to that unease. Practitioners often point out that privacy policies can allow audio or transcripts to be used for purposes beyond note creation, including model training or sharing within broader systems. Even when those practices are disclosed, the possibility of secondary use feels incompatible with conversations that assume strict confidentiality.

There is also a strong preference in these functions for keeping control close. On-device processing is frequently mentioned as more acceptable than cloud-based uploads because it reduces uncertainty about where data travels and who might access it later. That distinction matters most where legal, financial, or employment consequences are attached to how information is handled.

As a result, HR, legal, and finance teams tend to act conservatively. They rely on manual notes. They limit recording. They avoid tools that turn sensitive discussions into long-lived artifacts. These decisions are not about resisting change. They reflect an understanding that once certain conversations are captured and processed, the exposure cannot be reversed.

In these environments, restraint is a rational response to the weight those conversations carry.

Employee consent and trust

One of the most charged concerns around AI notetakers is whether employees truly have a choice about being recorded and transcribed. On paper, companies often handle this with policy language or in-app notifications. In practice, that leaves many people feeling they have little real control over how their words are captured and used.

Privacy professionals and commentators have flagged this issue clearly. Some experts argue that AI notetakers blur the lines between passive notification and active, informed consent, because participants often only discover that a tool has been recording them after the fact, when the recording or transcript appears in their files or discussions about retention and reuse occur. That raises questions about how freely consent is given and what participants actually understand about what has happened to their meeting data. 

A recent article on concerns about AI note-takers explained that the mere presence of an automated listener in a meeting can create a feeling of being watched or surveilled, which changes how people speak and participate. Employees may find themselves filtering language, avoiding uncertainty, or holding back contributions because they know their words are being captured and stored in ways that extend far beyond the moment the conversation ends.

This issue shows up in guidance from privacy and data protection professionals who stress that consent under frameworks like GDPR must be informed, specific, and meaningful. For example, organizations must tell participants not only that audio will be recorded, but also what data is captured, where it is stored, how long it stays, and who will have access. Details that often get buried in meeting invites or platform terms of service rather than spelled out before recording begins.

Zoom sono state molto popolari durante la pandemia

Shadow recording and unsanctioned tools

One of the more uncomfortable patterns to emerge around AI notetakers is that unclear rules and blanket restrictions do not stop recording. They change where it happens.

Often, when official tools feel restrictive or poorly explained, people look for alternatives. Personal accounts, browser extensions, local apps, and tools designed to avoid detection start to appear. A cybersecurity discussion on so-called “shadow AI” described an organization discovering hundreds of unsanctioned AI notetaker accounts operating without approval, visibility, or oversight. That behavior is not surprising. Employees often download a notetaker minutes before an important call, or activate functionality that already exists inside tools they use every day, such as Google Gemini or Microsoft 365. Recording does not feel like a separate decision when it is embedded into familiar software and framed as a default convenience rather than a policy choice.

The result is fragmented risk. Recordings end up spread across personal accounts, unmanaged storage locations, and systems no one in IT or security has visibility into. Retention rules are inconsistent. Access is unclear. Data reuse becomes impossible to track.

This is why blanket bans tend to backfire. When recording is treated as something to suppress rather than govern, it does not disappear. It becomes harder to see. Unofficial use creates more exposure than tools that are openly adopted, clearly explained, and understood by the people using them.

Shadow recording is not a failure of employee judgment. It is a signal that the gap between policy and day-to-day reality has grown too wide.

Why these concerns deserve weight

Taken together, these concerns are neither irrational nor hostile toward AI. They reflect a clear eyed view of how AI notetakers alter the lifecycle of workplace communication in ways people can feel immediately.

What people are reacting to is permanence, attribution, searchability, and reuse, alongside a shift in who controls what happens to their words once a meeting ends. Those reactions sit at the intersection of emotion and consequence. Conversations that once faded now persist. Context travels poorly. Ownership feels blurred.

