Your AI Notetaker Might Be a Liability: Insights from Stealer Logs

Remote and hybrid work have changed how we communicate and AI-powered notetaking tools aim to solve this. They join meetings, take notes, highlight key points, and help teams follow up. There are specific tools for this purpose and even large tech companies are adding similar features.

These tools definitely offer clear benefits. They help you remember key details, track tasks, and stay organized. But they also raise risks. Recording and storing sensitive talks can lead to privacy and security problems such as:

• Sensitive information often gets shared in meetings, making it a target for leaks.
• Not everyone should access meeting notes or recordings, but sometimes they do.
• Many AI tools process data in the background, and it’s not always clear what they store or use.
• Data protection laws require strict handling of personal and company information, which these tools might not fully meet.

In this article, we’ll focus on one growing threat: stealer logs. These logs, often sold on dark web markets, can contain login details and sensitive data and sometimes even full access to meeting tools and platforms. If attackers get this data, they could access private meetings, notes, or recordings without anyone noticing.

How Do AI Note-Taking Apps Work?

AI notetaking tools work through several connected steps. First, they capture the audio from meetings in real-time. Then, they clean the audio to remove background noise and improve quality. Next, they use speech recognition models to turn spoken words into text. After that, various models analyze the text. They identify speakers, detect important topics, and extract key actions or decisions.

Basic overview of AI note-taking app flow

All this processing happens mostly in the cloud, where powerful servers handle large amounts of data quickly. The system stores meeting transcripts and notes so users can access them later.

This setup allows the tool to provide transcripts, summaries, and task tracking during or after meetings. But because so much data flows through multiple stages and cloud servers, each step can create security risks if not carefully managed.

What Legal and Ethical Issues Come with AI Note-Taking Apps?

Legal and Ethical Points of Using AI Note-Taking Apps

Using AI note-taking tools can be incredibly helpful but they also come with some serious legal and ethical responsibilities. Before diving in, organizations need to think about how these tools collect, store, and use data, and how the output might influence decisions or impact privacy.

Protecting data should be a top priority. If you’re choosing a transcription service, make sure it follows data privacy laws and uses secure, well-managed systems. Ask vendors straightforward questions: What data do you store? Where is it stored? How long do you keep it? Their answers should align with your company’s policies. If the meeting includes sensitive topics, depending on the situation, it can be safer to store that data internally rather than relying on third parties.

Also, check whether the AI tool learns from your data. Some services use conversations to improve their models, which can sound great for performance but it also raises red flags for confidentiality. Organizations should know how to opt out of this kind of training and make sure staff understands how to use those settings properly.

Accuracy is another big issue. AI transcription tools aren’t perfect and they can misidentify speakers, assign tasks to the wrong person, or misinterpret important figures. These kinds of errors can lead to poor decisions, confusion, or missed deadlines. Remember, AI doesn’t “understand” what it’s writing, it just predicts what words come next based on patterns. Sometimes it even makes things up, a phenomenon known as “hallucination.” That’s why it’s best to treat AI-generated notes as drafts. Always review them before making decisions or sharing them with others.

Recording meetings brings legal concerns too. Rules around consent vary depending on where you are. In various U.S. states, only one person needs to agree to be recorded. But in states like California and Illinois, and in many other countries, everyone involved has to give consent. Some tools offer built-in notifications, like automatic consent messages in meeting invites, but those don’t fully cover your responsibilities. Be upfront: always tell participants if a meeting is being recorded. And avoid tools that secretly record, like certain browser extensions.

There are also industry-specific rules to keep in mind. Law firms need to protect attorney-client privilege, while healthcare providers must follow HIPAA to safeguard patient data. Even within a single company, different teams may have different privacy needs. Marketing might handle less sensitive data, while legal or HR departments need stricter controls. If your use case involves removing personal information, make sure the tool’s deidentification process meets the standards of your industry.

To use AI note-takers responsibly, build good habits:

• Keep your data retention policy up to date.
• Assign someone to regularly review AI-generated content.
• Set clear rules for deleting recordings and transcripts.
• Always inform meeting participants when AI tools are being used and explain what’s happening with their data.
• Let people opt out if they’re uncomfortable.
• And finally, never treat transcripts as the final word. Always double-check critical details before acting on them.

