In an age defined by rapid technological advancements, the integration of generative AI features into applications has emerged as a potent tool for organizations seeking to stay on the cutting edge of innovation. The likes of ChatGPT, Bard, Dall-E, and GitHub Copilot have redefined how we interact with AI, offering transformative capabilities that enhance customer experiences, boost employee productivity, and fuel creative innovation across industries.
However, like any technological progression, the integration of generative AI services introduces its own set of security considerations, particularly relevant to private organizations leveraging public APIs. The following areas should be considered when consuming APIs for generative AI tools:
- Data Privacy, Confidentiality & Retention
- API Security
- Model Security
- AI Regulations
1. Data Privacy, Confidentiality & Retention
Data privacy is crucial for keeping personal identifiable information (PII) data secure from unauthorized access or misuse. With rising personal data collection by companies, ensuring data security is crucial. To avoid legal penalties and harm to reputation, companies must abide by data privacy laws like General Data Protection Regulation (GDPR) [EU], California Consumer Privacy Act (CCPA) [California, US] or the Digital Personal Data Protection Act (DPDPA) [India].
It is important to remember that one should avoid providing sensitive or personally identifiable information to the API when querying public generative AI APIs. Customer data should be appropriately redacted when interacting with third party APIs if there are no binding agreements with the vendor on customer data confidentiality.
Across the globe, data retention regulatory requirements dictate how organizations manage and retain data related to their operations. They specify data retention periods depending on the nature of the data and the purpose for which it was collected. One should be aware of how long the API provider retains the data that is sent and ensure that it complies with the organization’s data retention policies.
2. API Security
Using generative AI API exposes an organization to the same security issues as any API access. The following points should be considered:
- API key management: API keys should be securely stored and rotated on a regular basis to avoid being exposed and misused.
- Injection attacks: Any input taken from users should be properly validated and sanitized to prevent injection attacks such as SQL or XSS injections.
- Denial of service attacks: Malicious users can potentially abuse your API by sending a high volume of requests, leading to a DoS situation where your application becomes overwhelmed and unresponsive.
- Man-in-the-middle attacks: If communication between your application infrastructure and the generative AI platform servers is not properly encrypted and secure, attackers could intercept and manipulate data traffic. Always use secure communication protocols.
- API abuse: Third party APIs may have usage limitations and rate limits. If not properly enforced, this can lead to excessive usage charges or abuse by malicious actors. Implement proper monitoring and access controls to prevent abuse.
- Vulnerable dependencies: Many applications rely on third-party libraries and frameworks. Ensure that your application and dependencies are up to date and do not have known security vulnerabilities.
3. Model Security
When accessing external AI APIs, while the AI model is not directly accessible, malicious users can execute certain steps that can bring in bias or fool the model into changing its responses to the same queries over a period. Your application needs to continuously review and moderate the queries to ensure that the following considerations are covered:
- Model Bias Manipulation: Data poisoning attacks involve injecting malicious data into the training dataset of a machine learning model. This can cause the model to learn incorrect patterns, which can lead to security vulnerabilities. If your queries to the API include past data from the user, you should consider strengthening this aspect.
- Model Inversion Attacks: Model inversion attacks involve using a machine learning model to infer sensitive information about the data it was trained on. This could allow an attacker to learn personal information about users, such as their passwords or credit card numbers. Make sure all data being passed to the model through the API is sanitized for any sensitive data.
- Adversarial Prompting: Adversarial prompting are slightly modified inputs that are designed to fool a machine learning model. This can be used to bypass security controls or cause the model to make incorrect predictions. Use the generative AI APIs in such a way that prompt injections do not propagate into the queries that are sent to the APIs. This will avoid any targeted manipulation of the application behavior from a malicious user.
4. AI regulations
The fast progress of AI has resulted in the need for regulations to address a wide range of societal concerns with different jurisdictions taking varying approaches. AI regulation began with the GDPR in 2018 and now has manifested into separate regulations like the AI Act (AIA) 2021 [EU], Artificial Intelligence and Data Act (AIDA) 2022 [Canada]. The common threads across all these regulations cover areas like fairness, non-discrimination, transparency and explainability, apart from privacy and security concerns. Here are some high-level pointers on aligning with the current set of AI regulations:
- Be transparent to users: Make sure you are open to users and explicitly call out use of generative AI APIs in your application. Also, users should be aware of how their data is being used to influence the outcomes in the application.
- Maintain chain of ‘explainability’: Make sure you keep track and present to users the chain of explainability in the use of AI. Explainability covers areas like what are the inputs and what training data is being provided to the model(s) to generate the output shown to the user.
- Ensure user safety: Use of AI carries physical risks in examples of autonomous vehicles and robotics. There are also other safety risks that get introduced in the form of privacy & data leakages. The component used in your application to access and process generative AI content should not pose such a risk to individual users.
- Ensure fairness and non-discrimination: AI systems can inherit biases present in their training data, leading to unfair or discriminatory outcomes. Ensure that your application does not enable such biases by propagating user inputs directly to the generative AI APIs.
You can explore the specific AI regulations in detail to better understand the requirements from a user servicing perspective.
In conclusion, by addressing the above security considerations comprehensively, businesses and developers can navigate the AI landscape confidently while safeguarding data, systems, and regulatory compliance in an ever-evolving digital age.
