Testing Ecommerce Site Usability

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

Core Web Vitals should matter to not just SEO experts, but anyone interested in scaling D2C Often your product is great, and your ads are getting people to your site, but good chunk of them bounce before even seeing the second image Or worse, your D2C site ranks below third-rate aggregators on Google despite having a better brand, better product, and better reviews on keywords you should own One big reason could be that your site experience sucks. And that’s exactly what Core Web Vitals is trying to measure. These aren’t vanity metrics. They’re Google’s way of telling you that your user experience is frustrating. And we won’t reward it with visibility Here’s what Core Web Vitals actually mean (without jargon): •LCP (Largest Contentful Paint) = How long your main visual/image takes to load. If it’s >2.5s, it’s a problem •CLS (Cumulative Layout Shift) = Does the screen jump around while loading? If buttons shift while the user tries to click, it’s hurting conversions •INP (Interaction to Next Paint) = How fast your site reacts to taps/clicks. If there’s a delay when they hit “Add to Cart”-bad news. Think of it this way: •LCP = First impression •CLS = Does the page feel stable? •INP = Is it snappy? Why should founders/CMOs care? Because these metrics directly affect 3 things: 1. Organic traffic (SEO): Google demotes slow and clunky sites. Doesn’t matter how good your content or backlinks are 2. Conversion rate: People bounce when images load late, or buttons move as they click 3. Ad ROAS: Your performance marketing team is paying to drive traffic to a broken experience. You lose money before the user even evaluates the product How to check your Core Web Vitals: Free Tools: •PageSpeed Insights •Lighthouse Ideal Benchmarks in my opinion: •LCP < 2.5s •CLS < 0.1 •INP < 200ms If you are doing everything else right- good products, good marketing, good creatives etc, don’t let slow LCP and messy CLS undo your good work

Run a Core Web Vitals check — site speed = silent revenue leakage Every extra second your site takes to load is costing you sales. Customers don’t wait. They click back, they bounce, they buy elsewhere. Google’s Core Web Vitals give you a simple way to measure site speed and usability. The three key metrics: Largest Contentful Paint (LCP) — how fast the main content loads. First Input Delay (FID) — how quickly the page responds to user actions. Cumulative Layout Shift (CLS) — how stable the page layout is as it loads. Why this matters for eCommerce: Faster sites convert more. Even a 0.5 second improvement can lift revenue. Google uses Core Web Vitals as a ranking signal, so speed directly impacts SEO. Poor mobile performance means wasted ad spend, as users drop off before they even see the offer. A quick check you can run today: Plug your site into Google PageSpeed Insights. Review your Core Web Vitals scores. Prioritise fixes for your top revenue-driving pages. Site speed is silent revenue leakage. Fix it, and you unlock growth. Question: Have you checked your Core Web Vitals in the last 3 months? #SEO #ecommerce #digitalmarketing

Today, National Institute of Standards and Technology (NIST) published its finalized Guidelines for Evaluating ‘Differential Privacy’ Guarantees to De-Identify Data (NIST Special Publication 800-226), a very important publication in the field of privacy-preserving machine learning (PPML). See: https://lnkd.in/gkiv-eCQ The Guidelines aim to assist organizations in making the most of differential privacy, a technology that has been increasingly utilized to protect individual privacy while still allowing for valuable insights to be drawn from large datasets. They cover: I. Introduction to Differential Privacy (DP): - De-Identification and Re-Identification: Discusses how DP helps prevent the identification of individuals from aggregated data sets. - Unique Elements of DP: Explains what sets DP apart from other privacy-enhancing technologies. - Differential Privacy in the U.S. Federal Regulatory Landscape: Reviews how DP interacts with existing U.S. data protection laws. II. Core Concepts of Differential Privacy: - Differential Privacy Guarantee: Describes the foundational promise of DP, which is to provide a quantifiable level of privacy by adding statistical noise to data. - Mathematics and Properties of Differential Privacy: Outlines the mathematical underpinnings and key properties that ensure privacy. - Privacy Parameter ε (Epsilon): Explains the role of the privacy parameter in controlling the level of privacy versus data usability. - Variants and Units of Privacy: Discusses different forms of DP and how privacy is measured and applied to data units. III. Implementation and Practical Considerations: - Differentially Private Algorithms: Covers basic mechanisms like noise addition and their common elements used in creating differentially private data queries. - Utility and Accuracy: Discusses the trade-off between maintaining data usefulness and ensuring privacy. - Bias: Addresses potential biases that can arise in differentially private data processing. - Types of Data Queries: Details how different types of data queries (counting, summation, average, min/max) are handled under DP. IV. Advanced Topics and Deployment: - Machine Learning and Synthetic Data: Explores how DP is applied in ML and the generation of synthetic data. - Unstructured Data: Discusses challenges and strategies for applying DP to unstructured data. - Deploying Differential Privacy: Provides guidance on different models of trust and query handling, as well as potential implementation challenges. - Data Security and Access Control: Offers strategies for securing data and controlling access when implementing DP. V. Auditing and Empirical Measures: - Evaluating Differential Privacy: Details how organizations can audit and measure the effectiveness and real-world impact of DP implementations. Authors: Joseph Near David Darais Naomi Lefkovitz Gary Howarth, PhD