Threat Landscape Around AI Note Taking Apps: What Can We Learn from Other Cases?

We’ve identified the following case studies that reveal the threat landscape around AI notetaker apps. From leaked credentials to leaked meeting recordings these incidents demonstrate how quickly “convenience” can transform into “catastrophe”.

According to Cybernews, 84% of 52 major AI platforms have suffered data breaches. Alarmingly, 93% had SSL/TLS issues, 91% showed infrastructure flaws, and 51% had corporate credentials stolen. The productivity tools segment, including AI notetakers, proved especially vulnerable with 92% experienced breaches and every single one showed infrastructure weaknesses.

With 75% of employees using AI tools at work but only 14% of companies enforcing policies, shadow IT has become the norm of this new era.

The result is a high-risk environment where sensitive data routinely bypasses security oversight, turning AI convenience into a serious liability.

The Accidental Data Leak Case From an AI Notetaker App

An accidental data leak from an AI note taking application – Source

In one case, a venture capital firm’s routine use of Otter AI during client meetings created an unexpected privacy breach. During what should have been a standard call, the firm’s AI notetaker continued recording long after the external participant had left the meeting.

The result was catastrophic from a confidentiality standpoint. The automated transcript contained internal discussions about sensitive business matters and it was automatically distributed to all meeting participants, including the external contacts.

Moreover, such incidents pose an even greater risk as the corporate information captured and leaked through these recordings can later be sold on dark web platforms if a threat actor can get that data.

Worried about your sensitive data ending up on the dark web? You can get your Free Dark Web Report now and see if your corporate information is at risk.

Can Threat Actors Impersonate AI Note-Taking Apps?

The nature of AI meeting bots has created a new attack vector where malicious actors can impersonate legitimate AI services to gain unauthorized access to confidential meetings and sensitive discussions.

In this scenario, the attack methodology is disturbingly simple. Threat actors can create meeting accounts with legitimate looking AI bot names. When they request to join meetings, busy participants might approve these requests without verification, assuming they’re legitimate AI tools that a colleague has activated. The psychological conditioning is powerful since users have become accustomed to seeing AI bots join their meetings, making them less likely to scrutinize unexpected bot participants. During this passive presence, threat actors can harvest confidential discussions without raising suspicion.

Can AI Note-Taking Apps Be Used for Phishing? Risks of Post-Meeting Manipulation

The impersonation attack scenario doesn’t have to end when the meeting concludes. AI notetakers created the perfect ground for “post-meeting manipulation”. Threat actors can use transcript emails as sophisticated phishing vectors to exploit the trust established during the meeting itself.

After silently recording a meeting while masquerading as a legitimate AI assistant, the threat actor in this scenario can send an email containing the meeting transcript. This email can look like it is from a recognizable AI service, complete with proper branding, formatting, and even accurate meeting content that was genuinely recorded during the session. Unlike cold phishing emails that trigger suspicion, these messages can come as expected follow-ups to meetings that participants know occurred.

In this scenario other parties can get infected as well. Participants from different organizations who were present in the same meeting can also be affected. Since they received the same calendar invite and may expect follow-up materials, threat actors can send tailored phishing emails to these external attendees.

What Are the Access Control Risks of AI Note Taker Apps?

General flow of the access control issue

A company exploring AI tools to improve efficiency decided to test a meeting assistant. The assistant was designed to join virtual meetings, transcribe discussions, and distribute summaries. But the implementation revealed a deeper issue: invisible access can carry significant, unintended consequences.

The situation began during an ordinary team meeting. An unfamiliar participant appeared in the session, an AI notetaker. No one on the call had invited it. Still, assuming someone else had authorized its presence as part of a trial, the group continued with the meeting.

Afterward, participants received a detailed summary. The message appeared to originate from a colleague who hadn’t been present. That person also wasn’t involved in the AI tool evaluation. When asked about the message, the individual recalled using a notetaker tool during a client meeting a few days earlier. After the session, the tool sent a follow-up email. To access the notes, sign-in was required which was completed using a company account through SSO.

What wasn’t clear at the time was that this sign-in granted the tool ongoing access to the user’s calendar. A default setting allowed the notetaker to automatically join all scheduled meetings associated with the account. There had been no explicit warning that this would happen.

