Mastering the Compliance Challenge in AI

Discover the significance of agility and compliance in today's evolving regulatory landscape and delve into how a federated learning approach, backed by robust governance, can empower organizations to innovatively and securely harness sensitive data for trusted AI solutions.
Published 6 October 2023

AI product development faces a complex intersection of innovation, security, and compliance. The stakes are high in regulated industries like healthcare and finance, where compliance isn't just a legal obligation – it’s crucial for building trust and integrity. Federated Learning* (FL) serves as a bridge, allowing ML engineers to send computations to vital data typically beyond their reach. By integrating governance, security, and privacy controls with this approach, FL lays a strong foundation for creating compliant ML models.

Data stays local: the first step

Often data is moved from its source to a centralized server for processing and model training, thereby exposing sensitive data to various risks such as risk to its confidentiality, integrity, or compliance with privacy regulations. Federated learning changes this by keeping data in its original location, providing multiple advantages:

  • Enhanced security and easier governance: Keeping sensitive data localized not only reduces the surface area for potential security threats but also simplifies governance. Without the need for transmission, risks of data interception or unauthorized access are significantly minimized.

  • Compliance-friendly: Federated learning inherently supports compliance with data protection and privacy laws such as GDPR, CCPA, and HIPAA, which have strict requirements around data movement and handling. By leaving the data where it is, you sidestep the complications around data transfer across jurisdictional boundaries.

  • Data integrity: The data remains in its original, secure environment, preserving its quality and metadata. This is vital for ensuring that machine learning models are trained on high-quality data and that data lineage is maintained for auditing purposes.

  • Data minimization: The decentralized architecture of federated learning processes less data than a centralized approach would. Instead of aggregating all data into one place, only essential model updates or insights are communicated, adhering to the principle of data minimization.

  • Data custodian trust: Crucially, keeping the data localized allows data custodians to maintain full control over their datasets. This trust is key for willingness to participate in machine learning initiatives, especially in sensitive or highly regulated sectors like healthcare or finance.

  • Real-world relevance: By allowing data to stay in its operational context, federated learning ensures that models are trained on real-world data distributions, thereby enhancing model relevance and performance.

For example, in healthcare applications such as medical imaging AI, FL enables model training without sharing or centralizing data. This not only supports compliance with many data privacy regulations, but it also enables model training on real-world data to meet FDA requirements. It also keeps data custodians in control of how their valuable data is used.

Governance: The backbone of federated learning

While federated learning has the advantage of keeping data localized, its real potential in regulated industries is unlocked when combined with robust governance mechanisms. The governance layer should serve as a sophisticated control system that provides data custodians with the level of granularity they need to align data usage with compliance requirements and privacy norms. For example:

Granular permissions and access control

One necessity is the ability to set granular permissions and access controls. This enables data custodians to specify who can do what with the data and the derived models. For example, permissions can be set to allow only particular types of computations, for a specific purpose, or to restrict access to certain groups within the organization.

Audit trails and oversight

Being able to trace any activity on the data is crucial for accountability. Audit trails record who accessed what data, when, and for what purpose. This is not just a strong deterrent against misuse but also a vital tool for analysis in case of a data breach or other security investigation.

Privacy controls

Privacy-enhancing technologies, such as differential privacy, can be integrated into the governance layer to provide an extra level of assurance. These technologies can minimize the risk of identifying individual data points in aggregated datasets, thereby reducing the chance of privacy violations. The governance layer can automatically enforce these privacy controls when models are being trained or queried.

Model approval and security

Before a model is allowed to run computations on the data, it should undergo a rigorous approval process managed by the data custodians. This process can include an analysis of the model’s architecture, purpose, and the types of data it will interact with. Once approved, the model should be monitored for drift or changes in behavior that might necessitate re-approval or adjustments.

Continual oversight

Advanced governance offers oversight capabilities, allowing data custodians to continually monitor the health and behavior of models. Anomalies can be flagged instantly, and appropriate actions can be taken before they escalate into more significant issues.

Policy adjustments

Regulations and compliance requirements are often changing necessitating policy adjustments. A robust governance layer should allow for updates to privacy policies, access controls, and other settings without requiring a complete system overhaul.

Model card catalogs

Model cards make it easier for compliance teams and ML engineers to understand the privacy implications of each model. These are standardized documents that accompany trained ML models, offering essential details about the model’s performance, limitations, and privacy controls in place.