Recognizing that reality matters. Without understanding how these tools change behavior and expectations, it is easy to dismiss discomfort as resistance, or to over rely on assurances that sound sufficient on paper but fall short in practice.

That context is the starting point for any meaningful evaluation of how AI notetakers handle privacy.

Privacy regulation around AI notetakers is often reduced to shorthand. Some teams treat it as something that can be handled with a checkbox. Others see it as territory best avoided unless legal gets involved. Neither approach reflects how these rules show up once recording becomes part of everyday work.

Frameworks like GDPR matter here because AI notetakers deal with spoken conversation. People talk differently than they write. They revise themselves mid sentence. They share context they might never put into a document. Regulation becomes relevant precisely because those moments are being captured and kept.

Questions to consider when looking at AI notetakers

There are many reasons that people will need to do some due diligence before diving into the right AI notetaker for their business operations. Here are just a few talking points to consider when making that choice.

1) Lawful basis, consent and legitimate interest

One of the first questions teams ask is whether everyone in a meeting needs to agree to be recorded. The answer is rarely simple, which is often where unease begins.

Under GDPR, organizations need a lawful basis to process personal data. Consent is one option. Legitimate interest is another. Many workplace tools rely on legitimate interest, on the assumption that the processing supports a reasonable business purpose and does not override individual rights.

AI notetakers sit close to the edge of that assumption. Most people expect notes to be taken. Fewer expect their exact words to be captured and processed by an automated system. That gap between expectation and reality matters, even when the lawful basis is technically sound.

This is why disclosure carries so much weight. When consent is not the basis, people still need to understand what is happening to their data before it happens, not after a transcript appears.

If someone joined your meeting expecting notes rather than a permanent record, would they feel they had been given a real choice?

2) Data minimization in a world of full transcripts

Data minimization sounds theoretical until it collides with a recorded meeting.

The idea itself is straightforward. Organizations should collect and keep only what they need. AI notetakers often do the opposite by default, capturing everything because it is easy to do so and useful to search later.

That does not make these tools incompatible with regulation. It does mean teams have to decide what they actually need. Full recordings might be helpful. They might also be unnecessary. Transcripts might be valuable for a time. They might not need to exist forever.

Minimization here is about intention. What problem is being solved, and how much data is being kept to solve it?

If a summary would meet the need, why keep the entire conversation?

3) Rights of access, deletion and control

Regulation stops feeling abstract when someone asks to see their data.

Under GDPR, people have the right to access personal data and, in many cases, ask for it to be removed. When meetings are recorded and transcribed, those rights extend to spoken contributions as much as written ones.

That introduces real operational demands. Teams need to know where recordings live (preferably in a single unified space) and how to act on requests without guesswork. This is where tooling matters. Retention settings and deletion processes stop being nice to have and start being necessary.

Without those controls, even well-meaning teams can find themselves stuck.

If someone asked you to remove their contributions from past meetings, could you do it cleanly?

4) Purpose limitation and secondary use

Another point where regulation meets reality is purpose limitation. Data should be used only for the reason it was collected.

For AI notetakers, that raises a simple question. If a meeting is recorded to generate notes, what else happens to that data over time? Does it stay within that boundary, or does it flow into other systems and uses?

For teams operating under EU regulation, this question carries particular weight. Reuse that feels distant or technical can still matter if it was not clearly explained at the outset. This is why statements about model training and reuse tend to be read closely rather than taken on trust.

If the data did more than participants expected, would that surprise feel acceptable?

Why EU-centered teams are cautious by design

European regulators have taken a firm view on workplace monitoring and power imbalance. AI notetakers touch both areas at once, which explains why EU-centered teams often move carefully.

That caution usually shows up early. Teams think about retention before rollout. They ask about access before enabling recording. They look for justification rather than retrofitting rules later.

This is an attempt to avoid problems that are hard to unwind once trust is lost.

How to evaluate an AI notetaker responsibly

Once teams accept that AI notetakers raise real privacy and security questions, the next challenge is choosing one with care. This is where many evaluations stall, not because people are disengaged, but because it becomes difficult to tell how a tool behaves once it moves beyond a demo and into everyday use.

