Understanding the Privacy and Security Debate

Before diving headfirst into AI, companies need to define what data privacy means to them in order to use GenAI safely. After decades of harvesting and storing data, many tech companies have created vast troves of the stuff - and not all of it is safe to use when training new GenAI models. Most companies can easily recognize obvious examples of Personally Identifying Information (PII) like Social Security numbers (SSNs) - but what about home addresses, phone numbers, or even information like how many kids a customer has? These details can be just as critical to ensure newly built GenAI products don’t compromise their users' privacy - or safety - but once this information has entered an LLM, it can be really difficult to excise it. To safely build the next generation of AI, companies need to consider some key issues: ⚠️Defining Sensitive Data: Companies need to decide what they consider sensitive beyond the obvious. Personally identifiable information (PII) covers more than just SSNs and contact information - it can include any data that paints a detailed picture of an individual and needs to be redacted to protect customers. 🔒Using Tools to Ensure Privacy: Ensuring privacy in AI requires a range of tools that can help tech companies process, redact, and safeguard sensitive information. Without these tools in place, they risk exposing critical data in their AI models. 🏗️ Building a Framework for Privacy: Redacting sensitive data isn’t just a one-time process; it needs to be a cornerstone of any company’s data management strategy as they continue to scale AI efforts. Since PII is so difficult to remove from an LLM once added, GenAI companies need to devote resources to making sure it doesn’t enter their databases in the first place. Ultimately, AI is only as safe as the data you feed into it. Companies need a clear, actionable plan to protect their customers - and the time to implement it is now.

The rapid advancement of AI technologies, particularly LLMs, has highlighted important questions about the application of privacy laws like the GDPR. As someone who has been grappling with this issue for years, I am *thrilled* to see the Hamburg DPC's discussion paper approach privacy risks and AI with a deep understanding of the technology. A few absolutely refreshing takeaways: ➡ LLMs process tokens and vectorial relationships between tokens (embeddings), fundamentally differing from conventional data storage and retrieval. The Hamburg DPC finds that LLMs don't "process" or "store" personal data within the meaning of the GDPR. ➡ Unlike traditional identifiers, tokens and their embeddings in LLMs lack the necessary direct, targeted association to individuals that characterizes personal data in CJEU jurisprudence. ➡ Memorization attacks that extract training data from an LLM don't necessarily conclude that personal data is stored in the LLM. These attacks may be practically disproportionate and potentially legally prohibited, making personal identification not "possible" under the legislation. ➡ Even if personal data was unlawfully processed in developing the LLM, it doesn't render the use of the resulting LLM illegal (providing downstream deployers some comfort when leveraging third-party models). This is a nuanced and technology-informed perspective on the complex intersection of AI and privacy. As we continue to navigate this rapidly evolving landscape, I hope we see more regulators and courts approach regulation and legal compliance with a deep understanding of how the technology actually works. #AI #Privacy #GDPR #LLM

AI is revolutionizing security, but at what cost to our privacy? As AI technologies become more integrated into sectors like healthcare, finance, and law enforcement, they promise enhanced protection against threats. But this progress comes with a serious question: Are we sacrificing our privacy in the name of security? Here’s why this matters: → AI’s Role in Security From facial recognition to predictive policing, AI is transforming security measures. These systems analyze vast amounts of data quickly, identifying potential threats and improving responses. But there’s a catch: they also rely on sensitive personal data to function. → Data Collection & Surveillance Risks AI systems need a lot of data—often including health records, financial details, and biometric data. Without proper safeguards, this can lead to privacy breaches, with potential unauthorized tracking via technologies like facial recognition. → The Black Box Dilemma AI systems often operate in a "black box," meaning users don’t fully understand how their data is used or how decisions are made. This lack of transparency raises serious concerns about accountability and trust. → Bias and Discrimination AI isn’t immune to bias. If systems are trained on flawed data, they may perpetuate inequality, especially in areas like hiring or law enforcement. This can lead to discriminatory practices that violate personal rights. → Finding the Balance The ethical dilemma: How do we balance the benefits of AI-driven security with the need to protect privacy? With AI regulations struggling to keep up, organizations must tread carefully to avoid violating civil liberties. The Takeaway: AI in security offers significant benefits, but we must approach it with caution. Organizations need to prioritize privacy through transparent practices, minimal data collection, and continuous audits. Let’s rethink AI security—making sure it’s as ethical as it is effective. What steps do you think organizations should take to protect privacy? Share your thoughts. 👇

