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4 AI Privacy Issues—and How to Combat Them

Dan O'Keefe, Appian
January 15, 2024

Artificial intelligence is changing the world. With use cases ranging from content generation to deep data analysis to detecting health issues, AI can greatly improve lives and enhance business outcomes. And with the explosion of generative AI services and large language models, we can expect AI to become even more ubiquitous than it already is. 

But AI isn’t perfect. In particular, AI privacy issues put organizations at risk or prevent adoption in the first place. For example, AI without data privacy safeguards could pose data breach risks that prevent organizations in heavily regulated industries from ever considering using it.

Still, organizations can’t afford to miss out on the AI revolution. Failure to hop on the artificial intelligence train could leave you miles behind your competition. The key is to understand the critical artificial intelligence privacy issues and how you can minimize or eliminate privacy risks.

This post will explain four AI privacy challenges—and how you can combat them.

[Staying on top of the latest AI trends is critical for gaining and maintaining a competitive edge. Find out what the experts had to say with the 2024 AI Outlook: Expert Advice on Navigating the AI Economy.]

AI Privacy Issue 1: Data usage transparency.

For many software vendors, access to customer data is key to their business model. This applies to many AI services vendors as well. When you use an AI service, you often provide the vendor with the ability to use your data to train their own models. This raises AI privacy issues around who gets access to that data. Your data could be used to train a model that your rivals benefit from. 

To avoid this, look for organizations that don’t have a vested interest in your data. Large public cloud providers, also known as hyperscalers, can be notoriously problematic, using customer data to create their own proprietary models. Check the privacy statement before you invest in a service. Also, look for vendors that follow a private AI philosophy. 

And be wary of any public service. OpenAI directly cautions users against entering sensitive, private, or proprietary data into ChatGPT. This is good advice. Unless you can be sure your data will remain private, be careful about what you enter into public AI services. 

[ Want to succeed at AI? Get specific about your use cases. Learn how by watching the Appian World presentation: 6 Ways AI Makes a Difference to Your Business. ]

AI Privacy Issue 2: Lack of opt-in.

Recently, questions have arisen about how data was used to train many of the most popular large language models. For instance, authors and creatives have started taking generative AI and LLM providers to court over potential misuse of copyrighted materials in model training.

But this isn’t just a problem for big name AI companies. If you’re training your own models using customer data, are you providing proper opt-in statements to them? Have they given informed consent for their information to be processed? Since many AI solutions require vast amounts of data, it may not always be clear to users how their data will be used. Most mistakes are unintentional, but you may still come under fire from data privacy laws like GDPR.

The solution? First, talk to your legal team. Make sure they review opt-in statements and data usage policies so you’re protected. Second, again, look for private AI providers. If you can scope the amount of data used in model generation, you will reduce your risk exposure and potentially make models more accurate.

AI Privacy Issue 3: Data breaches.

Every year, cyberattacks grow exponentially. Cybercriminals come up with new methods of compromise, modify existing attack patterns, or simply run their old tricks knowing that some people will still fall victim (social engineering attempts like tech support scams or phishing still catch many people). Making matters worse, AI has become a favorite tool among shady actors. It can allow them to quickly generate convincing phishing emails at scale that can help them gain unauthorized access to data. If you’re not careful, your private data could soon become public, leading to reputational damage and potential fines.

So how do you deal with this? Two ways. First, practice vigilance with internal security. A full explanation of cybersecurity best practices is beyond the scope of this blog, but work closely with your security team and listen to their recommendations. User security training is essential,   and implementing intensive, layered security controls is critical. You may also want to add certain AI services to a “deny list” for your organization to prevent your own users from sharing critical data with third parties. 

Second, check your software supply chain. In other words, how do your AI process platform vendors stack up on their own security practices? Check their trust centers to ensure they use robust security measures. It’s also critical to ask about their compliance certifications. Compliance doesn’t guarantee security, but it’s a very good proxy. An organization has to demonstrate sound security practices to be deemed compliant under strict standards like HIPAA, FedRAMP, SOC 3, or FISMA, to name just a few. Even if you aren’t in a heavily regulated industry, knowing that the organization earned multiple critical security certifications can offer peace of mind.

AI Privacy Issue 4: Bias.

Hold on. Bias? A privacy issue? AI systems can exacerbate existing biases that lead to poor outcomes and predictions for users. But how is this a privacy issue?

Well, here’s how. Bias in an AI system used in decision-making could violate an individual’s privacy rights—and potentially lead to difficult outcomes for an organization like fines or reputational damage. Consider a company using AI for background checks or employment screenings. Bias could easily creep into an AI system that might reject candidates based on non-essential, private information (or worse, protected characteristics or demographics). To combat this, it’s critical to only apply AI systems when you’re certain there’s a low-risk of bias or when you have reasonable control or human supervision over the AI model outputs.

Keep your data safe with private AI.

Your best bet is to seek out organizations that emphasize private AI—organizations where privacy is a core component of their AI philosophy and the bedrock of their systems. A private AI approach means that your data will never escape your control, existing in a closed circuit system. Private AI means a commitment from your vendor to never training their own algorithms on your data. Private AI vendors follow strong data security principles and are transparent about their practices. 

AI is already changing everything. And with new regulations and executive orders around AI forthcoming, we can expect private AI to become even more important. 

Want to learn more about the private AI approach? Our guide offers a good introduction to private AI, including practical tips on how to apply it to real-world tasks.