[Blog] Establishing Trusted Data for AI, ML, & Digital Twin Models

In today’s data-driven world, artificial intelligence (AI), machine learning (ML), and digital twin technologies are transforming industries, revolutionizing processes, and reshaping enterprise business environments. With more than 328 million terabytes of data created each day, it has become the new “oil” – providing the energy needed to fuel next generation digital systems.

However, the effectiveness and reliability of these technologies heavily depends on the quality and trustworthiness of the data they are built upon. Establishing trusted data is not just a prerequisite, it’s the cornerstone of successful AI, ML, and digital twin models. This explosion of data also presents a wide range of safety and security ramifications for developers to navigate. As more data transitions between devices, sensors, and systems, there is greater potential for breaches and attacks. Additionally, with more data driving the advancement of AI, ML and digital twins, it increases the risk of a "technological singularity" event where machine intelligence becomes superior to that of humans, resulting in unforeseeable outcomes.

These implications raise additional concerns about data, particularly around extracting and refining data in a trusted and responsible way. Since the data economy will only continue to grow, the key question is: How can we “trust” data today?

Defining Trusted Data and its Challenges with AI, ML, and Digital Twin Models

Trusted data is data that stakeholders can confidently use to make decisions, develop models, and drive innovation. While “trust” is a human term, the concept of “trust” when it comes to data often refers to data provenance. Data provenance is a documented trail that clearly shows the origin and history of data, including where it came from, how it was created, and how it has been modified over time. It is a crucial element to ensure data quality and integrity. Establishing data provenances enables an unbroken chain of trust from the origin of data to the current state of use.

The majority of AI, ML, and digital twin models today do not have effective data provenance. In fact, very few – if any – have any data provenance requirements, enforcement mechanisms, or broadly approved standards to follow. This leaves today’s major AI systems vulnerable to data poisoning, malicious training, and data drift susceptibilities. Data poisoning and malicious training refer to the intentional and deliberate manipulation of training data to compromise the performance or integrity of AI and ML models in a harmful way. By injecting false data into algorithms or datasets, attackers can influence biases, vulnerabilities, and the accuracy of patterns or predictions.

Additionally, when data provenance is not established, it can lead to data drift. This occurs when the properties of the data used to train AI and ML models change over time – whether that’s due to underlying data distribution, environmental changes, or user behavior – leading to a decline in the model's performance.

Solutions for Developing Greater Data Provenance in AI, ML, and Digital Twin Models

As AI and ML models and digital twins become more ubiquitous, there must be a greater focus on data provenance and building trust. To establish this, there are three key areas of focus:

1. Developing guidelines and standards,
2. Implementing immutable data options, and
3. Establishing compliance and enforcement mechanisms.

Guidelines and standards

Industry and government standards bodies must begin creating and implementing data provenance guidelines that, at a minimum, require some level of disclosure as to the data provenance composure of the model. Take, for example, a level-tiered system. In this system, Level 0 could indicate no data provenance, Level 1 denotes data provenance of origin, and Level 2 represents full data provenance with an unbroken chain of trust throughout the data’s lifecycle. AI/ML models and digital twins would report out the percentage level of compliance at each level.

Immutable data options

Immutable data options refer to data that cannot be altered or deleted after it has been recorded. Blockchain technology offers a solution to achieve immutable data due to its decentralized and distributed system. With a blockchain network, each transaction or piece of data is cryptographically linked to the previous one and, once a transaction is added to the blockchain, it becomes virtually impossible to modify or remove. This ensures the integrity and trustworthiness of the data and further establishes data provenance.

Compliance and enforcement mechanisms

Compliance and enforcement mechanisms are also needed to establish data provenance and provide confidence. With robust compliance measures, organizations can mitigate risks associated with data misuse and ensure transparency and accountability in data management processes. Incorporating independent third-party validation into compliance frameworks further enhances data provenance. By providing impartial assessments of adherence to standards and regulations, this reduces the potential for conflicts of interest and ensures integrity.

Additionally, as compliance criteria evolves, standards should shift too. This guarantees AI/ML models and digital twins are implementing the latest practices and security protocols, can adapt to threats, and maintain trust.

Leveraging FPGAs for Trusted Data Processing

When it comes to trusted data, the role of Field Programmable Gate Arrays (FPGAs) cannot be overstated. Particularly in establishing data provenance, FPGAs are uniquely positioned to offer several critical advantages for secure data processing.

Security Enhancements: Most prominently, FPGAs offer built-in security features – such as encryption and authentication mechanisms – that help safeguard data during processing. By incorporating FPGAs into data processing infrastructure, organizations can strengthen data security and mitigate the risk of cyber threats and data breaches.

Performance Optimization: By offloading data processing tasks to FPGAs, organizations can enhance the performance of AI, ML, and digital twin Models. With optimized workflows and high throughput, organizations can process large volumes of data and facilitate the efficient management and analysis of data across diverse sources.

Real-time Processing: FPGAs’ real-time processing capabilities enable organizations to analyze and respond to data streams with minimal latency. This is invaluable for data provenance as it ensures data transactions and transformations are recorded more promptly and that provenance records reflect the most up-to-date information.

Customization and Flexibility: Due to their highly customizable nature, FPGAs can be programmed or reprogrammed to perform specific tasks over time. This flexibility allows for optimized data processing pipelines that can capture and manage provenance information. It also enables organizations to adapt provenance mechanisms to their specific environments and requirements, enhancing the accuracy, completeness, and relevance of provenance records.

Enhancing AI, ML, and Digital Twins with Trusted Data

With more innovation and technological transformation on the horizon, data will only continue to be a critical component of our digitally-driven world. As a result, prioritizing the establishment of data provenance is paramount for enhancing trust and reliability in AI/ML and digital twin models. Through the implementation of guidelines and standards, immutable data options, and compliance mechanisms, developers can bolster confidence in the integrity and reliability of these technologies and ensure safety and security dependent outcomes are more deterministic.

By integrating FPGAs into data processing pipelines, organizations can unlock new levels of performance, flexibility, and security, laying a solution foundation for building trusted and reliable AI-driven solutions. To learn more about FPGAs and their role in data provenance in AI/ML and digital twin models, reach out to speak with the Lattice team today.