Data Privacy Issues With AI

This Stanford study examined how six major AI companies (Anthropic, OpenAI, Google, Meta, Microsoft, and Amazon) handle user data from chatbot conversations.  Here are the main privacy concerns. 👀 All six companies use chat data for training by default, though some allow opt-out 👀 Data retention is often indefinite, with personal information stored long-term 👀 Cross-platform data merging occurs at multi-product companies (Google, Meta, Microsoft, Amazon) 👀 Children's data is handled inconsistently, with most companies not adequately protecting minors 👀 Limited transparency in privacy policies, which are complex and hard to understand and often lack crucial details about actual practices Practical Takeaways for Acceptable Use Policy and Training for nonprofits in using generative AI: ✅ Assume anything you share will be used for training - sensitive information, uploaded files, health details, biometric data, etc. ✅ Opt out when possible - proactively disable data collection for training (Meta is the one where you cannot) ✅ Information cascades through ecosystems - your inputs can lead to inferences that affect ads, recommendations, and potentially insurance or other third parties ✅ Special concern for children's data - age verification and consent protections are inconsistent Some questions to consider in acceptable use policies and to incorporate in any training. ❓ What types of sensitive information might your nonprofit staff  share with generative AI?  ❓ Does your nonprofit currently specifically identify what is considered “sensitive information” (beyond PID) and should not be shared with GenerativeAI ? Is this incorporated into training? ❓ Are you working with children, people with health conditions, or others whose data could be particularly harmful if leaked or misused? ❓ What would be the consequences if sensitive information or strategic organizational data ended up being used to train AI models? How might this affect trust, compliance, or your mission? How is this communicated in training and policy? Across the board, the Stanford research points that developers’ privacy policies lack essential information about their practices. They recommend policymakers and developers address data privacy challenges posed by LLM-powered chatbots through comprehensive federal privacy regulation, affirmative opt-in for model training, and filtering personal information from chat inputs by default. “We need to promote innovation in privacy-preserving AI, so that user privacy isn’t an afterthought." How are you advocating for privacy-preserving AI? How are you educating your staff to navigate this challenge? https://lnkd.in/g3RmbEwD

NEWS 21/10/25: Department of Homeland Security obtains first-known warrant targeting OpenAI for user prompts in ChatGPT According to a recent article by Forbes, the U.S. Department of Homeland Security (DHS) has secured a federal search warrant ordering OpenAI to identify a user of ChatGPT and to produce the user’s prompts, as part of a child-exploitation investigation. https://lnkd.in/eatmK3zv? Key details: - The warrant was filed by child-exploitation investigators within DHS. - It specifically targets “two prompts” submitted to ChatGPT by an anonymous user. The warrant asks OpenAI for the user’s identifying information and associated prompt history. - This is described as the first known federal search warrant compelling ChatGPT prompt-level data from OpenAI. What this means for privacy: -Prompts are treated as evidence. What users have assumed to be ephemeral or private entries in a chat session with an AI service may now be subject to law-enforcement production. -Scope of data retention and access must be reconsidered. If prompt history can be identified and requested, both users and providers should evaluate how long prompts are stored, under what identifiers, and how anonymised they truly are. - Implications for user trust and provider responsibility. AI companies may face growing legal obligations to disclose user-generated content and metadata, which may affect how the services present themselves (privacy guarantees, terms of service) and how users engage with them. - International context and legal cross-overs. For users in jurisdictions with strong data-protection regimes (for example, the General Data Protection Regulation in the UK/EU), the fact that prompt-data can be subject to U.S. warrant may raise questions about extraterritorial access and data flow compliance. In short: this isn’t just another law-enforcement request. It marks the first time a generative-AI provider has been legally compelled to unmask a user and disclose their prompt history. ============ ↳I track how stories like this shape the ethics and governance of AI. You can find deeper analysis at discarded.ai. #AISafety #AIRegulation #Privacy #Governance #Ethics Image AI Generated

