Data Privacy Regulations for Businesses

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.

Level Up Your Data Career from Data Engineer to a Data Architect❗️ 👉 Here's a comprehensive breakdown of the skills and responsibilities that define a top-notch Data Architect by Deepak Bhardwaj: ➡️ Technical Expertise ↳ Database Management: Master both SQL and NoSQL databases ↳ Data Modelling: Design data models aligned with business needs ↳ ETL Processes: Proficiency in modern ETL tools and techniques ↳ Big Data Technologies: Familiarity with Data Lake, Hadoop, and Spark ↳ Cloud Platforms: Experience with AWS, Azure, or Google Cloud ➡️ Problem Solving ↳ Critical Analysis: Interpret complex data sets to inform decision-making ↳ Data Quality Assurance: Ensure data accuracy, consistency, and reliability ↳ Issue Resolution: Guide teams in troubleshooting data-related problems ➡️ Communication & Collaboration ↳ Stakeholder Communication: Explain technical concepts to non-technical audiences ↳ Team Leadership: Effectively lead and mentor data teams ↳ Cross-Department Collaboration: Work with IT, business analytics, and compliance teams ➡️ Strategic Thinking ↳ Business Understanding: Align data architecture with organizational goals ↳ Data Governance: Establish and enforce data management policies ↳ Innovation: Integrate new technologies to improve data systems ➡️ Documentation & Compliance ↳ Comprehensive Documentation: Maintain detailed records of architecture, processes, and standards ↳ Regulatory Adherence: Ensure compliance with data regulations and industry standards ➡️ Data Security & Privacy ↳ Threat Modeling: Identify potential security vulnerabilities in data architectures ↳ Encryption Strategies: Design end-to-end data protection schemes ↳ Access Control: Develop sophisticated role-based access systems ↳ Privacy by Design: Incorporate data anonymization and pseudonymization techniques ↳ Compliance Expertise: Stay current with evolving data protection regulations (GDPR, CCPA, etc.) ➡️ System Integration & Interoperability ↳ API Design: Create robust, scalable APIs for data exchange ↳ Middleware Expertise: Understand various integration patterns and technologies ↳ Data Standards: Implement industry-specific data exchange standards ↳ Legacy System Integration: Develop strategies to connect modern and legacy systems ↳ Microservices Architecture: Design data flows in distributed system environments 📍These skills complement the foundational areas, enhancing your ability to create secure, interconnected data ecosystems that meet modern enterprise needs. #data #engineering #cloudcomputing #dataarchitect #bigdata #business #growth #innovation

All risk is enterprise risk. Cybersecurity Risk Management (CSRM) must be part of Enterprise Risk Management (ERM). Many companies think managing cyber risks is: ╳ Just an IT problem. ╳ Isolated from other risks. ╳ A low-priority task. But in reality, it is: ☑ A key part of the entire risk strategy. Here are the key steps to integrate cybersecurity risk into enterprise risk management: 1. Unified Risk Management ↳ Integrating CSRM into ERM helps handle all enterprise risks effectively. 2. Top-Level Involvement ↳ Top management must be involved in managing cyber risks along with other risks. 3. Contextual Consideration ↳ Cyber risks should be considered in the context of the enterprise's mission, financial, reputational, and technical risks. 4. Aligned Risk Appetite ↳ Align risk appetite and tolerance between enterprise management levels and cybersecurity systems. 5. Holistic Approach ↳ Adopt a holistic approach to identify, prioritize, and treat risks across the organization. 6. Common Risk Language ↳ Establish a common language around risk that permeates all levels of the organization. 7. Continuous Improvement ↳ Monitor, evaluate, and adjust risk management strategies continuously. 8. Clear Governance ↳ Ensure clear governance structures to support proactive risk management. 9. Digital Dependency ↳ Understand how cybersecurity risks affect business continuity, customer trust, and regulatory compliance. 10. Strategic Enabler ↳ Prioritize risk management as both a strategic business enabler and a protective measure. 11. Risk Register ↳ Use a unified risk register to consolidate and communicate risks effectively. 12. Organizational Culture ↳ Foster a culture that values risk management as important for achieving strategic goals. Integrating cybersecurity risk into enterprise risk management isn't just a technical task. It's a strategic necessity. 💬 Leave a comment — how does your company handle cyber risk? ➕ Follow Andrey Gubarev for more posts like this

Your Smarthome Is Talking—But Who’s Listening? Smart home devices offer incredible convenience, allowing us to control lights, locks, appliances, and cameras remotely. However, each of these Internet of Things (IoT) devices also represents a potential vulnerability in your home’s digital perimeter. Many users install these gadgets without changing default settings, leaving them wide open to cyber intrusions. Threat actors have exploited poorly secured devices to spy on households, manipulate smart locks, or gain access to broader home networks. To avoid these risks, we must treat IoT devices with the same caution as computers or smartphones. That means using strong, unique passwords, enabling two-factor authentication where possible, and consistently updating firmware. Network segmentation is another smart move—placing IoT devices on a separate Wi-Fi network to prevent them from interacting with sensitive systems like work laptops or home servers. Finally, it’s important to evaluate the necessity of each new connected device. Ask yourself if the benefits truly outweigh the privacy risks. Not every gadget needs to be online, and sometimes convenience can come at the cost of security. In an age where even your thermostat or baby monitor can be exploited, a little common sense goes a long way in protecting your privacy and peace of mind. #cybersecurity #IoT #smarthomes #securitycameras #babymonitors #webcams #smartappliances

Isabel Barberá: "This document provides practical guidance and tools for developers and users of Large Language Model (LLM) based systems to manage privacy risks associated with these technologies. The risk management methodology outlined in this document is designed to help developers and users systematically identify, assess, and mitigate privacy and data protection risks, supporting the responsible development and deployment of LLM systems. This guidance also supports the requirements of the GDPR Article 25 Data protection by design and by default and Article 32 Security of processing by offering technical and organizational measures to help ensure an appropriate level of security and data protection. However, the guidance is not intended to replace a Data Protection Impact Assessment (DPIA) as required under Article 35 of the GDPR. Instead, it complements the DPIA process by addressing privacy risks specific to LLM systems, thereby enhancing the robustness of such assessments. Guidance for Readers > For Developers: Use this guidance to integrate privacy risk management into the development lifecycle and deployment of your LLM based systems, from understanding data flows to how to implement risk identification and mitigation measures. > For Users: Refer to this document to evaluate the privacy risks associated with LLM systems you plan to deploy and use, helping you adopt responsible practices and protect individuals’ privacy. " >For Decision-makers: The structured methodology and use case examples will help you assess the compliance of LLM systems and make informed risk-based decision" European Data Protection Board

The European Parliament has just adopted the European Health Data Space (EHDS) regulation, marking a historical moment for digital health in the EU. This monumental step will have a profound impact on the rights of natural persons to their electronic health data and on the possibilities for the reuse of such data. The EHDS will empower individuals to access their electronic health data through patient portals or apps. This is aligned with the goals of the Digital Decade policy programme 2030: 100 % citizens having access to their electronic health records. Furthermore, the regulation will ensure that electronic health data follows patients when they seek care from different healthcare providers within their own Member State or across the EU. Through the European electronic health record exchange format, the regulation will promote further harmonisation of structures in data exchanged by electronic health record systems. In addition to structured data, the format should also support the exchange of unstructured clinical documents, to ensure the implementation of the rights of natural persons. The electronic health record systems will be certified to guarantee their compliance with interoperability and logging requirements. To ensure secure access to electronic health data for secondary use purposes, the regulation will establish a network of health data access bodies in each Member State. This will accelerate research and innovation in the EU, contributing to the development of new treatments and advanced health solutions. Two key infrastructures, MyHealth@EU and HealthData@EU, will be established to support the implementation of the EHDS. While the creation of these infrastructures is a significant undertaking, progress is well underway. The main components of MyHealth@EU are already operational, and pilots are ongoing in HealthData@EU. The European Health Data Space regulation signifies a major leap forward, enabling seamless flow of health data for the benefit of all of us.

Privacy Notice vs. Privacy Policy—what’s the difference and why it matters? We were sitting at the kitchen table with the camp brochure open, highlighters were everywhere. My daughter was old enough to decide, but she wanted all the details. She looked up at me again and added one more to her already long list of questions: ⁉️ Will I be sharing a room? ⁉️ What will we be doing there? ⁉️ Can I call home? ⁉️ How long is the camp? ⁉️ What if I change my mind? ⁉️ Who else is going? So I gave her the full picture—the schedule, the rules, contact details, and all the what-ifs. Not to convince her, but because it was her call to make. And honestly, I had asked those same questions when I called her school, just before I was comfortable enough to send her there. And that, is exactly what a privacy notice is for. That memory came rushing back during a recent conversation on the topic with other Privacy pros and Jamal Ahmed. When an organisation wants your personal data, it’s not just polite to explain, it’s a legal obligation. And more importantly, you should have a choice. A privacy notice helps you decide whether to go ahead or not. It tells you: ℹ️ What data they want ℹ️ Why they need it ℹ️ Who they’ll share it with ℹ️ How long they’ll keep it ℹ️ What your rights are It’s a public-facing document, meant for users, customers, or stakeholders. It gives you the power to say: “Yes, I’m okay with this” or “No thank you.” A privacy policy, on the other hand, is internal. It guides the organisation’s team on how to handle your data. It’s their rulebook, not something you review or agree to, but something they must follow. Under the GDPR, privacy notices must be: ✔️ Concise ✔️ Transparent ✔️ Intelligible ✔️ Easy to access ✔️ Written in plain language ✔️ Delivered free of charge 📌 If the organisation collects your data directly (like when you sign up), they must inform you of all the requisite details at that point. 📌 If they got your data indirectly (like from a partner), they must tell you within a month or at first contact (whichever comes first) and include some more details like where they got your data and what kind it is. So what’s the difference again? 💬 A privacy notice is like handing someone the camp brochure, with all the details, so they can make an informed decision. 📘 A privacy policy is the internal camp manual—used by staff to make sure things run safely and responsibly. When people get the full story, in clear language, and at the right time. They get to choose what’s best for them before they commit. That’s what real respect and real privacy actually looks like. #PrivacyMatters #GDPRCompliance #FintechPrivacy #DataGovernance #ComplianceEdge #ParentingAndPrivacy

ISO has released revised version of PIMS - ISO/IEC 27701:2025 (https://lnkd.in/dVBXbQ9E), & has also introduced ISO/IEC 27706:2025 PIMS 2019 v/s 2025: 🔶Certification Requirements 🔹Dependency: • 2019: Required prior ISO 27001 (ISMS) certification • 2025: Standalone standard: organizations can certify to PIMS independently 🔹Impact: • 2019: Restricted to entities with existing ISMS • 2025: Enables SMEs, Startups, FinTechs, Healthcare Providers, AI and Cloud companies to pursue PIMS certification without needing full ISMS ✅Wider accessibility accelerates privacy compliance across industries -- 🔶Alignment with Updated Standards 🔹Base Alignment: • 2019 - ISO 27001:2013 & 27002:2013 • 2025 - ISO 27001:2022 & 27002:2022; includes mappings to ISO 29100, 27018, 29151 🔹Focus: • 2019: Add-on privacy management on top of ISMS • 2025: Modernized controls integrating cybersecurity, cloud, & AI considerations ✅Reflects evolving technology landscape & modern privacy risks -- 🔶Management System Framework 🔹Structure: • 2019: Dependent on 27001 clause structure with privacy controls as an extension • 2025: Clauses 4–10 mirror 27001 but tailored for privacy governance & management 🔹Governance: • 2019: Implicit privacy responsibilities • 2025: Strengthens executive accountability & integrates privacy into overall enterprise risk management ✅Clearer leadership roles drive stronger privacy culture -- 🔶Controls & Annexes 🔹Control Structure: • 2019: Separate Annex A (PII Controllers) & B (PII Processors) • 2025: Unified Annex A: A.1: PII Controllers, A.2: PII Processors, A.3: Shared Controls 🔹Number of Controls: • 2019: Relied heavily on 27001 SOA; ~80–100 controls including non-privacy ones • 2025: 78 total privacy-focused controls: 31 for PII Controllers, 18 for PII Processors, 29 shared; & ~52 unrelated / non-privacy controls removed 🔹Guidance: • 2019: Minimal • 2025: New Annex B offers practical step-by-step implementation guidance for PII Controllers, PII Processors, & shared controls ✅Streamlined controls with actionable guidance improves adoption & focus -- 🔶Scope Expansion 🔹Data Coverage: • 2019: General PII • 2025: Includes biometric, health, IoT data; alignment with GDPR, CCPA/CPRA, LGPD, & emerging global data protection laws 🔹Data Transfer & Consent: • 2019: Basic reference to privacy requirements • 2025: Enhanced consent management, transparency in automated processing, & traceability of cross-border data transfers ✅ Built for modern data ecosystems & global privacy concerns -- 🔶Implementation and Compliance 🔹Ease of Adoption: • 2019: Dependent on ISMS maturity • 2025: Can be implemented standalone; more structured & understandable 🔹Accountability: • 2019: Standard audit/reporting requirements • 2025: Stricter reporting obligations, enhanced supplier & sub-contractor oversight, and stronger audit preparedness ✅ Reduced barriers to adoption, improved clarity, & increased accountability

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

🚩 The US government pushes for PQC adoption and extensive use of cryptography. On Jan. 16th, 2025, the Biden administration published the "Executive Order on Strengthening and Promoting Innovation in the Nation’s Cybersecurity" (EO 14144). The Trump administration revoked several Biden Executive Orders on the inauguration day, but this EO was not one of them. This EO shows near-future requirements by US agencies to their vendors. These requirements may permeate to the financial sector as requisites from US agencies to their providers or as features that will be more relevant in major technology products and offerings. It also shows interesting trends on actions that may need to be prioritized. The EO focuses on making cybersecurity controls effective to avoid organizations and the supply chain to comply minimally with no impact in improving security. It seeks accountability of software and cloud services providers.  👉 Highlights on cryptography There are several requirements promoting the use of cryptography and accelerating the transition to PQC: ✔ Use of public-key cryptography to implement phising-resistant authentication. ✔ Implement Internet routing protections to defend against malicious traffic diversions ✔ Implement cryptography-protected DNS, email, voice, videoconference and instant messaging. ✔ Implement PQC "as soon as practicable". ✔ Improve key management onprem and in the cloud. I appreciate the expanded focus on means to achieve data protection: 👍 Introducing or improving cryptography in various processes and protocols. 👍 Protecting Internet traffic routing, as it is a first step for HNDL attacks. More details: 📌 The order highlights “the People’s Republic of China presenting the most active and persistent cyber threat” to the US. 📌 Use of Route Origin Authorizations and performing Route Origin Validation filtering. 📌 NIST to publish updated guidance on BGP security methods, route leak mitigation and source address validation. 📌 Encrypted DNS must be deployed wherever supported. 📌 Email messages must be encrypted in transport and, where practical, use end-to-end encryption. 📌 Expand the use of authenticated transport-layer encryption between email servers and with clients. 📌 Voice, VCand IM must enable transport encryption and use end-to-end encryption by default. 📌 Implement PQC key establishment or hybrid key establishment including a PQC algorithm as soon as practicable upon support from the vendors. 📌 Support TLSv1.3 ASAP but no later than 2029. 📌 Cryptographic keys with extended lifecycles should be protected with HSMs, TEEs, etc. Executive order: https://lnkd.in/d-ifZtrf National Institute of Standards and Technology (NIST) responsibilities: https://lnkd.in/dnhUbrfH #pqc #cryptography #cybersecurity #policy