Responsible evaluation focuses on outcomes rather than assurances. The aim is to understand what happens to meeting data over time, across different situations, and when edge cases appear.

1) Start with questions that describe behavior

Early evaluations often focus on whether a tool meets regulatory requirements. That matters, but it rarely tells the whole story. More useful insight comes from understanding how the system operates by default.

  • Where meeting data is stored, including the regions involved
  • How long recordings and transcripts exist before removal
  • Who can view recordings, transcripts, or summaries after a meeting
  • Whether access changes based on role, workspace, or ownership
  • What happens to data when someone leaves the organization 

Tools that handle these areas well make it easier for teams to explain usage internally and apply consistent rules without friction.

2) Consent, disclosure, and expectations

Recording works best when people understand what is happening before it starts. Evaluation should look closely at how tools communicate recording status in real meetings.

  • Clear notification when recording begins
  • Ability to switch recording on or off per meeting
  • Predictable handling of late joiners
  • Visible signals for external participants, such as a bot

When recording is explicit and deliberate, teams are more comfortable using it and less likely to create workarounds.

3) AI processing and model use

How AI systems interact with meeting data is a key area of focus during evaluation.

  • Whether meeting data contributes to model training
  • Whether processing is isolated per customer
  • What data remains after summaries are generate
  • Whether third party models are involved

     

Tools that explain these flows clearly and design for isolation give teams confidence about how their data is handled beyond the immediate meeting.

4) AI processing and model use

How AI systems interact with meeting data is a key area of focus during evaluation.

  • Whether meeting data contributes to model training
  • Whether processing is isolated per customer
  • What data remains after summaries are generated
  • Whether third party models are involved

     

Tools that explain these flows clearly and design for isolation give teams confidence about how their data is handled beyond the immediate meeting.

5) Recognizing incomplete answers

During evaluations, some responses signal the need for deeper discussion.

  • Broad compliance claims without explanation
  • References to certifications that are not tied to recording workflows
  • Statements about safety without detail on access or retention
  • Heavy reliance on policy without supporting system controls

     

Stronger evaluations treat these as prompts for clarification rather than red flags.

6) Evaluating impact inside the organization

Responsible evaluation also includes thinking about internal use.

  • How recording fits into existing meeting culture
  • Who decides when recording is appropriate
  • What guidance people receive before using the tool
  • How concerns or opt outs are handled

     

When these decisions are made early, teams are more likely to use the tool openly rather than quietly avoiding it.

What stronger answers tend to include

More helpful answers focus on how the product behaves in normal use rather than edge cases.

  • Clear explanation of default settings
  • Straightforward description of access in day-to-day scenarios
  • Honest acknowledgement of limits or constraints
  • Documentation that reflects how the tool is actually used

     

This kind of detail makes it easier for teams to roll tools out responsibly and support them over time.

Looking for evidence beyond conversation

Evaluation is stronger when it looks beyond sales conversations and into evidence that exists independently of them.

Publicly available security or trust centers that are kept current, documentation that clearly explains how recording and transcription behave in practice, and transparent descriptions of how data moves between systems all make a difference. Independent audits that reflect real usage rather than abstract compliance add further weight. Having this material available makes it easier for security, legal, and operations teams to align early and support responsible rollout over time.

How responsible vendors mitigate privacy risk in practice

Responsible vendors mitigate privacy risk through concrete product decisions rather than broad assurances. These decisions show up in how recording works, who can access content, how long data exists, and how AI processing is handled behind the scenes.

One of the most visible areas is recording itself. Responsible tools make recording explicit rather than hidden. In tl;dv’s case, meetings are recorded through a visible bot that joins the call, making recording apparent to all participants. Hosts can start or stop recording, and recording is not designed to run silently in the background. This reduces the risk of surprise and supports informed participation from the outset.

