ChatGPT and Identity Security: Breaking down Cybersecurity risks of AI

It is fair to say that few technologies have grabbed more news headlines in 2023 than ChatGPT. As an artificial intelligence (AI) language model, ChatGPT is a powerful tool that has the potential to revolutionize multiple business areas, including, but not limited to, marketing, operations, engineering, risk management, legal and employee optimization. However, as with any technology, there are potential risks and unintended consequences associated with the use of ChatGPT and similar AI applications, particularly in the area of identity security.

As technology continues to advance, the use of AI is becoming more widespread in various fields, including finance, health care, retail and education, among others. AI language models, such as ChatGPT, are designed to understand human language and respond to user inquiries, making them highly effective tools for businesses and organizations to automate tasks, streamline processes and improve customer experiences. However, the use of AI technology also poses significant Identity & Access Management (IAM) risks that need to be addressed to ensure data security and privacy.

This article examines the identity security-related risks of artificial intelligence, particularly focusing on the ChatGPT language model and similar applications. We will discuss the different ways in which AI technology, such as ChatGPT, can pose identity security risks and explore how Identity & Access Management solutions can potentially mitigate these risks.

Cyber Security Risks Are Becoming Apparent

One of the primary identity security risks associated with ChatGPT is the potential for the model to be manipulated or exploited by malicious actors. ChatGPT is a machine learning model that relies on a large dataset of human-generated text to generate responses to user queries. If an attacker gains access to this dataset or is able to manipulate the input to the model, they could potentially use ChatGPT to generate fraudulent or misleading responses. For example, an attacker could use ChatGPT to generate convincing phishing emails that appear to come from a trusted source, such as a bank or government agency. The attacker could use the model to craft messages that are personalized to the victim's interests and appear to be legitimate, making it more likely that the victim will be deceived into providing sensitive information.

To mitigate this risk, it is important to ensure that access to any AI tools’ training data is tightly controlled and that the model is trained on high-quality, trustworthy data. Additionally, security measures such as Multi-Factor Authentication (MFA) and context-aware adaptive authentication, as well as encryption, should be used to protect the model and its inputs.

Another identity security risk associated with tools such as ChatGPT is the potential for the model to unintentionally reveal sensitive information about users. ChatGPT, for example, is designed to generate responses based on the context of the input it receives, which can include personal information, such as names, addresses and other identifying details. If this information is not properly safeguarded, it could be unintentionally revealed in the responses generated by ChatGPT. For example, if a user inputs a question about their medical history or financial situation, ChatGPT could potentially generate responses that include sensitive information that the user did not intend to share.

To address this risk, it is crucial that ChatGPT is properly configured to handle sensitive information. This may involve implementing additional security measures, such as Privileged Access Management (PAM), data masking or anonymization, as well as developing policies and procedures for handling sensitive information in a secure and responsible manner.

A related risk is the potential for bias or discrimination to be unintentionally introduced into the responses generated by ChatGPT. Machine learning models are trained on historical data, which can reflect biases and discriminatory practices that existed in the past and may be prevalent today. If this bias is not properly addressed, it can be perpetuated and even amplified by the model.

For example, if ChatGPT is trained on a dataset that includes biased or discriminatory language, it may inadvertently generate responses that reflect and reinforce these biases. This could have negative consequences for users who belong to marginalized groups or who are already at risk of discrimination. At another level, biased datasets could influence global markets and even political elections!

To manage this risk, it is important to carefully consider the training data used to train ChatGPT and to take steps to mitigate any potential biases. This may involve carefully curating the training data to ensure that it is diverse and representative of the population, as well as protecting datasets with modern security techniques, including consideration of data access management.

Another identity security risk associated with ChatGPT is the potential for the model to be used to generate deepfake content. Deepfakes are synthetic media that are generated using artificial intelligence and machine learning techniques. They can be used to create highly convincing but fake images, videos or audio recordings.

