Paola Zeni, Chief Privacy Officer at RingCentral, looks at the challenges and pitfalls of navigating data privacy and security in a new, AI-centric world.

Today it’s nearly impossible to ignore the impact of AI. Even if a business isn’t actively using it, they’re likely aware of how AI is revolutionising everything from customer interactions to employee engagement. One of AI’s greatest benefits is the transformative way it enables businesses to harness data. Data is intrinsic to almost every business process and how we collect it and use it has evolved drastically. However, this opportunity also brings heightened responsibility for ensuring data privacy and security, particularly when working with third-party AI vendors.

Businesses are racing to implement AI and gain a competitive advantage. As they do so, many must decide between building their own Large Language Models (LLMs) or collaborating with third-party vendors. For many, building an in-house LLM may be costly, time-consuming, and may require infrastructure they may not yet have. In these cases, collaborating with external AI providers becomes an attractive alternative.

However, concerns over how sensitive data is protected in such collaborations have given rise to numerous misconceptions. This, in turn, leads to uncertainty and hesitancy within businesses contemplating whether to adopt. But businesses can reap the benefits of AI if they know what to be aware of. 

It’s time to debunk 

Misconception 1: Sharing data with third-party AI vendors equates to losing control over it.

One of the most common misconceptions is that sharing data with an AI vendor requires handing over full control of that data. In reality, reputable AI vendors offer terms that stipulate how data will be used, who has access, and what the limitations are. Businesses can establish rules around the use of their data and ensure that only authorised personnel can access it. 

Misconception 2: Data shared with AI vendors is more vulnerable to breaches.

Some businesses fear that outsourcing to an AI vendor increases the risk of data breaches, but this isn’t necessarily the case. AI vendors are subject to existing data protection regulations, such as GDPR, and to new AI laws that are coming into force. Additionally, they must comply with industry standards around encryption, security audits, and data monitoring. That said, when working with third-party AI vendors, businesses should always perform due diligence to ensure adherence to adequate data protection standards. 

Misconception 3: All data is accessible to AI vendors.

It’s often understood that AI vendors have unrestricted access to all the data they receive. Actually, AI systems can use anonymisation and data minimisation techniques to ensure that vendors only handle the data necessary for their specific task. Often, data is processed in such a way that it cannot be traced back to the individual or the organisation. This approach, combined with granular access controls, ensures that sensitive information remains protected even when external vendors are involved.

Collaborating with third-party AI vendors doesn’t inherently compromise data privacy. With contractual agreements in place and adherence to data protection regulations, sensitive information can be securely managed. 

Key data protection practices 

I believe there are four crucial practices that leaders should implement to ensure they are adhering to the highest standards of data protection practices, within a multi-vendor ecosystem. 

This includes:

Use secure APIs and interfaces 

Any interfaces and APIs used to exchange data should be secure and encrypted. Secure APIs help ensure that data flowing between systems remains protected, and any vulnerabilities are promptly identified and addressed.

Conduct regular security audits and penetration testing 

Continuous security testing is essential to identify vulnerabilities before they can be exploited. Businesses should closely collaborate with third-party providers to conduct regular security audits, including penetration testing, to confirm both parties’ systems are resilient against cyber threats. 

Check compliance with applicable privacy laws 

Data protection laws and regulations are continually evolving and differing by country. Businesses must remain abreast of these changes and stay compliant. Partnering with vendors that are also compliant with these regulations is imperative, considering that non-compliance can lead to fines and reputational damage.

Have an incident response plan in place 

Even with the best security measures in place, breaches can still happen. Having a strong incident response plan is critical to mitigating the impact of a data breach. Work with your partners to develop a clear and actionable response plan that includes prompt breach notifications, containment strategies, and communication protocols. By responding swiftly and effectively, businesses can mitigate the damage caused by data breaches. 

What is on the horizon?

Continued proliferation of data protection laws across jurisdictions will necessitate ever-greater data governance. 

Further, growing consumer awareness around data privacy risks will also drive greater transparency and stronger protection measures from businesses, particularly with the widespread adoption of AI. As a result, it is imperative that when embarking on an AI implementation journey, data protection is front of mind, especially as AI becomes integral to our day-to-day lives. 

Given these considerations, businesses can confidently embrace AI with the assurance that their data is secure, and their future is bright.

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