Over the past year, generative AI applications have risen significantly with various use cases. The rise of ChatGPT has proven a revolutionary step towards gen-AI models. These models use large language models (LLMs) to generate images or natural language text content. Many generative AI applications have evolved to render high-quality images or videos, well-written content, understand an article to construct a summary, perform translation, etc.
Apart from all the benefits & use cases generative AI delivers to our dynamic working ecosystem, it poses significant security challenges. Enterprises should consider these security challenges and address them with the help of gen-AI administrators & AI developers involved in a particular project. This article will highlight some essential security best practices used in Azure.
Various Security Challenges Associated with Generative AI
Generative AI uses various AI techniques like generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers. With a ton of automation and benefits, generative AI invites a lot of security challenges that contrast with grave concerns. Here are some of the security challenges of generative AI.
- They lack the robustness of Large Language Model (LLM) services. Also, the reliability of gen-AI models with real-world scenarios can be formidable because of their complexity and susceptibility to unexpected inputs or conditions.
- Data used in training generative AI models often raises questions about the confidentiality and misuse of data. Enterprises remain concerned about whether the data used for training these large AI models reveals the confidentiality & integrity of users’ data.
- Stealing the AI model is another significant concern for generative AI teams and enterprises developing them. Modern cybercriminals often attempt to extract or clone the generative model. Such attempt leads to unauthorized replication or distribution of gen-AI model.
- Enterprises dealing with generative AI models often fail to audit & monitor the LLM behavior & outputs appropriately. These checks are mandatory to gauge the accuracy, compliance, & quality of gen-AI models.
- Biasness and fairness while developing the generative AI model is another significant concern tempting AI & security researchers. Biased or unfair outcomes spawned by the generative AI model can steer to discrimination or misleading results.
Data Processing in Azure - OpenAI Services
Azure and OpenAI prioritize data security when storing & processing them for generative model development. Azure designed its security architecture robustly to protect all user data against unauthorized access. They ensure that privacy and security remain intact & in line with Microsoft’s data safety policies & norms.
All the user data inputted while dealing with AI models – the responses – users’ data, remain private. Azure does not use these data unless the user chooses to utilize those data to train customized AI models. Azure OpenAI operates on input data such as prompts and subsequent AI-generated responses. Additionally, it handles data submitted for training customized AI models.
Customizing generative AI models is an assistance feature that the company handles with care. It is because of the user data used for training the gen-AI model. To avoid inappropriate or abusive data within the training model, Azure & OpenAI offer robust content filtering & monitoring strategies. OpenAI also follows strict guidelines to ensure that the content generation complies with all ethical guidelines. It also assures that nothing detrimental slips through.
Best Security Practices for Gen-AI in Azure
If any generative AI-based app development company follows a layered approach mingled with the Zero Trust framework, it can undoubtedly create a robust security strategy for LLMs. In this section, we will encounter some noteworthy security best practices Azure follows while developing and managing gen-AI applications.
Role-Based Access Controls (RBAC)
It is a prevalent access regulatory method for computers and network resources where the system enables role-based access for individual users within an organization. Azure services use RBAC to manage access to data for different users. It also allows network & cloud admins to assign appropriate permissions to AI developers and engineers & restrict access based on their roles and responsibilities.
Data classification with sensitivity
Enterprises that use real-time data for training LLMs and other AI models should classify and label the data based on sensitivity. Enterprises should recognize all sensitive data, such as financial records, Personally Identifiable Information (PII), Intellectual Property (IP), proprietary information, etc., and protect them. Classifying & labeling data assets in a governance-consistent manner by implementing automatic categorization tools & algorithms is also a great practice towards security for gen-AI.
Encrypting data at rest & in motion
Azure OpenAI also leverages the power of encryption so that no unauthorized users can understand the information used for training gen-AI models. With the help of encryption, enterprises dealing with sensitive data such as PII, training datasets, generated samples, model parameters, etc., can protect them during storage and in transit. With encryption key rotation & robust encryption key management practices, enterprises can bolster the overall security of gen-AI data from threats.
Data Masking and Redaction
Another best practice to protect data used in training generative AI models & LLM applications is to enforce the data masking technique. Data masking or redaction is a technique used in cybersecurity to conceal acute & critical data. It also supersedes the original data with obfuscated ones in non-production environments. Thus, even if an illegitimate user gains access to these training datasets – used for testing or troubleshooting, they cannot utilize or steal them for other illicit purposes.
Threat detection and monitoring
Another essential technique that Azure and OpenAI use to prevent cyber attacks on generative AI systems and their data is continuous threat detection and monitoring. Azure uses Azure Defender for detecting and reacting to security threats. It also comes with alert & monitoring mechanisms that can identify breaches & suspicious activities. Another advanced threat detection and response tool that Azure and OpenAI use is the Azure Sentinel. This tool centralizes all threats and reduces the investigation efforts.
Privacy and security tests for compliance
Enterprises dealing with generative AI application development should conduct recurring security assessments. These security assessments should contain checks on data privacy efforts, security compliance, vulnerability checking, and penetration testing. Through such checks, enterprises can identify & resolve vulnerabilities in their LLM applications. These tests also help security experts closely monitor whether the LLM projects comply with the GDPR or HIPAA regulations.
Conclusion
We hope this article catered to a concrete idea of numerous best practices enterprises can use to treat data cautiously and the security of gen-AI models. Again, a segregated approach by implementing RBAC and the Zero Trust principle can help diminish the risk profile by restraining exposure to datasets and trained models. Periodic audits and reviews of various security verticals can also help increase compliance standards & minimize the blast radius of generative AI systems.
Here’s where VE3 can help you. With our expert security & artificial intelligence solutions, we can assist you in enhancing enhances data security for enterprises like yours, employing generative AI models. Our advanced tools facilitate robust access controls and real-time anomaly detection, ensuring compliance with RBAC and Zero Trust principles while minimizing risks. To know more, explore our innovative digital solutions or contact us directly.