The next big data privacy scandal in 2026 is not surveillance. It is surveillance pricing. Two people can buy the same thing on the same day and pay different prices because their data told the system they would tolerate it. This is the part more people need to understand. The next privacy battle is not only about: “Who has my data?” It is also about: “What are they doing with it?” Because once companies know your location, device type, browsing behaviour, repeat visits, urgency signals, and purchase history, privacy becomes a pricing issue. We are already seeing signals of this. Uber openly calls it surge pricing. Airbnb has Smart Pricing. Amazon lets sellers automate price changes in real time. Hotels and airlines have used dynamic pricing for years. In 2025, India’s consumer affairs ministry sent notices to Ola and Uber after allegations that identical rides were being priced differently on Apple and Android phones. So, what changes the privacy conversation is when dynamic pricing stops reacting only to market demand and starts learning from the customer in front of it. This is why I think the most important privacy question in 2026 is no longer: “Was my data leaked?” It is: “Is my data being used to influence the price, urgency, ranking, or offer I see?” Think about everyday Indian internet behaviour: You check a flight 4 times from the same laptop. You open a hotel app from a premium phone. You try booking a cab during rain, from a high-income pin code, late at night. You revisit the same product after showing clear buying intent. You may still call it convenience. But increasingly, it can also become behavioural exploitation. Because the moment customers feel the system knows them well enough to charge them more, trust collapses. And once trust collapses, growth gets expensive. My view is simple: Data privacy in 2026 is not just about protecting people from theft. It is about protecting people from invisible disadvantage. That is the conversation more founders, platforms, and regulators need to have now. Whats your surveillance pricing case you faced? Seqrite #DataPrivacy #DynamicPricing #AI #ConsumerRights #DigitalEconomy #Privacy #TechPolicy #StartupIndia #CyberSecurity #TrustInTechnology

𝟔𝟔% 𝐨𝐟 𝐀𝐈 𝐮𝐬𝐞𝐫𝐬 𝐬𝐚𝐲 𝐝𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐬 𝐭𝐡𝐞𝐢𝐫 𝐭𝐨𝐩 𝐜𝐨𝐧𝐜𝐞𝐫𝐧. What does that tell us? Trust isn’t just a feature - it’s the foundation of AI’s future. When breaches happen, the cost isn’t measured in fines or headlines alone - it’s measured in lost trust. I recently spoke with a healthcare executive who shared a haunting story: after a data breach, patients stopped using their app - not because they didn’t need the service, but because they no longer felt safe. 𝐓𝐡𝐢𝐬 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐩𝐞𝐨𝐩𝐥𝐞’𝐬 𝐥𝐢𝐯𝐞𝐬 - 𝐭𝐫𝐮𝐬𝐭 𝐛𝐫𝐨𝐤𝐞𝐧, 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐬𝐡𝐚𝐭𝐭𝐞𝐫𝐞𝐝. Consider the October 2023 incident at 23andMe: unauthorized access exposed the genetic and personal information of 6.9 million users. Imagine seeing your most private data compromised. At Deloitte, we’ve helped organizations turn privacy challenges into opportunities by embedding trust into their AI strategies. For example, we recently partnered with a global financial institution to design a privacy-by-design framework that not only met regulatory requirements but also restored customer confidence. The result? A 15% increase in customer engagement within six months. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐫𝐞𝐛𝐮𝐢𝐥𝐝 𝐭𝐫𝐮𝐬𝐭 𝐰𝐡𝐞𝐧 𝐢𝐭’𝐬 𝐥𝐨𝐬𝐭? ✔️ 𝐓𝐮𝐫𝐧 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐧𝐭𝐨 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐦𝐞𝐧𝐭: Privacy isn’t just about compliance. It’s about empowering customers to own their data. When people feel in control, they trust more. ✔️ 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐏𝐫𝐨𝐭𝐞𝐜𝐭 𝐏𝐫𝐢𝐯𝐚𝐜𝐲: AI can do more than process data, it can safeguard it. Predictive privacy models can spot risks before they become problems, demonstrating your commitment to trust and innovation. ✔️ 𝐋𝐞𝐚𝐝 𝐰𝐢𝐭𝐡 𝐄𝐭𝐡𝐢𝐜𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Collaborate with peers, regulators, and even competitors to set new privacy standards. Customers notice when you lead the charge for their protection. ✔️ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐀𝐧𝐨𝐧𝐲𝐦𝐢𝐭𝐲: Techniques like differential privacy ensure sensitive data remains safe while enabling innovation. Your customers shouldn’t have to trade their privacy for progress. Trust is fragile, but it’s also resilient when leaders take responsibility. AI without trust isn’t just limited - it’s destined to fail. 𝐇𝐨𝐰 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐫𝐞𝐠𝐚𝐢𝐧 𝐭𝐫𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧? 𝐋𝐞𝐭’𝐬 𝐬𝐡𝐚𝐫𝐞 𝐚𝐧𝐝 𝐢𝐧𝐬𝐩𝐢𝐫𝐞 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫 👇 #AI #DataPrivacy #Leadership #CustomerTrust #Ethics

