AI and Data Privacy

Concerns about privacy is the top inhibitor of AI adoption

Many of us are interested in the hottest new AI apps and prompts to improve our productivity. But if you are subject to a data breach, that could be for naught. That’s why I dove into how to consider your privacy concerns this week (And I really tried not to make it dry and boring).

Also, I have some news. The Artificially Intelligent Enterprise just acquired AI Lessons, a weekly newsletter that provides one actionable lesson every week. In the near future, you’ll be getting the benefit of over 100 AI lessons as part of Artificially Intelligent Enterprise. This will be a quick five-minute read on Tuesdays that has one actionable bit of advice to improve your productivity with AI. I hope you enjoy the new content.

I've tried to replicate the feature article and most of the content from my email newsletter in the LinkedIn Newsletter, but if you want the additional bonus content, including the Prompt of the Week, then I suggest you subscribe via email to The Artificially Intelligent Enterprise and get this newsletter in your inbox every Friday.

Sentiment Analysis

Data Privacy Isn’t Just an AI Problem

It seems that the only snail mail I receive these days is notifications of data breaches. The issue isn't just personal; it's a global problem as data privacy violations increasingly dominate headlines. Recent actions by governments worldwide reflect a growing intolerance for such infractions.

In South Korea, the data privacy watchdog utilized the Personal Information Protection Act (PIPA) to levy a substantial fine on AliExpress, penalizing them two billion won (about $1.4 million) for privacy violations. These infractions included the unauthorized transfer of customer data to third-party sellers in China and other foreign nations. Similar scrutiny is now aimed at Temu as this case unfolds, signaling a broader crackdown on data mismanagement.

In the United States, Texas recently made history by securing a $1.4 billion settlement with Meta, Facebook's parent company. This is the largest data privacy settlement brought by a state. The lawsuit, initiated in 2022, accused Meta of unlawfully collecting biometric data from millions of Texans without their consent, violating state laws.

While AI is the shiny new toy everyone's talking about, the real issue is as old as the first computer. It's about how we, as a society, handle the massive troves of electronic data we're generating every second. So, the next time you get one of those "We value your privacy" emails (right after a data breach, of course), remember: in the world of data privacy, we're not just the audience – we're part of the show. And it's high time we all learned our lines.

AI Efficiency Edge - Quick Tips for Big Gains

Use a Local LLM to Keep Your Data Private

Running a local large language model (LLM) using Ollama is a practical solution for organizations with policies against using public chatbots due to data privacy, security, or compliance concerns. Using Ollama, you can run Llama 3.1, Phi 3, Mistral, Gemma 2, and other models.

Using Ollama to run a local LLM enables organizations to utilize powerful AI models while keeping all data in-house, aligning with strict security and compliance requirements. I also included below a couple of other ways to run LLMs locally in this week’s AI Toolbox.

AI TL;DR - Latest AI News for Business Users

• OpenAI could be about to drop Project Strawberry in huge ChatGPT upgrade - Rumors are circulating that OpenAI is about to drop a new model this week and it could be our first sighting of the highly anticipated “Project Strawberry” — a reasoning-focused AI. I did a little TL;DR on Linkedin as well.
• Microsoft’s AI Can Be Turned Into an Automated Phishing Machine - Attacks on Microsoft’s Copilot AI allow for answers to be manipulated, data extracted, and security protections bypassed, new research shows.
• Why Every Big Tech Company Has Failed to Dethrone Nvidia as King of AI - Over the past two decades, Nvidia has created what is known in tech as a “walled garden,” not unlike the one created by Apple. The key to understanding Nvidia’s walled garden is a software platform called CUDA.
• A popular state bill could transform the AI industry. But two federal lawmakers from Silicon Valley don’t like it - The fate of a bill that could reshape Silicon Valley’s surging artificial intelligence industry could be decided this week, with everyone from big tech to members of Congress jockeying to influence the outcome.

AI and Data Privacy

In 1890, two young Harvard Law graduates, Samuel Warren and Louis Brandeis, penned a groundbreaking article titled "The Right to Privacy." Alarmed by the rapid advances in photography and the rise of mass media, they argued that privacy was a fundamental right, one increasingly at risk in the modern age.

However, as technology advanced, we faced these issues with the Internet, email, and the telephone. As AI advances, the age-old battle for privacy is reignited. This time on a digital frontier where data is the new currency, and our personal information is the most valuable commodity.

As artificial intelligence (AI) becomes increasingly integrated into various aspects of our lives, the issue of data privacy has never been more critical. AI systems rely on vast amounts of data from healthcare to finance to learn, adapt, and make decisions. However, this reliance on data brings significant privacy concerns that cannot be ignored. In an era where data is often called the "new oil," protecting personal information has become a paramount challenge for developers, businesses, and policymakers.

A Brief History of Data Privacy Concerns in AI

The relationship between AI and data privacy dates back to the early days of machine learning, when the focus was primarily on the development of algorithms. As AI systems evolved, the need for large datasets became apparent, leading to the widespread collection of personal data. Initially, the concerns were minimal, as the datasets used were often anonymized and limited in scope. However, as AI systems became more sophisticated, the ability to re-identify individuals from anonymized data increased, raising significant privacy issues.

Introducing regulations like the General Data Protection Regulation (GDPR) in the European Union marked a turning point, emphasizing the importance of data protection and privacy. These regulations highlighted the need for transparency, consent, and the right to be forgotten, directly impacting how AI systems are designed and operated. Despite these advancements, the rapid pace of AI development continues to outstrip the evolution of data privacy frameworks.

Current Trends and Challenges

Today, AI systems are more powerful and pervasive than ever before. The rise of machine learning models, especially those based on deep learning, requires extensive datasets, often containing sensitive personal information. This raises several challenges:

1. Informed Consent: Obtaining meaningful consent from individuals to use their data in AI systems is increasingly difficult. Most users do not fully understand how their data is used nor the potential risks involved. Takeaway: If you input data into a chatbot like ChatGPT or Claude, the vendor is responsible for keeping that data private based on their usage policy. Understand that sharing your private client data with a third party may breach the confidentiality of your data.
2. Data Anonymization: While anonymization techniques are employed to protect individual identities, advances in AI have made it possible to re-identify anonymized data, rendering these techniques less effective. Takeaway: I included a link to Opaque Systems Whitepaper, Securing Generative AI in the Enterprise, this week because it’s a good primer on how to think about data privacy in the enterprise for 2024. I collaborated with them on the creation and I think it’s worth the read.
3. Bias and Discrimination: AI systems trained on biased datasets can perpetuate and exacerbate existing biases, leading to discriminatory outcomes. Ensuring fairness in AI requires careful consideration of the data used and the potential for unintended consequences. Takeaway: If your company uses Generative AI models, you should be testing for fairness and bias. The term of art for this is model evaluation. IBM has a nice primer on the topic here.
4. Security Breaches: As AI systems become more complex, they become more vulnerable to security breaches. Protecting sensitive data from cyber-attacks is a growing concern as AI systems are increasingly targeted. Takeaway: These new models can be exploited in ways we may not have seen before. Security vendors are developing new products and features, but remember, exposing these AI models generates a larger attack face. So exercise due caution.

AI New Tech Same Privacy Issues

As AI advances, the importance of data privacy cannot be overstated. It is not just a technical challenge but a fundamental issue that touches on ethics, law, and human rights. By addressing these challenges head-on and incorporating privacy into the core of AI systems, we can harness AI's full potential while safeguarding individuals' rights and freedoms.