Managing Privacy Issues in User Profiling

Embedding privacy-by-design principles into new projects and systems ensures that privacy is considered throughout the entire lifecycle of the project, from initial design through development, deployment, and decommissioning. Here’s how to embed these principles with examples: Privacy-by-Design Principles 1. Proactive not Reactive; Preventative not Remedial: • Embed privacy features proactively rather than as an afterthought. • Example: When designing a new customer feedback app, include encryption and secure data storage from the outset to prevent data breaches. 2. Privacy as the Default Setting: • Ensure personal data is automatically protected in any IT system or business practice. • Example: A new online service automatically opts users out of data sharing by default. Users must explicitly opt in if they choose to share their data. 3. Privacy Embedded into Design: • Integrate privacy into the architecture of IT systems and business practices. • Example: When developing a mobile banking app, ensure that data minimization is a core feature, collecting only the data necessary for the service. 4. Full Functionality – Positive-Sum, not Zero-Sum: • Avoid unnecessary trade-offs; ensure both privacy and functionality are achievable. • Example: A health app provides personalized services without compromising user privacy by using anonymized data for analytics. 5. End-to-End Security – Lifecycle Protection: • Secure personal data throughout its entire lifecycle, from collection to deletion. • Example: In a new document management system, implement encryption, secure access controls, and regular data deletion policies. 6. Visibility and Transparency – Keep it Open: • Ensure all stakeholders are aware of data practices, and the systems are open to scrutiny. • Example: A cloud service platform includes clear privacy policies, regular privacy impact assessments (PIAs), and audit logs available to users and regulators. 7. Respect for User Privacy – Keep it User-Centric: • Prioritize user privacy preferences and control. • Example: A social media platform allows users to easily manage their privacy settings and provides tools for users to understand and control how their data is used.

What are we discussing with our clients with children-facing products following the UK Information Commissioner's Office latest investigation into social media platforms and video sharing platforms: 🔹 Profile Settings: You need to set your children’s profiles to private by default, particularly where they also allow contact from strangers by default. If not - you need to demonstrate a compelling reason. 🔹 Geolocation: Have geolocation data collection set to "private" by default; do not nudge children to switch geolocation settings on or encourage them to share their location with others through tagging or including location when posting content. If you make geolocation information public, do not share more granular information and always enable going back to the "private" setting. 🔹 Profiling/targeted advertising: profiling much be off by default unless there is a compelling reason in the best interests of the child. If you do profile - you must make it clear what personal information is being collected from children or how it is being used for targeted advertising. You also must be able to demonstrate why/how you are doing this in the best interests of the child, for example, refraining from excessive data collection and giving children options to control advertising preferences. 🔹 Recommender systems: Recommender systems are algorithmic processes that use personal information and profiling to learn the preferences and interests of the user to suggest or deliver content to them. Your privacy notice must be clear about the specifics of how you use personal information to make recommendations, or what measures you take to protect children’s privacy when doing so. You must ensure that you are not using your recommender systems to show children inappropriate or harmful content 🔹 Information of children under 13 years old: Self-declaration is unlikely to be appropriate when using children’s information in a way that raises high data processing risks such as: Large scale profiling; Invisible processing; Tracking or target children for marketing purposes or to offer services directly to them. You should adopt an age assurance method with an appropriate level of technical accuracy, and one that operates in a fair way to users 🔹 Consent: Check age at log-in not after. ICO is looking at platforms that rely on consent as their lawful basis for processing the information of users that are not logged in, particularly those under 13 years old who would require verified parental consent. #dataprivacy #dataprotection #privacyFOMO https://lnkd.in/e_tTYZW8 -

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.

⚠️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.

The Case for App Scanning and SDK Governance: Lessons from Texas Lawsuit The State of Texas has filed a lawsuit against a large insurance company and its analytics subsidiary for alleged violations of the Texas Data Privacy and Security Act (TDPSA), the Data Broker Law, and the Texas Insurance Code. What happened: - A large insurance company and its analytics subsidiary created a Software Development Kit (SDK), that was embedded into third-party apps offering location-based services. - This SDK secretly collected sensitive user data, including precise locations, speed, direction, and other phone sensor data, without users' awareness. - The collected data was used to create a massive driving behaviour database covering millions of users. - This data was monetized, influencing insurance premiums and policies, often without users' knowledge or consent. - Users were not informed about how their data was being collected or shared, and privacy policies were not clear or accessible. Key issues: 1) No user consent: People did not know their data was being collected or sold. 2) Inaccurate profiling: The SDK often mistook passengers or other scenarios as "bad driving," leading to misleading profiles. 3 ) Non-compliance: The analytics subsidiary failed to register as a data broker, as required by Texas law. Why this matters: This case highlights the risks of hidden data collection in apps. It shows how companies can misuse sensitive data and the importance of protecting user privacy through stronger controls. The way forward: To effectively address these risks, organizations must take assertive action by implementing the following measures - a) Conduct regular mobile app scanning: Analyze apps weekly or bi-weekly to identify permissions, embedded SDKs, and dataflows. b) Govern SDKs effectively: Establish strict policies for integrating and monitoring SDKs. Require transparency from SDK providers about what data is collected, how it is used, and who it is shared with. Avoid SDKs that fail to meet these standards. c) Monitor hidden dataflows: SDKs often operate in the background and can rely on permissions obtained by the app to collect sensitive data. Regularly audit these dataflows to uncover any implicit collection or sharing practices and address potential violations proactively. d) Communicate transparently with users: Update #privacy policies to clearly explain what data is collected, how it will be used, and who it will be shared with. Obtain explicit consent before collecting or sharing sensitive data. The risks of hidden #dataflows and implicit data collection are significant, especially as #SDKs become more complex. How frequently does your team #audit apps for SDK behaviors and permissions? What tools or strategies have you found most effective in uncovering hidden #datasharing?

Many platforms today, like WhatsApp, Facebook, YouTube, and Amazon, automatically use your personal information for various purposes, including training their AI systems. Unfortunately, these companies often do not provide a transparent way for users to stop this, making it difficult to protect your data. Most companies don’t offer clear options for people to control how their data is used. Users often lack access to tools that allow them to fully opt out, meaning their data is continuously collected and processed without clear consent. However, there are still ways to limit how much of your personal information is used by these companies in India. 1. LinkedIn: LinkedIn recently opted users into allowing their data to be used for training AI models, without much notice. However, you can still opt out. From your LinkedIn homepage (on mobile or desktop), go to your profile settings, find “Data Privacy,” and under “How LinkedIn uses your data,” toggle off the option for generative AI improvements. 2. Google (Gmail, Docs, etc.): Google's services like Gmail and Docs often use predictive text features, which analyze your emails, chats, and other data to suggest words or phrases while you type. If you'd like to opt out of Google using your data to personalize these services, you can disable the “smart” features in your settings. Go to Gmail's settings, click on “See all settings,” and find the section called "Smart Features and Personalization." Here, you can turn it off. 3. X (formerly Twitter): Go to "Settings and Privacy," then "Privacy and Safety." Under "Data Sharing and Personalization," you can turn off the setting that allows X to use your posts and interactions for AI training. 4. Snapchat: Snapchat uses data from its AI chatbot to train its systems. It also collects data like your location unless you turn it off. Here's how you can stop the app from using your location: -iPhone: Go to your phone's settings, find Snapchat, and select "Location." Set it to "Never." Then, open the Snapchat app, go to your profile, click on the settings icon, and clear your AI data under "Privacy Controls." -Android: Follow similar steps by long-pressing on the Snapchat icon, tapping "App Info," and then managing location permissions. Inside the app, clear your AI data from "Account Actions." 5. Meta (Facebook, Instagram): Opting out of Meta’s AI usage is more complicated, and not fully available in many countries, including India. You can request Meta to stop using your data for AI, but it involves filling out a complex form and even providing proof, like screenshots, to support your request. Unfortunately, Meta does not guarantee that your request will be honored, and the process is far from straightforward. #DataPrivacy #CyberLaw #DataProtection #PrivacyMatters #DigitalSecurity #IndianLaw #CyberSecurity #ProtectYourData #DigitalPrivacy #PrivacyAwareness #StaySecure #DataProtectionIndia #wdp #womendataprotectionfoundation