Data Privacy Considerations in AI

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.

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

⚠️Privacy Risks in AI Management: Lessons from Italy’s DeepSeek Ban⚠️ Italy’s recent ban on #DeepSeek over privacy concerns underscores the need for organizations to integrate stronger data protection measures into their AI Management System (#AIMS), AI Impact Assessment (#AIIA), and AI Risk Assessment (#AIRA). Ensuring compliance with #ISO42001, #ISO42005 (DIS), #ISO23894, and #ISO27701 (DIS) guidelines is now more material than ever. 1. Strengthening AI Management Systems (AIMS) with Privacy Controls 🔑Key Considerations: 🔸ISO 42001 Clause 6.1.2 (AI Risk Assessment): Organizations must integrate privacy risk evaluations into their AI management framework. 🔸ISO 42001 Clause 6.1.4 (AI System Impact Assessment): Requires assessing AI system risks, including personal data exposure and third-party data handling. 🔸ISO 27701 Clause 5.2 (Privacy Policy): Calls for explicit privacy commitments in AI policies to ensure alignment with global data protection laws. 🪛Implementation Example: Establish an AI Data Protection Policy that incorporates ISO27701 guidelines and explicitly defines how AI models handle user data. 2. Enhancing AI Impact Assessments (AIIA) to Address Privacy Risks 🔑Key Considerations: 🔸ISO 42005 Clause 4.7 (Sensitive Use & Impact Thresholds): Mandates defining thresholds for AI systems handling personal data. 🔸ISO 42005 Clause 5.8 (Potential AI System Harms & Benefits): Identifies risks of data misuse, profiling, and unauthorized access. 🔸ISO 27701 Clause A.1.2.6 (Privacy Impact Assessment): Requires documenting how AI systems process personally identifiable information (#PII). 🪛 Implementation Example: Conduct a Privacy Impact Assessment (#PIA) during AI system design to evaluate data collection, retention policies, and user consent mechanisms. 3. Integrating AI Risk Assessments (AIRA) to Mitigate Regulatory Exposure 🔑Key Considerations: 🔸ISO 23894 Clause 6.4.2 (Risk Identification): Calls for AI models to identify and mitigate privacy risks tied to automated decision-making. 🔸ISO 23894 Clause 6.4.4 (Risk Evaluation): Evaluates the consequences of noncompliance with regulations like #GDPR. 🔸ISO 27701 Clause A.1.3.7 (Access, Correction, & Erasure): Ensures AI systems respect user rights to modify or delete their data. 🪛 Implementation Example: Establish compliance audits that review AI data handling practices against evolving regulatory standards. ➡️ Final Thoughts: Governance Can’t Wait The DeepSeek ban is a clear warning that privacy safeguards in AIMS, AIIA, and AIRA aren’t optional. They’re essential for regulatory compliance, stakeholder trust, and business resilience. 🔑 Key actions: ◻️Adopt AI privacy and governance frameworks (ISO42001 & 27701). ◻️Conduct AI impact assessments to preempt regulatory concerns (ISO 42005). ◻️Align risk assessments with global privacy laws (ISO23894 & 27701).   Privacy-first AI shouldn't be seen just as a cost of doing business, it’s actually your new competitive advantage.

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 do companies developing or bringing Generative AI products to market (in Europe) need to learn from the The Italian Data Protection Authority decision on Generative AI Overall: 🔹 Reminder that you may get an enforcement and a fine even for a violation that you have already remedied in the meantime 🔹 You must match data processing to the relevant purpose in your notices. Lists are not compliant 🔹 You need an adequate age verification and parental consent mechanism and an opt-out that people know about Legal Basis 🔹 You need to figure out your GDPR legal basis in advance of starting the processing 🔹 If relying on legitimate interest you need to conduct (and document and date!) your legitimate interest analysis (ambiguous references are not enough) [This is echoed in the European Data Protection Board guidelines on AI models from earlier this week - parts 2-3 of my series still forthcoming; see here for part 1: https://lnkd.in/eSpxJZ77] Privacy Notice 🔹 Your notice needs to be accessible in languages other than English and it needs to be easily accessible 🔹 Your privacy notice must address non-users of the model whose data was used for mode training 🔹 You must adequately describe the purpose of the data processing adequately and distinguish between categories of data and their respective purposes (e.g. specify which types of data are requires for communication, fraud preventing and service improvement) 🔹 Having an opt out mechanism may not be enough - people must be made aware of it and how it works. 🔹 Having publications and papers available may not be a substitute for a non user privacy disclosure 🔹 Do not list all possible purposes for processing without matching them to specific categories of personal data [this is equally applicable under the US state privacy laws] 🔹 Information on anonymized or de-identified data must be clear and technically accurate [e.g. don't imply that de-identified data can't be re-identified] (this may be a US deidentify vs. EU deidentify definition issue...) 🔹 You need to implement adequate age verification measures in order to allow consent for teens and parental consent for kids. 🔹 You need to make the opt out possibility and mechanism abundantly clear You can be fined and required to: 🔹 Submit for approval an age verification method and a way to get parental consent 🔹 Launch a 6 month awareness campaign for disclosure of the data processing and privacy rights (with detailed instructions on opt out) on both traditional and digital media 🔹 Revise privacy notice #dataprivacy #dataprotection #privacyFOMO pic byChatGPT