From that point on, the tool began silently joining meetings. It recorded conversations, produced notes, and shared them with participants. Some of these summaries included invitations for others to start using the same tool. This created a self-reinforcing loop: more access led to more visibility, which led to more sign-ups. The tool wasn’t operating under direct oversight. It had embedded itself within the organization’s workflow, spreading on its own terms.

This incident offers a clear warning about the risks of unchecked automation and overly permissive access controls.

How Stealer Logs Can Expose Sensitive Information on AI Note Taking Apps?

To understand how stealer logs may expose sensitive data from AI notetaking applications, we analyzed stealer logs linked to ten popular domains in this space. Stealer malware captures browser-stored credentials, session tokens, and authentication cookies as well as other types of credentials. If compromised, these credentials can allow attackers to retrieve meeting recordings, transcripts, or even join live sessions. This chapter explores the distribution of this data and highlights how seemingly low-level infections can lead to serious privacy and data security risks in modern AI-powered collaboration tools.

Stealer logs are a growing threat in the cybersecurity landscape, affecting individuals and organizations alike. From how they’re created to how threat actors use them, there’s a lot to unpack. If you’re looking to better understand what stealer logs are, how they work, and the risks they pose, we’ve got you covered. To learn more about stealer logs, you can read our article Stealer Logs: Everything You Need to Know

Distribution of the Compromised Stealer Log Data

The growing adoption of AI-powered notetaking apps in professional environments has introduced a new category of security risks since these tools store sensitive data in the cloud and are integrated with corporate accounts. One of the most alarming threats comes from stealer logs: logs generated by malware designed to silently extract stored credentials, browser sessions, tokens, and application data from infected devices.

Our analysis of leaked stealer log data has revealed the scale at which sensitive information from notetaking apps can be compromised. In just one dataset, the following credentials and data points were exposed:

• Total Email/Password Combinations (Credentials Exposed): 148,135
• Total Password Hashes: 14,933
• Total Unique Victim IPs: 6,902
• Total Credit Cards Exposed: 3,749

Distribution of the Compromised Stealer Log Data by Victim Country

The distribution of stealer log data linked to AI notetaking applications shows a wide global reach. India (11.12%) and Indonesia (10.12%) appear at the top, suggesting high adoption of these tools and potentially weaker endpoint protections in some environments. Egypt (6.91%) and the United States (6.22%) follow, showing that both emerging and developed markets are affected. Interestingly, countries like Rwanda, Algeria, and Ecuador also show notable activity. These regions may be exposed through unofficial app sources, browser extensions, or unmonitored deployments.

The data highlights how stealer malware campaigns are not limited to one region. Attackers often target users globally, relying on poor credential hygiene and weak device security. For AI notetaking platforms, this reinforces the need for strong token handling, region-aware access controls, and alerting mechanisms to detect suspicious logins.

These numbers reflect real data siphoned from real users, many of whom likely had no idea their information was at risk. Once infected with stealer malware, devices silently send stored credentials, autofill data, session tokens, and browser cookies to attacker-controlled infrastructure.

If a notetaking app stores login sessions locally or syncs with cloud services using persistent tokens, this data can easily be harvested. From there, attackers can:

• Access private meeting notes, including sensitive conversations, intellectual property, and client data.
• Compromise associated email accounts, especially when the same credentials are reused across platforms.
• Leverage calendar and workspace integrations to expand access into broader enterprise environments.
• Sell harvested data on underground markets, where credentials and session tokens can be repurposed for further attacks.

The appeal of notetaking tools lies in their convenience. But this convenience often involves granting deep access to calendars, emails, and cloud storage. When those tools are installed on endpoints that lack adequate security controls, they become high-value targets for stealer malware.

Conclusion

AI notetaking applications offer clear benefits for productivity, helping teams capture and organize meeting content with minimal effort.

But behind this convenience lies a complex web of security and privacy risks. From vulnerable APIs and browser extensions to weak OAuth setups and exposed cloud storage, each part of the system presents potential entry points for attackers.

The heavy reliance on third-party SDKs and cloud infrastructure only adds to the challenge, creating dependencies that are often hard to track or secure.

As these tools become more common in business environments, organizations must take a closer look at how they’re implemented and secured. A strong security posture starts with understanding the risks, then actively working to reduce them. AI can improve how we work, but only if we build it on a foundation of trust, transparency, and security.