How do you protect your NLP data and models?

There are various methods available to secure your data. When it comes to secure data transmission over the internet, the HTTPS protocol plays a vital role. By utilizing SSL/TLS encryption, HTTPS ensures that data exchanged between a client and a server remains confidential and protected from unauthorized interception. On the other hand, encryption at rest focuses on safeguarding database stored data when it is not actively in use. Both methods can ensure comprehensive data security throughout application lifecycle.

The data used for training, testing, and validating the models should be stored and protected in an encrypted storage location if it is curated from private sources. The underlying technology can be as simple as a storage account or a NoSQL database. The decision factor here should be the discoverability of the data. Additional safeguards are required if PII data is in the text data files. Redacting such data elements or the Chief Data Office guidance should be adopted here. Between the data storage to the model-building infrastructure, it is essential to have role-based access controls and in-transit data encryption in place.

Use strong encryption and vault to store the keys. Limit the access to the vault and enable approval, audit and time driven access, ****encrypt your backups**** Make sure data in flight and rest is protected. anonymize and remove personally identifiable information (PII) from the data used to train models conducts regular security audits to identify and address potential vulnerabilities in the systems Encrypt and decrypt parameters during integration

To protect your NLP data and models: encrypt data, control access, use secure storage and network communication, update software regularly, follow secure coding practices, respect user privacy, monitor and audit, train employees, and comply with legal and ethical requirements.

Reducing the visibility to avoid any public access. Training company users on what they should or not ask or share in the NLP application.

Securing NLP models is key! There are different techniques to secure NLP models. In 2022, University of California proposed a new technique called DW Distillation Resistant Watermaking to prevent NLP models by stolen by querying and distilling. The method inject watermarks into victim's prediction probability that turns into secure keys and probe suspect model for the keys.

Centralized AI/ML Infrastructure for building, maintaining, and orchestrating AI/ML models is key to achieving the goal. Such a strategy will ensure visibility, access control, and enterprise protection for models built. While this is a foundational approach, there are advanced methodologies such as 'watermarking models.' Watermarks are handy in the event of theft and re-use of the models. As the field of AI explodes, there is a strong requirement for model watermarking in Open Source and Enterprise AI/ML models.

Secure Data from NLP 1) Post sensitive data in combination of encryptionmultiple coding languages 2)To limit data from downloading (from single IP address only download 1 to 10 records) 3)Add images, watermarks to save your secure data from stolen

Penetration testing is one way to monitor the application security. It is a proactive security assessment technique used to simulate attacks on different systems. The goal is to identify vulnerabilities that malicious actors could exploit. By mimicking real-world attack scenarios, penetration testing helps discover weaknesses and address them before they can be targeted by cybercriminals.

Observability in AI/ML platforms and models in production is required. As your models deliver critical decisions, it is human to create adversarial examples to influence the model's decision. Observing the input data frequency, and validating the sources of data flowing to inference endpoints, along with drift and decay monitoring, is required. Partnering with Sitte Reliability Engineering(SRE) and the Cyber Risk team during the AI/Ml model deployment ensures success. At the same time, do not forget industry standard AI/Ml model monitoring concepts.

In the early days of web technology, everything was primarily presented in plain text. However, as we progressed, we soon realized the need for encryption to safeguard sensitive information. Just as data became a valuable asset in the digital realm, trained models emerged as revenue-generating assets. Therefore, it is imperative that all stakeholders involved in the development, deployment, and utilization of these models are well-educated and cautious when it comes to protecting them. Just as we safeguarded data, we must now prioritize the protection of these valuable AI models to ensure their integrity, security, and continued success.

It is applicable for the larger AI workstream to educate the team about Data Protection, Intellectual Property (IP), Software licensing, and Secure Application Designs. Fostering a culture of awareness, responsibility, and risk understanding, where to find support and accountability empowers the team.

Consulting an expert becomes even more imperative when your company lacks expertise in NLP. While solutions like ChatGPT offer incredible convenience and may assist in solution development, it is crucial to recognize the limitations of these tools and the potential risks they pose. An expert in NLP will have a deep understanding of the intricacies involved, nuances, privacy concerns, and compliance with rules and regulations. They possess the knowledge and experience to address and mitigate biases that may arise in data and models. By collaborating with an expert, your company can ensure that the implementation of complex NLP solutions is done with utmost care, accuracy, and in alignment with the highest standards of privacy and security.

Secure Data Transmission: a. Secure Communication Protocols: When transmitting NLP data between systems or networks, employ secure communication protocols such as HTTPS (Hypertext Transfer Protocol Secure) or SSH (Secure Shell) to encrypt the data and prevent unauthorized interception. b. API Security: If exposing NLP models via APIs (Application Programming Interfaces), ensure that proper authentication and authorization mechanisms are in place. Implement techniques like API keys, OAuth, or JSON Web Tokens (JWT) to authenticate and control access to the APIs.