Other recent blogs
A deliberate focus on AI is enabling businesses to build products with increased precision, improve customer experience, and unlock new revenue streams.
However, most AI applications thrive on reliable data available in enormous quantities. Data privacy and protection regulations, the GDPR being a significant one, can thus have a huge impact on the future of AI.
If you are part of a business that has anything to do with AI and collecting or processing personal data of residents or citizens of Europe, you must not miss out on this blog.
In this blog, we will navigate the GDPR landscape, its building blocks, how it impacts the organizations building or using AI systems and applications, and what you can do to ensure GDPR compliance (if it applies to your organization.)
Let’s start with the fundamentals of the GDPR.
What is GDPR?
Here’s what you get to see if you land on the home page of the GDRP website:
The General Data Protection Regulation (GDPR) is the toughest privacy and security law in the world. Though it was drafted and passed by the European Union (EU), it imposes obligations onto organizations anywhere, so long as they target or collect data related to people in the EU. The regulation was put into effect on May 25, 2018. The GDPR will levy harsh fines against those who violate its privacy and security standards, with penalties reaching into the tens of millions of euros.
This is the foremost text that you get to read on the home page of the website. And this paragraph alone throws a lot of light on the very building blocks of GDPR.
- First, the law is concerned with personal data such as your email address, phone number, bank account number, social media accounts, etc.
- Second, it applies to the personal data of people residing in Europe.
- Third, it applies to each and every organization out there that has anything to do with the personal data of EU citizens or residents.
- The fourth and most important point is the harsh penalties that non-compliance with the regulation can lead to - There are two tiers of penalties, which max out at €20 million or 4% of global revenue (whichever is higher), plus data subjects have the right to seek compensation for damages.
With these important points now out of the way, let’s proceed toward the foundational blocks that define the overall scope and structure of the GDPR.
Key principles that succinctly shape up the scope of GDPR
The core of the GDPR is made up of 7 principles that emanate from the EU’s firm stance on safeguarding the interests of their people against rising cybersecurity crimes that are a common occurrence these days. Below is a succinct explanation of these principles:
- Lawfulness, fairness, and transparency — You must process the collected data in a lawful, fair, and transparent manner.
- Purpose limitation — You can use the data for only the purposes for which the data subject has given consent.
- Data minimization — You must collect as much data as absolutely necessary for the specified purposes.
- Accuracy — You are responsible for keeping the stored data accurate and up-to-date.
- Storage limitation — You should hold the data for as long as necessary to generate the legitimate outcomes specified.
- Integrity and confidentiality — You must prioritize data integrity and confidentiality by Implementing robust security solutions.
- Accountability — Demonstrating GDPR compliance is the responsibility of the data controller.
Get the hang of the common GDPR terms: Data subjects, data controllers, data processors
The GDPR is a deliberate effort to signal Europe’s firm stance on the data protection of its people. And while laying out the framework of the regulation, the law keeps including certain terms. These are:
- Data subjects: Individuals residing in Europe
- Data controllers: Individuals and organizations deciding why and how the data will be collected and processed
- Data processors: Third-party organizations that process data on behalf of data controllers.
GDPR in the rapidly evolving AI landscape - Is it a threat to the future of AI, or is it something else?
The data privacy and security law might feel scary the first time. And it’s okay. However, if you deliberately think about it, the GDPR constraints can actually prove to be the foundational blocks of the development and usage of responsible AI.
Companies at the forefront of AI innovation are consciously expanding their understanding of building AI solutions that are safe, secure, and responsible. Google Cloud, for example, is expeditiously embracing a responsible or values-based AI approach across its AI-driven projects. According to this tech giant, practicing responsible AI practices can actually help your business open doors of new growth opportunities.
Responsible AI, as Google reports, can yield a multitude of benefits for your organization. And data security and privacy regulations, in this case, the GDPR, are actually good for the health of your organization. However, in the ever-changing business landscape and the rapid evolution of AI, it’s not going to be easy for businesses to comply with the GDPR law.
Challenges in complying with GDPR in AI
The GDPR protects the interests of individuals by empowering them with seemingly endless rights to their data. Some of these rights include the right to be informed, the right to access, rectify, and erase data, the right to object, and the right to restrict processing. And let’s not forget the penalties that can be imposed if your organization is found guilty of not complying with the data protection law. The most common challenges that can hold your organization back from truly leveraging AI technology if you are to comply with the GDPR include the following:
- Ensuring transparency in data collection and processing: Many AI algorithms, especially deep learning models, can be seen as "black boxes" where it's challenging to understand how decisions are made. GDPR requires transparency, which can be a challenge when it comes to explaining AI decisions.
- Protecting the rights of data subjects: GDPR grants data subjects various rights, such as the right to access, rectify, or erase their data. Implementing these rights in AI systems can be complex, particularly in the context of unstructured data or machine learning models.
- Automated decision-making: When AI systems make decisions that have legal or significant effects on individuals (e.g., credit scoring or job recruitment), GDPR imposes additional requirements, including the right to human intervention.
- Mandatory data protection impact assessments (DPIAs): Organizations must conduct DPIAs for high-risk processing activities. AI projects with potential privacy risks should undergo such assessments.
- Data transfers across borders: If personal data is transferred outside the EU, organizations must ensure it is protected to GDPR standards, which can be challenging when using cloud-based AI services hosted outside the EU.
7 tips for GDPR compliance in AI:
- Data governance: Implement robust data governance practices, including data mapping, classification, and lifecycle management.
- Privacy by design: Build privacy and data protection into AI systems from the outset rather than as an afterthought.
- Consent management: Ensure that you obtain clear and informed consent when required, and provide mechanisms for users to revoke consent.
- Algorithmic transparency: Work towards explainable AI models to provide transparency and accountability in decision-making processes.
- Data security: Invest in strong data encryption, access controls, and regular security audits to protect personal data.
- Documentation and record-keeping: Maintain detailed records of data processing activities and demonstrate compliance through documentation.
- Collaborate with legal and data protection experts: Ensure that your AI projects involve legal and data protection experts to assess and mitigate risks.
Remember, non-compliance with GDPR can result in severe fines, so it's essential for organizations working with AI to take data protection and privacy seriously and ensure they meet GDPR requirements. Consulting legal experts with GDPR expertise is advisable for complex AI projects.
A few final thoughts
AI is a double-edged sword. To ensure the development and deployment of responsible AI applications and solutions, data privacy and protection laws can be detrimental. Although complying with GDPR requirements can be a daunting task for businesses aiming to leverage AI solutions to accelerate time to market and customer experience, understanding the core tenets of the law can ease the adoption and compliance.
Kellton has always been at the forefront of data protection and AI innovation and helps companies of all shapes and sizes transform their IT landscapes with its cutting-edge digital solutions. Whether your business needs to build GDPR-compliant AI applications or modernize your traditional IT landscape with new and innovative digital solutions, we can help.