Can You Trust Your Data the Way You Trust Your Best Team Member? Do you know the feeling when you walk into a meeting and rely on that colleague who always has the correct information? You trust them to steer the conversation, to answer tough questions, and to keep everyone on track. What if data could be the same way—reliable, trustworthy, always there when you need it? In business, we often talk about data being "the new oil," but let’s be honest: without proper management, it’s more like a messy garage full of random bits and pieces. It’s easy to forget how essential data trust is until something goes wrong—decisions are based on faulty numbers, reports are incomplete, and suddenly, you’re stuck cleaning up a mess. So, how do we ensure data is as trustworthy as that colleague you rely on? It starts with building a solid foundation through these nine pillars: ➤ Master Data Management (MDM): Consider MDM the colleague who always keeps the big picture in check, ensuring everything aligns and everyone is on the same page.     ➤ Reference Data Management (RDM): Have you ever been in a meeting where everyone uses a different term for the same thing? RDM removes the confusion by standardising key data categories across your business. ➤ Metadata Management: Metadata is like the notes and context we make on a project. It tracks how, when, and why decisions were made, so you can always refer to them later.     ➤ Data Catalog: Imagine a digital filing cabinet that’s not only organised but searchable, easy to navigate, and quick to find exactly what you need.     ➤ Data Lineage: This is your project’s timeline, tracking each step of the data’s journey so you always know where it has been and is going.     ➤ Data Versioning: Data evolves as we update project plans. Versioning keeps track of every change so you can revisit previous versions or understand shifts when needed.     ➤ Data Provenance: Provenance is the backstory—understanding where your data originated helps you assess its trustworthiness and quality.     ➤ Data Lifecycle Management: Data doesn’t last forever, just like projects have deadlines. Lifecycle management ensures your data is used and protected appropriately throughout its life.     ➤ Data Profiling: Consider profiling a health check for your data, spotting potential errors or inconsistencies before they affect business decisions. When we get these pillars right, data goes from being just a tool to being a trusted ally—one you can count on to help make decisions, drive strategies, and ultimately support growth. So, what pillar would you focus on to make your data more trustworthy? Cheers! Deepak Bhardwaj

The recent $95 million settlement by Apple over allegations of Siri-enabled privacy breaches underscores a pivotal moment for tech professionals navigating the delicate balance between innovation and user trust. As voice assistants become integral to our daily lives, this case illuminates the risks of unintentional data collection and the potential fallout—financial, reputational, and ethical—when consumer privacy is perceived as compromised. For engineers, developers, and business leaders, this serves as a critical reminder: robust privacy safeguards and transparent practices aren’t optional—they’re fundamental to maintaining user loyalty in an increasingly data-sensitive world. This moment invites the tech community to reimagine AI solutions that are not only cutting-edge but also deeply rooted in trust and accountability. How can we, as innovators, ensure that technology enhances lives while respecting the privacy and trust of its users? #TechNews #Innovation #Privacy #Apple

