Data Protection Practices

🚨 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

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

DPDP is India’s GDPR moment. And nobody’s building for it. In 2018, I saw hundreds of EU startups go silent after GDPR. Not because they were hacked. But because their entire business model relied on collecting user data without structure, limits, or accountability. The only companies that survived? The ones who built privacy into the product, and not as a last-minute checkbox, but as part of their architecture. Now, in 2025, I see the same storm brewing in India. So even if you're just a 3-person startup with 2,000 users, you’re still a data fiduciary. DPDP doesn’t care if you’re bootstrapped. It doesn’t care if your data is on Firebase or S3. It doesn’t care if your customers “never read the privacy policy anyway.” It only cares about one thing: Are you accountable for the personal data you collect? If you process personal data: identity, biometrics, usage, location, health, you’re liable. And here’s what nobody’s telling you: It’s not about ₹250 crore fines. It’s about invisible erosion.  • Your Google Ads won't convert like they used to. • Your data broker tools will quietly get you blacklisted. • Your pipeline will dry up as procurement teams now ask privacy questions. • Your cloud infra bills will spike as you retro-build audit logs & consent systems. • Your pitch deck will be rejected because you didn’t appoint a Data Protection Officer. So before you optimize your funnel, ask yourself: Do you have revocable consent? Can users delete their data today, not “in the next release”? Do you track who accessed what, when, and why? Can you issue a full access report within 24 hours? Is your AI model trained on personally identifiable data? I think the first Indian startup that gets caught misusing or leaking personal data under DPDP, won’t just pay fines, but also lose customers, investors, credibility and their market. Fast. What’s one DPDP blindspot you think Indian startups are ignoring? Seqrite #DPDP #DataPrivacy #StartupIndia #IndianStartups #PrivacyByDesign #CyberSecurity #TechPolicy #FounderInsights #Compliance #AIandPrivacy #Founder

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

Think Before You Share: The Hidden Cybersecurity Risks of Social Media 🚨🔐 In an era where data is the new currency, every post, check-in, or status update can serve as an intelligence goldmine for cybercriminals. What seems like harmless sharing—your vacation photos, workplace updates, or even a "fun fact" about your first pet—can be weaponized against you. 🔥 How Oversharing Exposes You to Cyber Threats 🔹 Geo-Tagging & Real-Time Location Leaks Sharing your location makes you an easy target. Cybercriminals use this data to track routines, monitor absences, or even launch physical security threats such as home burglaries. 🔹 Social Engineering & Credential Harvesting Those "what’s your mother’s maiden name?" or "which city were you born in?" quiz posts are a hacker’s playground. Attackers scrape these responses to guess password security questions or craft highly convincing phishing emails. 🔹 Metadata & Digital Fingerprinting Every photo you upload contains EXIF metadata (including GPS coordinates and device details). Attackers can extract this information, identify locations, and even map out behavior patterns for targeted cyberattacks. 🔹 OSINT (Open-Source Intelligence) Reconnaissance Threat actors don’t need sophisticated hacking tools when your social media profile provides a full dossier on your life. They correlate job roles, connections, and public interactions to execute whaling attacks, corporate espionage, or deepfake impersonations. 🔹 Dark Web Data Correlation Your exposed social media details can be cross-referenced with breached databases. If your credentials have been compromised in past data leaks, attackers can launch credential stuffing attacks to hijack your accounts. 🔐 Cyber-Hygiene: Best Practices for Social Media Security ✅ Restrict Profile Visibility – Limit exposure by setting profiles to private and segmenting audiences for sensitive updates. ✅ Sanitize Metadata Before Uploading – Use tools to strip EXIF data from images before posting. ✅ Implement Multi-Factor Authentication (MFA) – Enforce adaptive authentication to prevent unauthorized account access. ✅ Zero-Trust Mindset – Assume any publicly shared data can be aggregated, exploited, or weaponized against you. ✅ Monitor for Breach Exposure – Regularly check if your credentials are compromised using breach notification services like Have I Been Pwned. 🔎 The Internet doesn’t forget. Every post contributes to your digital footprint—control it before someone else does. 💬 Have you ever reconsidered a social media post due to security concerns? Drop your thoughts below! 👇 #CyberSecurity #SocialMediaThreats #Infosec #PrivacyMatters #DataProtection #Phishing #CyberSecurity #ThreatIntelligence #ZeroTrust #CyberThreats #infosec #cybersecuritytips #cybersecurityawareness #informationsecurity #networking #networksecurity #cyberattacks #CyberRisk #CyberHygiene #CyberThreats #ITSecurity #InsiderThreats #informationtechnology #technicalsupport

