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AI productivity apps can boost your work efficiency, but they come with real privacy and security risks. These tools often request broad access to your email, calendar, and documents to work their magic. The problem? That same access creates new attack surfaces for hackers and data thieves.
Recent incidents show the scope of concern. Cybersecurity risks are cited by 51 percent of US employees, inaccuracies by 50 percent, and concerns about personal privacy by 43 percent when it comes to workplace AI tools (Source — McKinsey, 2025). Meanwhile, security experts warn about everything from prompt injection attacks to unauthorized data sharing through third-party connectors.
This guide breaks down the five biggest risks, shows you what major apps are doing about security, and gives you a practical checklist to stay protected. By the end, you’ll know what to watch for and what settings to change before connecting your work accounts to any AI assistant.
Before you connect any AI app to your work accounts, run through this essential safety check. These steps catch the most common security gaps in under two minutes.
Start with your app permissions. Most AI tools ask for more access than they actually need. Review what data the app can see and limit it to the minimum required for your specific use case.
The rise of AI productivity tools creates new security challenges that didn’t exist five years ago. Prompt Injection vulnerabilities exist in how models process prompts, and how input may force the model to incorrectly pass prompt data to other parts of the model, potentially causing them to violate guidelines, generate harmful content, enable unauthorized access, or influence critical decisions (Source — OWASP Gen AI Security Project, 2025).
Third-party connectors present another major concern. When you connect your Gmail or Google Calendar to an AI assistant, you’re creating a bridge between your private data and the AI company’s servers. That bridge can be compromised. Earlier this year, security researchers demonstrated how malicious prompts embedded in calendar events could trick AI assistants into leaking sensitive information from connected accounts.
The “shadow AI” problem compounds these risks. Employees increasingly use AI tools without IT approval, often sharing company data through personal accounts. This creates security blind spots where sensitive information flows to unmanaged systems. A convergence of rapidly evolving technological developments is leading to an increased focus on privacy and security by design and effective AI and data governance by companies and regulators around the world (Source — Dentons, 2025).
Prompt injections involve bypassing filters or manipulating the LLM using carefully crafted prompts that make the model ignore previous instructions or perform unintended actions (Source — OWASP, 2025). Think of it as social engineering for AI systems.
Here’s how it works: An attacker embeds malicious instructions in content the AI processes. For example, they might hide commands in an email that tell the AI to ignore its safety guidelines and share confidential information. The AI follows these hidden instructions without realizing they came from an untrusted source.
OWASP defines two types of prompt injection vulnerabilities: Direct Prompt Injection: A user’s prompt directly changes the LLM’s behavior in an unintended way. Indirect Prompt Injection: An LLM accepts input from an external source (like websites or files) that subsequently alters the LLM’s behavior (Source — Promptfoo, 2025). Both types can lead to data leaks or unauthorized actions.
Quick fix: Never paste sensitive data directly into AI prompts, and be cautious when processing files or emails from unknown sources through AI tools.
Email and calendar connectors create the largest attack surface in AI productivity apps. When you authorize an app to read your Gmail, you’re granting access to years of communication history, contact lists, and potentially confidential attachments.
The risk multiplies when AI apps use broad permission scopes. Many request “read all email” access when they only need to see specific folders or recent messages. This excessive access means a single compromised AI app could expose your entire digital communication history.
Third-party integrations add another layer of risk. Some AI apps share your data with external services for processing or storage. Each additional service in the chain creates new points of potential failure. A breach at any linked service could compromise your information.
Quick fix: Use the most restrictive permissions possible, regularly audit connected apps, and disconnect any AI tools you no longer actively use.
The AI models may memorize and leak sensitive information from the training dataset. It could be when the model is asked certain questions or when it generates outputs (Source — SentinelOne, 2025). This means your private data could inadvertently become part of the AI’s knowledge base.
Different vendors handle training data differently. Some explicitly state they don’t use customer data for model training, while others include broad language allowing them to improve their services using your information. The distinction matters because training data can be nearly impossible to remove once incorporated.
Retention policies vary widely, too. Some apps delete your data after processing, others keep it for months or years. Enterprise plans often include stronger data protection guarantees, but you need to verify these claims by reading the actual terms of service.
Quick fix: Check your AI app’s data use policy, opt out of training data use where possible, and prefer vendors with clear data deletion timelines.
The popularity of AI tools has spawned a wave of malicious copycat apps designed to steal credentials and data. These fake apps often appear in app stores with names similar to legitimate services, complete with stolen logos and descriptions.
Credential harvesting represents the most common scam. Fake AI apps ask users to “sign in with Google” or enter work credentials, then capture and sell this information to other attackers. The stolen credentials can then be used to access the victim’s real accounts and data.