One of the key identity security risks of AI language models is the potential for data breach at the AI applications vendor (think of a large-scale breach at OpenAI, Google or Microsoft, for example). Artificial intelligence language models are designed to store and process large amounts of data, including user information such as names, email addresses and phone numbers. If this data falls into the wrong hands, it can be used for malicious purposes, such as identity theft, phishing attacks and other forms of cybercrime. Additionally, the use of AI technology introduces new attack vectors for hackers and cybercriminals, who can exploit vulnerabilities in the machine learning algorithms or manipulate the data inputs to generate false responses.

Potential Identity & Access Management Solutions for AI Technology

AI language models like ChatGPT use large datasets and machine learning algorithms to understand natural language and generate responses. While these models are designed to be trained on large datasets to improve their accuracy, they can also be vulnerable to unwanted biases, breaches and misuse. To mitigate the identity security risks of AI technology, businesses and organizations must implement robust Identity & Access Management solutions that can protect model data (on the vendor side) and user data (on both the vendor and the consumer side) and ensure the security, integrity and accuracy of the AI system.

One approach is to implement context-aware adaptive Multi-Factor Authentication to control access to the AI language model and its users’ accounts. This type of MFA requires users to provide multiple forms of identification before accessing the system, such as a password, a fingerprint scan or a one-time passcode sent to their mobile device. At the same time, it uses AI to monitor user behavior, identify anomalies and prevent risk in real time for advanced threat defense. This can help prevent unauthorized access to the system and reduce the risk of data breaches.

Another approach is to implement encryption and anonymization techniques to protect user data. Encryption ensures that data is secure and protected from unauthorized access by converting it into an unreadable format that can only be decrypted by authorized users. Anonymization, on the other hand, removes any identifying information from the data, such as names or email addresses, to prevent it from being linked to specific individuals. As use of AI tools starts to proliferate in business, Privileged Access Management technologies are going to become important, ensuring that empowered users of the tools and custodians of the data that they rely on are protected from credential theft. At the same time, it is critical that both consumers of AI technologies and their suppliers take identity management very seriously as these technologies become mainstay.

Additionally, businesses and organizations should conduct regular audits and assessments of their AI systems to identify any potential vulnerabilities or security gaps. This can help ensure that the AI system is functioning as intended and that any security risks are addressed before they can be exploited. Identity management best practices, such as the adoption of a least privilege model, should be seriously considered by AI tool providers and their users. This technique ensures that users only have access to the resources they need to perform their duties. This reduces the risk of insider threats, accidental or intentional data breaches and other security incidents.

A Call to Action for AI Providers

There are several best practices for implementing identity security with AI technology that will help this exciting industry growth, provide value and lower the risk associated with some of the exposures discussed here. Here are three key ones:

  1. Access Control: Access control is critical for identity security. It involves limiting who can access your system, data or applications and is essential to providing access to only those who need it to perform their job functions. AI technologies should have proper access control measures in place to ensure that only authorized users can access the system and data.
  2. Multi-Factor Authentication: MFA is a security best practice that requires users to provide two or more forms of authentication before gaining access to a system or application. This approach provides an additional layer of security to prevent unauthorized access. AI technologies should implement MFA for all users, especially those who have administrative access.
  3. Monitoring and Logging: Monitoring and logging are critical for identifying and responding to security incidents in a timely manner. AI technologies should log all user activity and access attempts and provide real time alerts for suspicious activity. The system should also be able to identify potential threats and take appropriate action to prevent them from causing harm. Regularly reviewing logs is also important for identifying potential security issues and remediation.

Implementing these identity security best practices will help ensure that AI technologies, like ChatGPT, are secure and protect the confidentiality, integrity and availability of information.


While AI language models like ChatGPT have the potential to revolutionize various fields and industries, they also pose significant identity security risks that need to be addressed. By implementing robust Identity & Access Management solutions on both the consumer and the supplier side and adopting a holistic approach to cybersecurity, businesses and organizations can ensure the integrity and accuracy of their AI systems, protect user data and maintain customer trust. AI is evolving rapidly. The time for security considerations, including Identity & Access Management is NOW.

One Identity specializes in many of the tools and best practices discussed in this article. For further information, please refer to the following web resources:

Access Management

Privileged Access Management

Identity Governance & Administration

Active Directory Management

To sign up for a free trail of any or all of these technologies, this site for Access Management trials or this site for Identity Governance, Privileged Access Management and Active Directory Management trials.

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