🔐 Designing For Privacy UX. Privacy isn’t about hiding something, but protecting user’s personal space. UX guidelines on how to design more respectful, private experiences that drive long-term loyalty ↓ 🤔 When data requests feel intrusive, users enter fake data or give in. ✅ Privacy is about user’s control of what happens to their data. ✅ Privacy by default: features should work with min data required. 🚫 Don’t ask for permissions that you don’t need at the moment. ✅ Right to be forgotten → allow users to delete data in settings. ✅ Data portability → allow users to take their data with them. ✅ Hidden Unsub links downgrade email reach (marked as spam). ✅ Neutral choices → give people real choices with neutral defaults. ✅ Data you don't ask for is the data you can't lose in a breach. ✅ Explain then ask → if you need user’s data, first explain why. ✅ Try before commit → show and explain value before asking for data. ✅ Remind me later → give people time to make a decision on their terms. ✅ Contextual consent → ask for data only when user’s action needs it. ✅ Automated data decay → delete user's data not used after X months. --- In many companies, privacy is treated as a technical hurdle to be cleared off. Companies thrive on user’s data for personalization, customized offers, better AI models — but also invasive targeting, ultra-precise tracking, behavioral predictions and eventually reselling data to the highest bidder. All of it isn’t only invasive and undermines trust — it also makes for slow experiences and advertising following you everywhere you go. Predictive models know a person is pregnant based on their browsing habits before they do. And once they do, ads, offers and messages will follow you everywhere you go — before your closest relatives hear it from you. When we speak about privacy, we often assume that that’s an exaggerated problem that doesn’t really affect us much. After all, we have nothing to hide, and so there is no harm in companies knowing a few things about us. But privacy isn’t about hiding something. It’s about protecting your personal space from external influence and manipulation. It’s about protecting your personal decisions and your intimate experiences, and having a choice to share them with people you trust and care of. Most people wouldn’t feel comfortable being observed by a camera during their work or during their spare time. Yet as we move from one page to the next, that’s exactly what happens, often without our consent. And just like web performance and accessibility, privacy is a part of user's experience. The good news is that European Commission is looking into modifying the way GDPR works. So users could tick a box in browser preferences, with privacy settings turned on by default. And then websites shouldn't be allowed to ask for consent because it's already not granted. I'm looking forward to that future. I’ve also put together a few practical books and useful resources in the comments below ↓

ChatGPT is not your friend. It’s a database. In July 2025, Google indexed over 4,500 ChatGPT conversations containing sensitive personal information. Because users clicked “Share,” and the system created public URLs. Google crawled, indexed and shared them. Here’s what surfaced: 🔸 Mental illness, addiction, and abuse 🔸 Names, locations, emails, resumes 🔸 Medical histories, legal strategies All searchable, linkable and public until OpenAI intervened: ✔️ The “Discoverable” sharing feature was disabled on July 31. ✔️ They are working with Google and other search engines to remove indexed chats. ✔️ OpenAI reminded users: deleting a chat from history does not delete the public link. Millions of people, including employees and customers are confiding in AI. They believe it’s private and safe. But it isn’t. It’s recording. Indexing. Storing. And when systems designed for experimentation are used for confession, the boundaries between personal risk and enterprise liability vanish. What are the implications for Boards? 1️⃣ Regulatory risk Under GDPR: 🔹 Data subjects have the right to erase, access, and informed consent. 🔹 Shared AI conversations with personal or sensitive data may violate these rights. 🔹 AI-generated prompts could fall under automated decision-making clauses. Under the EU AI Act: 🔹 Transparency, risk classification, and human oversight are mandatory. 🔹 This incident may be classified as a high-risk system failure in healthcare, HR, legal. 2️⃣ Legal risk There is currently no legal confidentiality in AI interactions. ✔️ Anything entered into AI could be subpoenaed, discoverable in court or leaked. ✔️ Companies are liable if employees share PII, IP, or client data via chatbots. ✔️ HR, Legal, and Compliance teams must assume AI logs are discoverable records. 3️⃣ Reputational risk People assumed they were talking to a trusted tool. Instead, they ended up on Google. For enterprises using AI for: ▫️ Coaching or mental health ▫️ HR assistance ▫️ Legal or compliance advisory ▫️ Customer service … this is a trust risk. Public exposure = brand damage. 4️⃣ Operational risk Many organisations lack: 📌 AI input/output governance 📌 Policies for AI use in confidential workflows 📌 Deletion/audit protocols for AI-linked data Takeaway If employees or customers treat ChatGPT like a coach, or colleague, ensure to treat it like a legal and technical system. That means: ✅ Create AI use and data handling policies ✅ Restrict use of genAI in regulated or sensitive domains ✅ Review GDPR/AI Act exposure for all shared AI features ✅ Treat all AI interactions as auditable records ✅ Demand transparency from vendors: what is stored, shared, indexed? Until regulators catch up and new legal protections exist, assume every AI interaction is public, permanent, and admissible. #AIgovernance #Boardroom #EUAIACT #DigitalTrust #Stratedge

