Top 4 AI chatbot privacy concerns and how to mitigate them

4 AI chatbots privacy concerns

AI chatbots introduce privacy risks that organizations must consider. Four prominent concerns are data breaches, unauthorized access, communication interceptions and user profiling and data misuse.

1. Data breaches

Like any system that handles sensitive data, chatbots are vulnerable to data breaches.

Users often exchange confidential information with chatbots, including sensitive medical and financial details. The chatbot could store such information for an extended time.

For example, chatbots can store the following information:

• Conversation history between the chatbot and the user.
• Banking and financial information, such as transaction details, account numbers and card details.
• Health data, such as patient health information.
• Personally identifiable information, such as Social Security numbers, passport and driver's license numbers and mailing addresses.
• Business-critical data, such as internal documents, trade secrets and supplier details.

Cybercriminals are aware of the types and scale of information that chatbots process and store, which makes these tools a lucrative target for cyberattacks. If infiltrated, hackers can exploit chatbots to access sensitive information. This can have catastrophic consequences for an organization's reputation and compromise users' private information.

2. Unauthorized access

Due to their complexity, AI chatbots provide multiple entry points for cybercriminals to exploit, such as the following:

• API exploitation. Many chatbots integrate with external language model APIs, like OpenAI's API for ChatGPT. Security vulnerabilities in API implementations -- such as inadequate authentication, improper access controls or insufficient input validation -- can enable attackers to perform unauthorized actions, access sensitive user data or compromise chatbot services.
• Session hijacking. Chatbots use session management to maintain user authentication and conversation state. Attackers can hijack active user sessions if there are vulnerabilities in session implementation -- such as predictable session tokens, lack of session encryption, insufficient session timeouts or missing secure cookie attributes. Risk increases when users access chatbots on unsecured networks or compromised devices.
• Privilege escalation. Chatbots contain administrative dashboards to manage their functions. Dashboard access might not use strong access controls, such as applying multifactor authentication (MFA). If a threat actor successfully compromises one chatbot admin account, they can escalate privileges to access other areas or sensitive data.
• Third-party integration vulnerabilities. AI chatbots interact with various third-party services, including CRM systems, external services and databases. Threat actors can exploit integration points for malicious purposes. For instance, if a chatbot integrates with a CRM system to fetch customer information, an unsecured API could enable attackers to reveal customers' personal data, leading to privacy breaches and compliance violations.

3. Communication interception

When interacting with a chatbot using the internet, communications channels are subject to the following security risks, which can undermine user privacy:

• Man-in-the-middle attacks. Hackers can intercept unencrypted communication with a chatbot. Suppose a user communicates with a chatbot using a public Wi-Fi connection, and the connection between the user and the chatbot is not encrypted. Any attacker on the same network can intercept and read the messages. 
• Network traffic analysis. Even if chat messages are encrypted, attackers can still examine traffic patterns to reveal user behaviors. An attacker eavesdropping on encrypted messages between a user and a chatbot might examine the frequency and timing of messages. If they notice that a user interacts with a chatbot every day at 3 p.m., they might infer that the person requires assistance at that time of day. This might open the door to more targeted attacks, such as phishing scams that exploit the user's typical behavior.

4. User profiling and data misuse

Chatbots collect vast amounts of data about their users. Chatbot operators and other third-party providers that have access to chatbot conversation records can misuse this information.

User data can be used to profile chatbot users and track them across the internet in the following ways:

• Cross-platform tracking. Organizations might use chatbot data to track users across different platforms and online services. For example, Google is known for its extensive tracking capabilities across various services, including Gmail, Google Assistant and Google Search Engine. When users interact with a chatbot using Google Assistant, their interactions can combine with those from other Google services to create a comprehensive user profile.
• Behavioral pattern analysis. AI chatbots track users' actions, gathering information about their preferences, habits and interactions over time. This data enables the chatbot to create a comprehensive profile for each user, which providers can sell to advertisers or data broker companies.
• Predictive analytics abuse. Data from chatbot interactions can infer sensitive information about users, including their health conditions, financial status and personal preferences. This information could lead to unfair treatment, such as in employment, insurance or loan decisions.

Most AI chatbot users are unaware of how companies use their information. This lack of transparency increases privacy concerns among chatbot users.

Strategies for AI chatbot privacy protection

To mitigate privacy concerns, both organizations and individual users have a role to play.

Individual users can adopt the following strategies to bolster privacy protection:

• Limit data sharing. Don't share sensitive information with AI chatbots.
• Understand chat privacy terms. Each AI chatbot has its own privacy terms and usage agreements. Instead of scrolling through agreements to click the "Accept" button, read the sections about data collection practices. This often includes data storage timetables and details on who has access to data, such as third-party advertisers or data brokers.
• Claim your privacy rights. Under GDPR and similar national data protection regulations, users have the right to request access to the information stored by AI chatbots. Users who are concerned about the amount of information they shared with a chatbot can request access to their historical records and request the deletion of any sensitive information.

Organizations should also implement the following four privacy best practices when using AI chatbots in their operations:

1. Data anonymizing techniques

Because users' conversations with chatbots might contain sensitive information, such information should be anonymized to prevent unauthorized access and misuse. Most commercial chatbot services have explicit policies regarding using conversation data for model training; however, organizations should still implement protective measures.

Organizations can implement the following two data anonymization techniques to safeguard user data stored in chatbot conversations:

• Data masking. Mask sensitive data that is stored in chatbot systems. For example, replace users' credit card numbers with XXXX and maintain the original one for secure processing purposes. There are various tools that offer data masking capabilities. Consider dynamic data masking, which shows different data to users based on their access permissions.
• Differential privacy. The differential privacy technique adds calibrated noise to sensitive data. This facilitates the release of statistical information about a data set while protecting the privacy of individuals within that data set. This technique is beneficial when organizations need to share aggregate insights from chatbot interactions without exposing individual user information.

2. Encryption

Chatbot data should be encrypted across its entire lifecycle. This includes the following:

• Use secure encryption protocols, such as the Signal protocol, to encrypt data in transit when it is exchanged between users and the chatbot processing servers.
• Use encryption to secure data at rest to protect stored chatbot conversation logs.

3. Strong access controls

Implementing robust authentication techniques ensures that only authorized entities can access chatbot systems and user data. Implement strong access controls using the following methods:

• A zero-trust architecture within the IT environment ensures every request is authenticated before it is granted access. For chatbot systems, this means authenticating human users, API calls and other microservices communications.
• Role-based access control can manage access for different users and systems accessing the chatbot system. Define roles for chatbot administrators, chatbot operators and customer service representatives.
• Enforce MFA to protect access to chatbot administrative dashboards and sensitive areas.

4. User consent management systems

Organizations implement consent management to increase customer trust in their services and ensure compliance with privacy regulations.

With AI chatbots, consent management enables users to control how their data is processed. For instance, a typical chatbot consent management system should enable users to consent to the following data practices:

• The use of an AI chatbot to execute their service.
• The storage of chatbot conversation data to personalize service.
• The sharing of chatbot conversation data and metadata with third parties.
• The use of chatbot conversation data to train AI models.

There are various consent management platforms that organizations can choose from. Prominent options include Onetrust, Cookiebot and TrustArc.

Nihad A. Hassan is an independent cybersecurity consultant, expert in digital forensics and cyber open source intelligence, blogger, and book author. Hassan has been actively researching various areas of information security for more than 15 years and has developed numerous cybersecurity education courses and technical guides.