By combining robust governance measures with federated learning, organizations can create a powerful framework that not only respects privacy and security but also unlocks new avenues for innovation in highly regulated sectors. The governance layer acts as the control plane for federated learning, ensuring that models approved to run are both allowed and secure, thus paving the way for compliant, efficient, and ethical AI product development.

The risks of model memory

Model memory – where a machine learning model retains information from the training data – can be a problem when it comes to data privacy and compliance.

For example, in the context of regulations such as GDPR, the memory of a model could breach "right to erasure" if it retains individual data points. Thus, the model itself could become subject to GDPR and inadvertently a compliance risk if the model was not re-trained.

However, the repercussions extend beyond potential violations of data subject rights. Unauthorized parties may obtain specific data points or personal records from the training data, or even identify individual participants based on the model's behavior. Such unintentional revelations not only jeopardize the privacy of individuals but also qualify as data breaches. Hence, while addressing data subject rights is essential, it is equally crucial to consider the broader implications of model behavior and the potential security lapses it might signify.

There are a few strategies to address these challenges:

  • Data minimization: reduce the risk of model memory issues by processing only essential data. By limiting the data fed into a model, the chances of inadvertently memorizing or revealing specifics are minimized, enhancing data privacy and mitigating potential breaches.

  • Privacy enhancing technologies (PETs) – implementing differential privacy, for example, during the training phase can help to obfuscate the contributions of individual data points, making it less likely that the model will remember or reveal sensitive information. Choosing the right PET for the type of data is, however, crucial.

  • Model auditing: Regularly auditing your models for data leakage and unintended memorization should be a part of your compliance strategy.

  • Retraining policies: In the event of a "right to erasure" request or a similar compliance need, have a defined procedure for identifying which models are affected and how they should be retrained or updated. This is a non-trivial task that requires an efficient model tracking and governance system.

  • Allowable computations: Data custodians should define what constitutes an "allowable computation", which can then be enforced programmatically to ensure that certain types of queries that might lead to data leakage cannot be run on the model.

  • Regular purging: A method to regularly purge or update the model to "forget" data that it shouldn't store anymore, should be built into the model's lifecycle. This helps maintain compliance, especially in changing regulatory landscapes.

Addressing the intricacies of model memory requires an end-to-end approach that brings together compliance, data ethics, and machine learning operations (MLOps) under a unified governance framework. When these elements are carefully managed, ML engineers and data custodians can work together to encourage innovation and compliance.

Keeping data custodians in the loop

For federated learning initiatives to truly thrive in regulated environments, ML product teams and data custodians need to work together. Data custodians carry the responsibility of maintaining compliance and data security and, understandably so, they have reservations about opening their datasets for ML.

Products like Apheris provide the governance, security, and privacy layer through features such as:

  • Model Registry: a catalog of models with detailed model cards outlining privacy and security evaluation and implementation. A privacy assessment highlights data leakage risks.

  • Governance Portal: establish and audit asset policies, allowing data custodians to set permissions and retain control down to the computational level. Inspect compute requests or automate job execution according to requirements.

  • Trust Center: A resource hub offering best practices and advice for navigating compliance.

Apheris product includes a Model Registry: categorized model cards including a privacy and security assessment.

When data custodians are informed, involved, and in control, they're more likely to be on board with enabling ML or FL on their data, providing access to previously siloed data to build better or new products.

Achieve compliance and innovate

By adopting federated learning with robust governance layers, organizations can balance innovation with compliance, reduce risks, and expedite their AI initiatives effectively.

However, navigating the intricate landscape of compliance in regulated industries is a shared responsibility. By fostering a collaborative relationship between ML product teams and data custodians, organizations not only pave the way for innovative solutions but also build trust and assurance that compliance is a top priority.

Key takeaways:

  • Localized data: federated learning inherently aligns with many regulation requirements by minimizing data movement.

  • Governance layers: additional governance, security, and privacy layers are crucial for maximizing the benefits of FL.

  • Model memory: a systematic approach to managing model memory can mitigate risks of data leakage or unauthorized access.

  • Integrated solutions: solutions like Apheris simplify the complexity of compliance in AI development.

Governed, private, secure computational access to federated data with Apheris

In a changing regulatory environment, being agile yet compliant is not just an aspiration but a critical business imperative. A well-executed federated learning strategy, underpinned by strong governance, can serve as a catalyst for organizations aiming to securely leverage their data assets for innovative, compliant, and trustworthy AI.

*Federated learning (FL) is used in this article as an umbrella term for FL algorithms as well as remotely-executed ML algorithms where computations are brought to data.

Machine learning & AI
Computational governance
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