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.

Can AI truly protect our information? Data privacy is a growing concern in today’s digital world, and AI is being hailed as a solution—but can it really safeguard our personal data? Let’s break it down: Here are 5 crucial things to consider: 1️⃣ Automated Compliance Monitoring ↳ AI can track compliance with regulations like GDPR and CCPA. ↳ By constantly scanning for potential violations, AI helps organizations stay on the right side of the law, reducing the risk of costly penalties. 2️⃣ Data Minimization Techniques ↳ AI ensures only the necessary data is collected. ↳ By analyzing data relevance, AI limits exposure to sensitive information, aligning with data protection laws and enhancing privacy. 3️⃣ Enhanced Transparency and Explainability ↳ AI can make data processing more transparent. ↳ Clear explanations of how your data is being used fosters trust and helps people understand their rights, which is key for regulatory compliance. 4️⃣ Human Oversight Mechanisms ↳ AI can’t operate without human checks. ↳ Regulatory frameworks emphasize human oversight to ensure automated decisions respect individuals' rights and maintain ethical standards. 5️⃣ Regular Audits and Assessments ↳ AI systems need regular audits to stay compliant. ↳ Continuous assessments identify vulnerabilities and ensure your AI practices evolve with changing laws, keeping personal data secure. AI is a powerful tool in the fight for data privacy, but it’s only as effective as the governance behind it. Implementing AI with strong oversight, transparency, and compliance measures will be key to protecting personal data in the digital age. What’s your take on AI and data privacy? Let’s discuss in the comments!

 Privacy software isn’t one size fits all.   That's why companies need to evaluate every tool with care.   Because even tools that shine in sales demos can fall short when connected to company systems and put into practice. 📣 Friendly reminder from this year's enforcement actions: customize the tool for your business and do not rely on the vendor default settings.   So, it's super important to ask vendors detailed questions about how their software solutions will work in your company's particular environment.   Will it meet specific technical and compliance requirements?   Can the software connect to data sources without creating compliance blind spots?   Do the platforms adapt as regulations change or as business needs evolve?   These questions matter because running a comprehensive privacy program requires different software solutions for various compliance needs.   Depending on the company, that could mean evaluating all, some, or a mix of:   ✔ Data inventory tools that provide visibility into what personal data companies have and where it lives ✔ Privacy rights platforms that help companies respond to individual rights requests ✔ Privacy impact assessment software that evaluates risks before they become problems ✔ Consent management platforms that handle the collection and tracking of user permissions   Yet sometimes the pressure to implement quickly can push thorough reviews aside.   Privacy software is infrastructure.   Choose tools based on how they work in your company's environment, not how fast they can be deployed.   Asking vendors the right questions upfront helps companies avoid headaches and select privacy software that truly meets their needs. ⏱️ Plan accordingly. Many companies rush hoping for a decision in a few weeks. Thoughtful vendor selection can take 3-6 months. Start planning now.   Our latest blog covers the specific questions to ask for each software type: https://lnkd.in/eUnd8xvA

