Building secure chatbots: Best practices that ensure privacy

From customer support to knowledge retrieval and appointment scheduling, chatbots deal with vast amounts of sensitive data. As their adoption goes mainstream, you must understand the associated security and privacy concerns and how to avoid hacking and other vulnerabilities. This article looks at the strategies and techniques that ensure your chatbots do their job without compromising sensitive information.

Before we dive into the security aspects, let’s understand how a chatbot functions. A Chatbot simulates human-like conversations to provide automated assistance or information. Chatbots range from simple rule-based systems that follow predefined instructions to more advanced AI-powered models that employ machine learning and deep learning algorithms to learn from user interactions and improve over time.

Decoding the chatbot architecture 

Users interact with the chatbot through a mobile app or a browser. While generic information doesn't require user authentication, accessing sensitive information is mandatory. Once the user authenticates, the chatbot provides the required information. The diagram above shows that an encrypted database stores sensitive information. Machine learning components enable us to offer specific customized responses for an improved user experience for each user.

Ensuring security in chatbots 

Considering the security aspects of the chatbot solution in the early development stages helps protect it from malicious users. While incorporating security measures in the chatbot during development helps, you must follow it up with continuous testing during the development and deployment phases. Continuous testing is essential to identify new vulnerabilities in different products.

ChatGPT had a similar data breach in the Redis open-source library earlier this year. Redis is employed by OpenAI for caching user information, enabling quicker retrieval and accessibility. This vulnerability allowed users to see the chat history of other active users. ChatGPT acknowledged this issue in May 2023 and resolved it in the latest release.

Security measures for different stages of chatbot development

 You can refer to the following security standards to ensure the security testing of chatbots: 

• OWASP Top 10: The OWASP Top 10 for LLMs comprises a compilation of the most severe vulnerabilities detected in applications that use LLMs (Language Learning Models).
• SANS 25: The SANS Top 25 compiles the most critical software errors identified within the Common Weakness Enumeration (CWE) framework.
• Data security and privacy standards: Different security standards govern the protection of personal data and the privacy rights of individuals in different geographies. If a chatbot handles user data, it must comply with various standards, such as GDPR (General Data Protection Regulation) for the European Union region and DPA (Data Protection Act 2023) for the Indian region. 

Following these security measures is highly recommended while building a chatbot.

However, in case of a time constraint, you must conduct penetration testing before taking the chatbot live. 

Techniques to secure chatbots 

You can follow the following techniques to ensure security and privacy in your chatbots:

Threat modeling: Threat modeling is a structured approach to identify and analyze potential security threats and risks in a chatbot system, application, or digital environment. It is an essential part of the security design and risk assessment process that helps you proactively identify and mitigate security vulnerabilities before the attackers exploit them.

Threat modeling helps you identify and understand how an attacker might target the chatbot and the potential impact of such an attack on an organization's assets and data.

There are multiple threat modeling techniques like STRIDE (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege), DREAD (Damage, Reproducibility, Exploitability, Affected users, Discoverability), or PASTA (Process for Attack Simulation and Threat Analysis).

Vulnerability assessment is the systematic approach to identifying security vulnerabilities using automated tools. Once you have identified the vulnerability, you must identify the risk level and the time frame to address it without affecting daily operations.

Organizations enduring persistent cyberattacks can derive significant value from routine vulnerability assessments as cyber adversaries continuously seek exploitable weaknesses to infiltrate applications, systems, and potentially entire networks.

Penetration testing: Pen Test or penetration testing is a multi-layered security assessment methodology that combines manual test cases and automation tools. The primary goal of penetration testing is to identify and exploit vulnerabilities in a controlled and lawful manner before the exploitation by malicious hackers.

The penetration testing scope varies depending on organizational needs, infrastructure complexity, and specific security concerns. It covers multiple aspects of chatbot security, such as: 

• Addressing business logic flaws and design flaws, which are vulnerabilities or weaknesses in a software application's logical operations and workflows and the software's architecture and structure, respectively.
• Checking for misconfiguration caused due to incorrect or suboptimal settings, configurations, or options in software, systems, or devices.
• Creating test cases to check for security vulnerabilities like SQL injection, broken authentication, and XSS attacks by combining OWASP Top 10 and SANS 25 security issues for thorough security testing.
• Developing privacy and data protection scenarios to assess how applications handle and safeguard user data in compliance with relevant privacy regulations.
• Intercepting and relaying communication between two parties without their knowledge.
•  User experience testing (UX testing) ensures that user interfaces and workflows do not expose vulnerabilities, compromise user data, or lead to security breaches.
• Application Programming Interfaces testing to evaluate APIs for identifying and mitigating potential vulnerabilities.

Chatbot security and mitigation strategy: Best practices

Here are some best practices and mitigation strategies for chatbot security:

• Use OWASP Top 10 for LLM: Incorporate the  OWASP Top 10 principles while building the chatbot, which involves handling user inputs and managing data, understanding the potential risks and vulnerabilities, and taking appropriate steps for mitigation. Additionally, consider the specific context of your chatbot and its interactions.
• Authentication and authorization: Implement robust authentication mechanisms to verify user identities and grant appropriate access privileges, preventing unauthorized access to administrative functions. Ensure end-to-end encryption, multi-factor authentication, and intent-level authorization.
• Input validation: Validate and sanitize user inputs to prevent common security threats like SQL injection or cross-site scripting (XSS) attacks.
• Regular updates and patches: Keep the chatbot software and underlying frameworks up to date. Regularly apply security patches to address known vulnerabilities.
• Secure APIs and third-party integration: If integrating external services or APIs, employ secure API key management and rate limiting to prevent misuse. Verify security practices of third-party chatbot platforms or integrations to ensure they meet security standards.
• Limit data collection and user education: Only collect essential user data and avoid storing sensitive information unless necessary. And educate users on the required information type and alert them against sharing sensitive data or passwords.
• Secure data storage:  Use secure and encrypted databases to protect the information. Utilize data encryption techniques to secure sensitive data during transmission and storage to safeguard user information from unauthorized access.
• Regular security audits: Conduct periodic security and penetration testing to identify and address potential vulnerabilities.
• Rate limiting and CAPTCHA: Employ rate limiting and CAPTCHA mechanisms to prevent brute-force attacks and spam.
• Machine learning security: Leverage machine learning algorithms to ensure protection against adversarial attacks and biases.
• Self-destructive message: Use self-destructive messages to prevent the exposure of sensitive data in the event of unauthorized access. It's essential to note that self-destructive messages can help maintain privacy but are not foolproof. 

Build secure chatbots with Nagarro

As chatbots penetrate all facets of business operations, Nagarro works with leading organizations to enhance the security of these systems. We recently worked with a workforce management company where we did grey box model penetration testing for their chatbot integrated with MS Teams.

Nagarro's security team performed effective security testing to identify vulnerabilities in the chatbot, directly impacting the security stance of client and user privacy. We then submitted an assessment report, remediations, and mitigations for major potential security risks. This helped the client secure vast amounts of sensitive user data susceptible to misuse by hackers.

Remember, cybersecurity is an ongoing process, and vigilance is vital to safeguard your chatbot and user data. Implementing a robust security strategy can protect against evolving threats.

Are you, too, considering cybersecurity measures for your business? Let's talk!