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. 👇

Are you risking your company’s IP and customer personal data for the convenience of meeting transcription? Convenience is great, but not at the cost of accidentally donating your crown-jewel knowledge and customer personal data to someone else’s AI lab. AI-powered meeting transcription services are becoming increasingly popular - they offer so much convenience, sometimes even for free. I spent a few days combing through the actual Privacy Policies and Terms of Service for four popular AI notetakers—Otter.ai, Read.ai, Fireflies.ai, and tl;dv—to see whether they train their models on your conversations. I have no association with any of them, but what I found is worrying. Here’s the short version: 🔹 Otter.ai – On by default. Otter trains its speech-recognition models on 'de-identified' audio and text of your conversations. They claim that personal identifiers are stripped, but your confidential data still fuels their AI unless you negotiate a restriction. 🔹 Read.ai – Your choice. By default your data is not used. If you opt in to its Customer Experience Program, your transcripts can help improve the product. 🔹 Fireflies.ai – Aggregated-only. They forbid training on identifiable content, limiting themselves to anonymised usage statistics. No individual transcript feeds their AI. 🔹 tl;dv – Never. They explicitly prohibit using customer recordings for model training. Transcript snippets sent to their AI engine are anonymised, sharded, and not retained. Why it matters: Even “de-identified” data can leak competitive IP or sensitive customer information if models are ever breached or repurposed. Business recordings can contain personal data, meaning you’re still on the hook for consent, minimisation, and transfer safeguards. Your management, board and clients may assume you’ve locked this down; finding out later is awkward at best, non-compliant at worst. By the way - true anonymisation of data is exceptionally difficult, especially in complex data like speech. Claims that only 'deidentified' data is used for training needs to be scrutinised. Not one of the products reviewed provided any meaningful technical information about how they achieve this. What to do next: 1. Read the legal docs—marketing pages are full of assurances, but they don’t tell the full story. Read the privacy policies and terms of service. 2. Decide your red line: zero training, aggregated-only, or opt-in? 3. Configure or negotiate: most vendors offer enterprise DPAs or private-cloud options if you ask. 4. Review the consent flows: it’s not just your rights—your guests’ data is in play too. Have you asked the meeting participants if they are happy to hand their personal data and IP to a third party? Convenience is great, but not at the cost of accidentally donating your crown-jewel knowledge to someone else’s AI lab. I write about Doing AI Governance for real at ethos-ai.org. Subscribe for free analysis and guidance: https://ethos-ai.org #AIGovernance

Let’s try not to break the law today. I am being flooded with emails and demos pushing “helpful” apps that promise to quietly sit in on my online meetings. They are used on phones and intended to participate in online or face to meetings. They are promoted as especially “wonderful for remote workers”. Apparently, they give everyone an edge at work by “sneaking in this hack”. Here is how they work: They record the call on the phone by holding it near the online meeting on the computer. They upload voices and transcripts to AI. They run analysis on the content and can generate summaries, insights, and key takeaways. This is not just a bad idea. Using it can trigger real legal exposure. Here is why. 1. Recording meetings in the EU and UK is regulated activity Voices are personal data. Recording requires a lawful basis, clear advance notice, and strict purpose limitation. Covert, casual, or silent recording is not compliant. In some countries, it is criminal. 2. Uploading recordings to AI is secondary processing That raises the legal bar substantially. Participants must be explicitly informed that AI analysis will occur. Consent must be real, specific, and documented. Data transfers, retention, and vendor agreements matter. Most of these tools fail spectacularly on all counts. 3. “Just share this shortcut with your boss for brownie points” is actually brilliant advice! Leaders need to be alerted to this major security leak. And, if your company policy does not explicitly address meeting recording and AI analysis already, that is a governance gap. And governance gaps become liability. You boss will want to know. Corporate policy should already be clear on this. If it is not, it needs fixing immediately. 4. Enterprises already have compliant options If recording or summarization is allowed at all by your company, it should be done only through approved enterprise tools, configured to match organizational policy and regional law. Not through individual consumer apps quietly scraping meetings. Let me be very clear. If any participant is located in the EU or the UK, this category of tool is almost always the worst possible idea. In a long list of other locations, it is either illegal and/or a privacy violation, and will most likely be just cause for termination. Do not use it. Do not recommend it. Do not normalize it. Compliance is not anti-innovation. But pretending regulation does not exist is not innovation either.