📌 The Modern Data Quality Framework for BI Every company wants better dashboards, better insights, better AI. But very few stop to ask the one question that actually matters: Can we trust the data we’re using in the first place? Because the hard truth is this: Most data issues don’t come from tools. They come from unreliable foundations that nobody notices until something breaks in production. When I look at the teams that consistently ship trustworthy data, there’s always the same pattern behind the scenes. Let me walk you through my reasoning. 1️⃣ 𝐓𝐡𝐞 5 𝐏𝐢𝐥𝐥𝐚𝐫𝐬 𝐀𝐫𝐞 𝐒𝐭𝐢𝐥𝐥 𝐭𝐡𝐞 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐏𝐨𝐢𝐧𝐭 Accuracy, completeness, consistency, timeliness, and validity. We all know them. But most teams still treat these as “definitions.” On the other hand, the best teams treat them as operational targets. It’s a completely different mindset. Accuracy isn’t “nice to have.” It’s whether your revenue aligns with reality. Completeness isn’t a rule. It’s whether you trust the KPI enough to act on it. Everything changes once you start thinking this way. 2️⃣ 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐂𝐡𝐞𝐜𝐤𝐬 𝐌𝐚𝐤𝐞 𝐨𝐫 𝐁𝐫𝐞𝐚𝐤 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 This is where issues hide. I can’t count the number of times I’ve seen dashboards fail not because the model was wrong but because nobody noticed: → A column changed type → A pipeline skipped 2% of rows → A source table silently dropped a field → A null explosion went undetected for weeks This layer is invisible to most of the business, yet it’s the one that protects trust. If you don’t have anomaly detection or CI/CD tests, you’re relying on luck. And luck is not a data strategy. 3️⃣ 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐌𝐚𝐤𝐞𝐬 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐖𝐨𝐫𝐤 Data catalogs, lineage, ownership, contracts. People talk about them like buzzwords, but the impact is very real. Lineage isn’t a diagram. It’s how you debug issues in minutes instead of days. Contracts aren’t bureaucracy. They’re how producers guarantee stability for downstream teams. Stewardship isn’t a title. It’s accountability. What I’ve learned from my experience is simple: When governance is strong, you don’t spend your life firefighting. 4️⃣ 𝐀𝐭 𝐭𝐡𝐞 𝐂𝐞𝐧𝐭𝐞𝐫 𝐨𝐟 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠: 𝐃𝐚𝐭𝐚 𝐓𝐫𝐮𝐬𝐭 This is the part people underestimate. Trust is not something you “announce” on a slide. It’s something you earn, build, and protect over time. It shows up in adoption. It shows up in business confidence. It shows up in how quickly you can respond when an anomaly hits. Trust is the real KPI. And when it’s strong, everything else becomes easier. Executives stop asking "where did this number come from." Why does this matter so much? Because a lot of companies are scaling GenAI without first fixing data quality. And when AI learns from unreliable data, it becomes unreliable itself. If you want to improve decision-making, data quality is not a side topic. Everything else is built on top of it.

In an era where privacy is the ultimate luxury, Apple—a company renowned for its strong stance on user privacy—has found itself at the center of a massive controversy. The tech giant has agreed to pay $95 million (₹814 crores) in a lawsuit that accused Siri, its voice assistant, of recording private conversations without user consent and sharing them with third parties. The Allegations The case stemmed from claims that Siri was being inadvertently activated by users, leading to the recording of highly personal conversations. Even more troubling, these recordings were allegedly sent to third-party contractors for evaluation without user knowledge. This scandal was first exposed in 2019 by The Guardian, which reported that Apple’s contractors listened to sensitive discussions, including: - Medical consultations, - Private business meetings, and - Intimate personal exchanges. While Apple denied any wrongdoing, this case highlights a glaring gap between privacy promises and actual practices. The Settlement Here’s what it entails: - Payout to Users: Thousands of affected users will receive compensation of $24 (₹1,700) per device. - Legal Fees: A significant portion of the settlement—up to 30%—will go to the attorneys involved. - Apple’s Stance: The company maintains it did not violate user trust, but settled to avoid prolonged litigation. The Bigger Picture This incident is not just about Apple. It’s a wake-up call for every company operating in the digital age: 1. Transparency is Non-Negotiable: Users have the right to know how their data is collected, stored, and used. 2. Trust is Fragile: Even giants like Apple can face reputational damage if user privacy is compromised. 3. Accountability Must Follow Innovation: Companies can no longer prioritize profits over ethics. For Consumers - Be Informed: Read privacy policies, however tedious they may seem. - Be Proactive: Use device settings to limit data sharing and disable features like voice assistants when not in use. - Advocate for Stricter Regulations: Governments must enforce stringent data protection laws to safeguard user rights. Apple's Future Steps Since the controversy, Apple has taken steps to rebuild user trust, including: - Disabling human grading of Siri recordings, - Allowing users to opt out of sharing their data, and - Strengthening their privacy policies. However, this lawsuit serves as a stark reminder: Even the most trusted brands must remain under constant scrutiny. What’s Next? As users, we need to push for digital ethics and ensure companies treat our data with the respect it deserves. Should stricter penalties be imposed for such violations? Are current privacy laws sufficient in protecting us? Let’s discuss! #DataPrivacy #AppleLawsuit #TechnologyEthics #DigitalSecurity #Siri #ConsumerRights #TransparencyMatters #EthicalTech

🚨 AI Privacy Risks & Mitigations Large Language Models (LLMs), by Isabel Barberá, is the 107-page report about AI & Privacy you were waiting for! [Bookmark & share below]. Topics covered: - Background "This section introduces Large Language Models, how they work, and their common applications. It also discusses performance evaluation measures, helping readers understand the foundational aspects of LLM systems." - Data Flow and Associated Privacy Risks in LLM Systems "Here, we explore how privacy risks emerge across different LLM service models, emphasizing the importance of understanding data flows throughout the AI lifecycle. This section also identifies risks and mitigations and examines roles and responsibilities under the AI Act and the GDPR." - Data Protection and Privacy Risk Assessment: Risk Identification "This section outlines criteria for identifying risks and provides examples of privacy risks specific to LLM systems. Developers and users can use this section as a starting point for identifying risks in their own systems." - Data Protection and Privacy Risk Assessment: Risk Estimation & Evaluation "Guidance on how to analyse, classify and assess privacy risks is provided here, with criteria for evaluating both the probability and severity of risks. This section explains how to derive a final risk evaluation to prioritize mitigation efforts effectively." - Data Protection and Privacy Risk Control "This section details risk treatment strategies, offering practical mitigation measures for common privacy risks in LLM systems. It also discusses residual risk acceptance and the iterative nature of risk management in AI systems." - Residual Risk Evaluation "Evaluating residual risks after mitigation is essential to ensure risks fall within acceptable thresholds and do not require further action. This section outlines how residual risks are evaluated to determine whether additional mitigation is needed or if the model or LLM system is ready for deployment." - Review & Monitor "This section covers the importance of reviewing risk management activities and maintaining a risk register. It also highlights the importance of continuous monitoring to detect emerging risks, assess real-world impact, and refine mitigation strategies." - Examples of LLM Systems’ Risk Assessments "Three detailed use cases are provided to demonstrate the application of the risk management framework in real-world scenarios. These examples illustrate how risks can be identified, assessed, and mitigated across various contexts." - Reference to Tools, Methodologies, Benchmarks, and Guidance "The final section compiles tools, evaluation metrics, benchmarks, methodologies, and standards to support developers and users in managing risks and evaluating the performance of LLM systems." 👉 Download it below. 👉 NEVER MISS my AI governance updates: join my newsletter's 58,500+ subscribers (below). #AI #AIGovernance #Privacy #DataProtection #AIRegulation #EDPB