How do you get your personal information out of an LLM? You can’t. AI adoption is accelerating, unlocking incredible opportunities—but also raising critical questions about security and data management. I’ve spent years working at the intersection of data security, identity management, and scalable data infrastructure, and one thing is clear: AI models are only as ethical, trustworthy, and informed as the data they’re trained on. As AI systems grow more advanced, they increasingly rely on personal, proprietary, and operationally critical information to tackle ever increasing uses cases. That training needs to be correlated or matched to find patterns. So, fully anonymous data really doesn’t cut it in the data preparation steps, which leads to the need to safeguard personal identity in the model building pipeline. This is why it is critical to address the privacy and security of data aggregation early in the AI pipeline. Once private information enters the model weights, there’s no fixing that later. Employing privacy enhancing technology within the AI training process can safely consolidate and match operational information before transitioning to fully anonymous training sets. The right tooling means everything here. Without these safeguards in place, AI can introduce risks that create long-term challenges for businesses that didn’t account for protection from the start. Building AI responsibly isn’t just about compliance—it’s about ensuring AI remains a powerful tool without turning people away from toxic side-effects. I would love to hear how others in tech, privacy, and security are thinking about this challenge.  #ResponsibleAI #DataEthics #PrivacyFirst #SecureAI

❓ What are the risks from AI? Framework #3 This week we summarize the third risk framework included in the AI Risk Repository: “Navigating the Landscape of AI Ethics and Responsibility”, by Paulo Rupino Cunha and Jacinto Estima (2023). Using a systematic literature review and an analysis of real-world news about AI-infused systems, this framework clusters existing and emerging AI ethics and responsibility issues into 6 groups: 1️⃣ Broken systems: Algorithms or training data leading to unreliable outputs, often disproportionately weighing variables like race or gender. These can cause significant harm to individuals through biased decision-making in areas like housing, relationships, or legal proceedings. 2️⃣ Hallucinations: AI systems generating false information, particularly in conversational AI tools. This can lead to the spread of misinformation, especially among less knowledgeable users. 3️⃣ Intellectual property rights violations: AI tools potentially infringing on creators' rights by using their work for training without permission or compensation. This includes issues with AI-generated code potentially violating open-source software licenses. 4️⃣ Privacy and regulation violations: AI systems collecting and storing personal data without proper legal basis or user consent, potentially violating privacy laws like GDPR. This also includes the risk of exposing sensitive information through AI tool usage. 5️⃣ Enabling malicious actors and harmful actions: AI technologies being used for nefarious purposes such as creating deep fakes, voice cloning, accelerating password cracking, or generating phishing emails and software exploits. 6️⃣ Environmental and socioeconomic harms: The significant energy consumption and carbon footprint associated with AI applications, contributing to climate change and raising concerns about environmental sustainability. ⭐️ Key features: Concludes that AI ethics and responsibility needs to be reflected upon and addressed across five dimensions: Research, Education, Development, Operation, and Business Model. The discussion section classifies real-world cases of unethical or irresponsible uses of AI The paper identifies several key cases and issues that have led to the development of taxonomies, conceptual models, and official regulations, to better understand these issues and propose potential solutions to address them 💬 What do you think of this framework? Feel free to share your thoughts or any related resources in the comments 👇 📚 References/further reading Cunha, P.R., Estima, J. (2023). Navigating the Landscape of AI Ethics and Responsibility. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science, vol 14115. Springer, Cham. The AI Risk Repository website and preprint are linked in the comments.

5 Risks You Must Know About AI And Privacy (Your business depends on trust — but AI can put that trust at risk.) AI loves data, but mishandling it can cost you big. Here are the top 5 privacy risks you need to watch: ↳ Unauthorized Access — Weak controls let hackers or insiders grab sensitive data. ↳ Poor Anonymization — Bad techniques can easily be reversed, exposing identities. ↳ Bias And Discrimination — Biased AI models can create unfair, illegal outcomes. ↳ Data Over-Collection — Grabbing too much data increases breach and legal risks. ↳ Weak Ethical Guardrails — Without checks, your AI can drift into privacy violations. So how do you reduce these risks? Here’s your checklist: ↳ Strong Access Controls ↳ Regular Data Audits ↳ Robust, Irreversible Anonymization ↳ Ethical AI Frameworks To Monitor Bias ↳ Collect Only What You Need Winning with AI is not just about power, it’s about responsibility. __________________________ AI Consultant, Course Creator & Keynote Speaker Follow Ashley Gross for more about AI