Ensuring privacy and trustworthiness for data owners and providers presents a myriad of complex challenges.
Data breaches remain a prominent threat, with Europe as the most affected in Q1 2023 (17.5 million), followed by Asia and North America.
Data encryption and anonymization are critical to safeguard data from unauthorized access, but striking a balance between strong encryption and data usability can be tricky. Secure data sharing between multiple parties is necessary for collaboration while preserving privacy, but it often demands computationally expensive techniques like secure multi-party computation. This led data owners to adopt data minimization practices for reducing privacy risks, but this approach must also align with business needs.
Additionally, obtaining user consent and providing transparent information about data processing practices are essential for building trust, but designing user-friendly consent mechanisms can be challenging.
From an economic perspective, the cost of compliance with robust privacy measures and data protection practices poses significant challenges, particularly for smaller companies and startups. Liability and legal concerns add economic risks, making it vital for data owners and providers to take proactive steps in managing potential legal consequences. Monetizing data ethically while respecting privacy is another economic challenge. Striking a balance between generating value from data and ensuring data privacy is a delicate task that need careful consideration involving the development of economic models that provide fair incentives and compensate for potential risks, encouraging for example data sharing for research and collaboration.
Standardizing privacy regulations and technical measures across regions can help reduce compliance challenges and related costs. Moreover, third-party services are often relied upon for data processing, storage, and analysis. Ensuring these service providers adhere to strict privacy standards and data protection practices is crucial for maintaining trust in the data ecosystem.
Addressing these multifaceted challenges requires a comprehensive approach, involving strong technical measures, clear regulations, effective enforcement, and ongoing collaboration between stakeholders. Only through collective efforts can we establish a data landscape that prioritizes privacy and trustworthiness for all involved parties.
In this challenging scenario, RINA is committed to support data scientists and AI developers and bring benefit to European data space stakeholders.
In particular, RINA provides the Trustworthy AI Support Design Framework built on human-centric processes and a set of supporting tools to ensure lawful, ethical, and robust AI throughout the product lifecycle.
Our approach is applicable for AI software development to make AI-based systems as robust and resilient to attacks as possible through various measures:
The Framework considers a typical assessment and system monitoring audit workflow as step-by-step support to develop explainable and trustworthy AI applications.
Using RINA solution, AI developers and data scientists can benefit.
By a Framework able to support them across all the AI development life-cycle phases, namely: