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Published on 05/09/2024
Last updated on 06/18/2024

Balancing AI deployments with compliance and privacy concerns: Strategies for your enterprise


We're witnessing unparalleled growth in AI, and it is transforming industries and everyday life. If 2023 was AI’s breakout year, then 2024 is the year that organizations move toward AI production deployments. At the same time, the regulatory landscape surrounding AI and privacy is moving quickly as well. How should organizations navigate this complex landscape? 

Effective AI systems require vast amounts of data to learn, adapt, and evolve. This reliance on data highlights the critical intersection of AI development and data privacy. Any enterprise leveraging AI for innovation faces a significant challenge

How do we use vast datasets responsibly, adhering to stringent data privacy and protection laws?

To ensure your AI applications are ethical and trustworthy, you must balance AI innovation with proactive compliance with privacy laws.

Understanding the legal and regulatory framework 

The legal and regulatory framework around AI is rapidly evolving. To understand the current landscape, we need to understand how both forthcoming regulations and existing laws—such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA)—apply to AI. 

Existing laws and regulations 

The GDPR in the European Union places protections on personal data, regardless of the amount of personal data processed. Meanwhile, the CCPA protects consumer privacy and places restrictions on how businesses interact with an individual’s personal data. In both cases, this leads to certain restrictions: 

  1. In order to process personal data, you must have a legal basis. Article 6 of the GDPR speaks specifically to this. Generally speaking, user consent is required. 
  2. Businesses are required to offer data deletion when requested by the user. If personal data is used to train a machine learning model, then deleting that data becomes nearly impossible without retraining the model, and that’s an expensive task. 

Anticipated AI regulatory developments 

Similarly, new regulations are already in the works. The European Parliament recently adopted the AI Act, which is expected to go into effect by June 2024. Once in place, portions of the act will go into force over the course of six to 36 months.  

The act itself covers various AI-related applications, and it outlines high/unacceptable risk areas and transparency requirements. Several of those risk areas address specific uses of personal data, including: 

  • Social scoring systems: AI systems that evaluate and classify people based on their social behavior or personal traits. This can lead to potential discrimination or unfair treatment.
  • Emotion recognition systems: AI systems that use biometric data for the purpose of identifying or inferring a person’s emotions or intentions.
  • Biometric categorization systems: AI systems that use biometric data to attempt to categorize a person based on age, ethnicity, race, sex, or disabilities. Potential technical inaccuracies in these systems can lead to bias and discrimination. 

In the U.S., the White House issued an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence in 2023. In terms of data privacy, the executive order sets a vision rather than a regulatory framework. Nevertheless, it provides indicators of potential US-side regulations, such as:  

  • Advocating the use of privacy-preserving techniques when training AI systems 
  • Evaluations and guidelines for federal agencies in their use of AI systems 
  • Human rights and safety protection 

Many countries worldwide are adopting AI standards and policies. Enterprises that continue to innovate while also complying with evolving regulations will have a competitive advantage. If you’re ahead of the game on compliance, then this will go a long way toward gaining consumer and enterprise trust. 

Best practices for AI compliance 

Proactive AI compliance requires following a principle-based approach rather than merely adhering to the letter of the law. This will help you remain agile, not only in complying with current regulations but also by anticipating new regulations that come into effect. 

To maximize compliance benefits and minimize AI risks, consider the following best practices: 

1. Purposefully minimize personal data usage 

Perhaps the simplest way to reduce compliance risks regarding personal data is to avoid storing or using it. While certain business models require large amounts of personal data, others do not. It is well worth considering compliance implications when deciding to store personal data. 

When personal data is required, limit the purpose for its use. By processing only that data for which you have a clear purpose to do so, you keep your compliance footprint to a minimum.Ensure transparency and obtain user consent 

When personal data is essential for your use case, transparency is key to avoiding privacy issues. Transparency begins with informing users about:

  • What personal data is being processed 
  • How it is being processed 
  • The purpose for which it is being used 

Obtaining user consent for the processing of personal data is more than just good manners; it is legally required in the EU by the GDPR.

Additionally, consider data retention requirements, and keep the period to the minimum necessary. This is aligned with the timely handling of user requests to view, access, or delete their personal data. 

2. Ensure vendor compliance with privacy policies 

Any guidelines implemented above must also be verified and complied with by any vendors that process data for you. Processing and storage of personal data done on your behalf becomes your responsibility. Therefore, a vendor handling this data must also be governed and monitored by your privacy policy. 

3. Conduct compliance audits and assessments 

Conduct data compliance audits before deployment and establish a routine schedule for subsequent audits. This will help to ensure adherence to your working policies around personal data. You’ll also be able to identify risks before they become a larger issue. 

Aside from data privacy laws, many of the evolving AI regulations, including the EU’s AI Act, are focused on the ethical use of AI. In order to comply with these regulations, incorporate ethical AI assessments into your regular monitoring and compliance program. 

4. Incorporate privacy by design in AI development 

Following the above measures throughout your AI deployment process is also known as privacy by design. This is a proactive approach to privacy and compliance that does not wait for risks or compliance issues to emerge before addressing them. Instead, it seeks to prevent them from arising in the first place. 

Strategies for maximizing capabilities while ensuring AI privacy 

Privacy-preserving techniques are strategies to maximize the capabilities of an AI deployment while still maintaining a proactive approach toward privacy. Synthetic data, differential privacy, and federated learning, among other new techniques, offer privacy-conscious organizations a way to minimize compliance risks while enabling effective and meaningful AI deployments. 

1. Ensure synthetic data privacy 

The training of AI models is traditionally dependent on large datasets, which are increasingly challenging to anonymize reliably. Synthetic data provides the possibility of having large, statistically useful datasets which have no personally identifiable information. This makes synthetic data the clear choice when compliance and privacy risks are high. 

2. Build secure models with differential privacy 

Another technique is differential privacy. Differential privacy is a mathematical model that adds noise to sensitive data, thus giving individuals anonymity while preserving the meaningfulness of the dataset as a whole. 

3. Train AI models with federated learning 

Federated learning is a method by which the training of a machine learning model is done by separate processes, with only a portion of the data given to each one. Iterations are spread across devices and can be done without explicitly sharing the data.  

Ethical considerations in AI deployment 

Privacy is simply one piece of the larger conversation about ethics in AI deployment. Although regulations continue to evolve, enterprises should proactively address key themes.

First is the safe and effective use of AI. AI systems require safeguarding and monitoring to benefit, not harm, society. AI Risk Management Framework from the National Institute of Standards and Technology (NIST) provides an overview of potential risks in AI deployment. Created for voluntary use, the framework outlines critical risk areas.

Discrimination is one such risk. Algorithmic discrimination is one of the issues addressed by the recent executive order on AI, underscoring the need to examine possible biases in your AI deployment.

In all ethical matters, the ultimate goal is to use AI responsibly to benefit society and build trust in AI deployments. Achieving this requires careful, comprehensive ethical considerations at all levels of an organization. 

Future-proof your AI initiatives: Get ahead of AI compliance and privacy issues 

Organizations must take a principled, proactive approach to privacy in their AI deployments. Key existing regulations, like GDPR and CCPA, restrict personal data handling, requiring a legal basis and user consent. Upcoming regulations, such as the EU’s AI Act, will add additional regulations on AI deployments.

By incorporating privacy-by-design principles as well as AI-specific privacy-preserving techniques, organizations will be well-situated to adapt to any compliance requirements that come their way.

Interested in learning more about deploying AI in your enterprise? Read about how enterprises are accelerating the adoption of GenAI deployments

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