Smell that? 👃 It’s your data. When we activate awesome opportunities with #AI, we also activate new risks. You’re accountable for your #data — and possibly data for your clients, customers, or constituents. Now that we’ve acknowledged the stench 💨, let’s talk about how to clean it up — and keep it clean. Responsible AI starts with responsible data practices. Here are five actions I live by: (What would you add to the list?) 1️⃣ 🧹 Clean it. Messy data leads to messy outcomes. Before feeding data into any AI system, ensure it's accurate, consistent, and free of noise. Clean data is the foundation of responsible AI. 2️⃣ ⚡ Enrich it. While you're under the hood cleaning it, add context, structure, and meaning to make it more valuable and insightful. Enriched data empowers AI to deliver smarter, more relevant results. Deploy humans for this work — it’s worth it. 3️⃣ 🥷 Mask it. Privacy isn’t optional. Protect sensitive information through masking, anonymization, or tokenization. Responsible AI respects the people behind the data. However, there are more reasons to mask data beyond the usual PII, PHI, HIPAA, etc. Consider masking outdated policies, procedures, or standard work documents in your general-purpose AI solution so employees don’t surface unsafe or obsolete practices when asking the chatbot for help. 4️⃣ ⏰ Expire it. Following the law, set clear retention policies and delete what’s no longer needed. Responsible AI means knowing when to let data go. What good is a policy that says you’ll delete data after a set period — if no one actually does it? 5️⃣ 🪣 Minimize it. The first four help manage the data you already have. But going forward, ask yourself: is collecting it even necessary in the first place? The more you collect, the more you need to do the above on a regular basis. #ResponsibleAI #DataGovernance #AIGovernance #PrivacyByDesign #TechCompliance #AIandLaw #RiskManagement

🧠 “Data systems are designed to remember data, not to forget data.” – Debbie Reynolds, The Data Diva 🚨 I just published a new essay in the Data Privacy Advantage newsletter called: 🧬An AI Data Privacy Cautionary Tale: Court-Ordered Data Retention Meets Privacy🧬 🧠 This essay explores the recent court order from the United States District Court for the Southern District of New York in the New York Times v. OpenAI case. The court ordered OpenAI to preserve all user interactions, including chat logs, prompts, API traffic, and generated outputs, with no deletion allowed, not even at the user's request. 💥 That means: 💥“Delete” no longer means delete 💥API business users are not exempt 💥Personal, confidential, or proprietary data entered into ChatGPT could now be locked in indefinitely 💥Even if you never knew your data would be involved in litigation, it may now be preserved beyond your control 🏛️ This order overrides global privacy laws, such as the GDPR and CCPA, highlighting how litigation can erode deletion rights and intensify the risks associated with using generative AI tools. 🔍 In the essay, I cover: ✅ What the court order says and why it matters ✅ Why enterprise API users are directly affected ✅ How AI models retain data behind the scenes ✅ The conflict between privacy laws and legal hold obligations ✅ What businesses should do now to avoid exposure 💡 My recommendations include: • Train employees on what not to submit to AI • Curate all data inputs with legal oversight • Review vendor contracts for retention language • Establish internal policies for AI usage and audits • Require transparency from AI providers 🏢 If your organization is using generative AI, even in limited ways, now is the time to assess your data discipline. AI inputs are no longer just temporary interactions; they are potentially discoverable records. And now, courts are treating them that way. 📖 Read the full essay to understand why AI data privacy cannot be an afterthought. #Privacy #Cybersecurity #datadiva#DataPrivacy #AI #LegalRisk #LitigationHold #PrivacyByDesign #TheDataDiva #OpenAI #ChatGPT #Governance #Compliance #NYTvOpenAI #GenerativeAI #DataGovernance #PrivacyMatters