Access to recorded content is another area where implementation matters. tl;dv governs access to recordings and transcripts at the workspace level rather than making content universally visible by default. This means recordings are only available to people within the relevant workspace, and sharing outside that context is controlled. That approach limits unintended spread and aligns access with the original meeting context.

Retention and deletion are treated as operational controls rather than edge cases. tl;dv allows teams to manage how long recordings and transcripts remain available, and deletion removes the underlying meeting data rather than simply hiding it from view. Summaries and AI-generated outputs follow the same lifecycle as the source material, which supports predictable handling over time.

Trust Center

AI processing boundaries are explicitly documented in tl;dv’s Trust Centre.

According to published materials, customer meeting data is processed solely to deliver features like transcription, summaries, and insights, and is not used to train broader AI models, a distinction that helps teams assess secondary use risk because it draws a clear line around how data is handled beyond the immediate meeting. The Trust Centre also includes independent artefacts such as a SOC 2 Type II attestation and a penetration test report, along with details on infrastructure and organizational controls, showing how access and processing are governed in day-to-day operation.

Where this information lives is as important as the content itself. tl;dv separates system-level commitments from day-to-day behaviour. Its Trust Centre documents security, data handling, and processing practices at a high level, while its Help Centre explains how recording works, how access is managed, and how retention settings can be applied in practice. This makes it easier for teams to evaluate the platform during procurement and understand its behaviour once it is in use.

None of these measures eliminate risk entirely. They reduce ambiguity. They limit accidental exposure. They support informed use without relying on informal workarounds.

When vendors document what their systems do, design for visibility, and give teams control over access and retention, privacy becomes part of how the product operates rather than something bolted on after the fact. That is what responsible mitigation looks like in practice.

Choosing an AI notetaker without gambling trust

AI notetakers sit in an awkward place. They promise relief from admin, better recall, and fewer missed details, while also touching the most human part of work, conversation. It makes sense that people hesitate before letting tools listen, remember, and summarize what they say at work.

That hesitation reflects an instinctive understanding that once conversations are recorded and processed, the ground shifts. Words last longer. Context travels further than intended. What felt informal can suddenly feel permanent. Control starts to matter in a way it did not before.

Choosing responsibly does not mean writing these tools off. It means paying attention to how they behave by default, how clearly they explain themselves, and whether they fit the messy reality of real meetings rather than idealised ones. Visibility beats invisibility every time. Defaults matter more than edge cases. Clear documentation matters more than reassurance in a sales call.

It also helps to separate trying something out from committing to it. Many teams get value from using these tools in low-risk settings first, seeing how recording, access, and retention actually work, and deciding what feels acceptable before rolling anything out more widely. That kind of pace respects both efficiency and the people whose conversations are being captured.

When teams want to go deeper, the strongest signal is rarely a demo or a conversation with a slick sales rep. It is the written record a vendor leaves behind. Trust centres, security pages, and help articles show how privacy is handled when no one is trying to convince you. They make it easier to understand what is recorded, who can see it, how long it sticks around, and where AI processing stops.

Vendors such as tl;dv publish this material openly, drawing a clear line between high-level commitments and the practical details of day-to-day use. That gives teams space to verify claims for themselves, rather than taking them on trust.

Adopting AI notetakers should not feel like a leap of faith. With the right questions, clear documentation, and a willingness to move deliberately, teams can use these tools without quietly undermining the trust that makes meetings work in the first place.

FAQs About AI and Privacy

They can be, but only when there is a clear lawful basis for processing, proper disclosure before recording, and controls in place for access, retention, and deletion. Simply notifying participants after the fact is not enough.

This depends on the vendor. Some tools state that customer data is not used for model training, while others may rely on broader processing arrangements. Always check documentation carefully rather than relying on marketing claims.

These contexts carry higher sensitivity because conversations often involve confidential or high impact information. Many teams limit or avoid recording in these settings unless there is a clear, documented need and strong data controls in place.

Look at where data is stored, how long it is retained, who can access it by default, whether deletion fully removes recordings and transcripts, how AI processing works, and whether the vendor provides transparent security and trust documentation that reflects real-world use.