Some malicious apps go further, installing malware or requesting excessive device permissions. Once installed, these apps can monitor your activity, steal stored passwords, or serve as a backdoor for future attacks.
Quick fix: Only download AI apps from official vendor websites or verified app store listings, double-check developer names, and never enter credentials into apps you can’t verify as legitimate.
Shadow AI occurs when employees use unauthorized AI tools to complete work tasks, often sharing company data with unvetted services. This creates security blind spots where sensitive information flows outside official IT controls.
The convenience factor drives shadow AI adoption. Employees discover AI tools that make their work easier and start using them without considering security implications. They might paste customer lists into ChatGPT for analysis or upload financial reports to AI summarization tools.
Each instance of shadow AI creates potential data leakage points. Company information ends up on external servers without backup protection, audit trails, or data recovery options. When something goes wrong, IT teams may not even know which systems to investigate.
Quick fix: Establish clear AI usage policies, provide approved alternatives for common AI use cases, and educate employees about the risks of unauthorized AI tool usage.
Popular AI productivity apps vary significantly in their security approaches and data handling practices. This comparison focuses on key security features as of September 2025.
| App | Connectors Available | Default Data Use/Training | Enterprise Opt-Out | Certifications | Quick Tip |
|---|---|---|---|---|---|
| ChatGPT | Email, Calendar, Drive | Training opt-out available | Yes (Plus/Enterprise) | SOC 2 Type II | Check data controls in settings |
| Claude | Limited integrations | No training on conversations | Yes (Pro/Team) | SOC 2 Type II | Use Projects for sensitive work |
| Microsoft Copilot | Full Office 365 suite | Commercial data protected | Yes (Business plans) | ISO 27001, SOC 2 | Review SharePoint permissions |
| Google Bard/Gemini | Gmail, Drive, Calendar | Uses data for improvement | Limited options | ISO 27001 | Separate personal/work accounts |
| Notion AI | Notion workspace only | Training on public content only | Yes (paid plans) | SOC 2 Type II | Keep sensitive docs in private spaces |
| Slack AI | Slack messages/files | No training on customer data | Yes (paid plans) | SOC 2 Type II, ISO 27001 | Check message retention settings |
The key differences center on data training policies and connector scope. Microsoft Copilot offers the deepest integration but requires careful permission management. ChatGPT and Claude provide clearer opt-out mechanisms for training data. Google’s offerings show the most variation in data use policies between consumer and business versions.
Most enterprise-focused plans include stronger security controls and compliance certifications. However, the actual implementation details matter more than marketing claims. Always verify current policies on vendor websites before making decisions.
Start with a security-first setup before connecting any AI app to your work accounts. This prevents the most common security issues and gives you better control over your data.
Create separate accounts for AI tools when possible. Use your work email only for AI services that your IT team has explicitly approved. For personal productivity experiments, use a personal email account to keep work data isolated.
Configure the most restrictive permissions that still allow the app to function. If you only need an AI assistant to read recent emails, don’t grant access to your entire inbox history. Most apps allow you to adjust these permissions after initial setup.
Enable two-factor authentication on all AI app accounts. This adds a crucial security layer even if your password gets compromised. Use authenticator apps rather than SMS when possible, as they provide stronger protection against account takeover attempts.
Review connected apps monthly and remove any you no longer use. Each connected service represents an ongoing security risk. Regular cleanup reduces your attack surface and helps you spot any unauthorized access attempts.
Monitor your AI app usage through your Google, Microsoft, or other account security settings. These platforms show which third-party apps have access to your data and when they last accessed it. Unusual activity patterns can indicate compromised accounts.
IT teams need clear policies about AI productivity app usage, especially as these tools become more prevalent in workplace workflows. The goal is to balance productivity benefits with acceptable security risks.
Establish an approved AI tools list that includes security-vetted options for common use cases. Provide alternatives like enterprise-grade AI assistants with proper data protection agreements rather than simply blocking all AI access.
Implement data loss prevention (DLP) controls that flag when sensitive information gets shared with external AI services. Configure alerts for the sharing of customer data, financial information, or other classified content through unapproved channels.
Require business associate agreements (BAAs) or data processing agreements (DPAs) for any AI tools that handle sensitive company data. These legal frameworks establish clear responsibilities and provide recourse if data breaches occur.
Create a decision matrix for evaluating new AI tools: approve tools with strong security certifications for general use, require IT review for tools with broad data access, and block tools that lack basic security controls or have unclear data policies.
Set up regular security audits of connected AI applications, including permission reviews and data access logs. Many security breaches go undetected for months because organizations lack visibility into third-party app usage.