You’re the new Privacy Analyst at a U.S. retail company. Your manager just asked you to ensure the company is compliant with the California Consumer Privacy Act (CCPA), but you quickly realize there’s no data inventory or record of what personal data is being collected, where it’s stored, or who it’s shared with. How would you even begin? First, you’d start by building a data inventory — that means identifying what personal data the company collects (names, emails, browsing history, etc.), how it’s collected (forms, cookies, third-party platforms), and where it lives (CRM, marketing tools, cloud storage, etc.). You’d likely send out a questionnaire or meet with key teams (marketing, IT, sales) to gather this info. Then, you’d map the data flows — what systems touch this data, who has access, and whether it gets sent to vendors or service providers. This is essential for understanding risk and creating compliant privacy notices. Finally, you’d document it all and check it against the CCPA requirements — can users request access to their data? Can they delete it? Is there a way to opt out of data selling? This is GRC work in action.. breaking down compliance into trackable steps and helping the business stay accountable.

Let's make it clear: We need more frameworks for evaluating data protection risks in AI systems. As I delve into this topic, more and more new papers and risk assessment approaches appear. One of them is described in the paper titled "Rethinking Data Protection in the (Generative) Artificial Intelligence Era." 👉 My key takeaways: 1️⃣ Begin by identifying the data that should be protected in AI systems. Authors recommend focusing on the following: •  Training Datasets •  Trained Models •  Deployment-integrated Data (e.g., protect your internal system prompts and external knowledge bases like RAG). ❗ I loved this differentiation and risk assessment, as if, for example, an adversary discovers your system prompts, they might try to exploit them. Also, protecting sensitive RAG data is essential. •  User prompts (e.g., besides prompts protection, add transparency and let users know if prompts will be logged or used for training). •  AI-generated Content (e.g., ensure traceability to understand its provenance if used for training, etc.). 2️⃣ Authors also introduce an interesting taxonomy of data protection areas to focus on when dealing with generative AI: •  Level 1: Data Non-usability. Ensures that specified data cannot contribute to model learning or predicting in any way by using strategies that block any unauthorized party from using or even accessing protected data (e.g., encryption, access controls, unlearnable examples, non-transferable learning, etc.) •  Level 2: Data Privacy-preservation. Here, the focus is on how the training can be performed with enhanced privacy techniques (PETs): K-anonymity and L-diversity schemes, differential privacy, homomorphic encryption, federated learning, and split learning. •  Level 3: Data Traceability. This is about the ability to track the origin, history, and influence of data as it is used in AI applications during training and inference. This capability allows stakeholders to audit and verify data usage. This can be categorised into intrusive (e.g., digital watermarking with signatures to datasets, model parameters, or prompts) and non-intrusive methods (e.g., membership inference, model fingerprinting, cryptographic hashing, etc.). •  Level 4: Data Deletability. This is about the capacity to completely remove a specific piece of data and its influence from a trained model (authors recommend exploring unlearning techniques that specifically focus on erasing the influence of the data in the model, rather than the content or model itself). ------------------------------------------------------------------------ 👋 I'm Vadym, an expert in integrating privacy requirements into AI-driven data processing operations. 🔔 Follow me to stay ahead of the latest trends and to receive actionable guidance on the intersection of AI and privacy. ✍ Expect content that is solely authored by me, reflecting my reading and experiences. #AI #privacy #GDPR

🚀 Driving Privacy Excellence: Empowering You with DPIA Guidance 🚀 Data Privacy Impact Assessments (DPIAs) are not just regulatory checkboxes—they are powerful tools to embed privacy at the heart of innovation and trust. As privacy professionals, we have the unique opportunity to guide organizations in understanding risks, protecting individuals, and building systems that respect privacy by design. 🌍✨ To support your journey, I’m excited to share a comprehensive DPIA Guidance Document—crafted to simplify complexities, highlight best practices, and help you navigate the nuances of assessing privacy risks. Whether you're leading a DPIA for a groundbreaking AI system, a new marketing campaign, or a software overhaul, this resource is designed to: ✅ Clarify the process: Step-by-step guidance to ensure you're aligned with GDPR and other global standards. ✅ Enhance collaboration: Tips to engage stakeholders across your organization for meaningful DPIAs. ✅ Deliver actionable insights: Tools to identify, mitigate, and communicate risks effectively. Why DPIAs Matter They build trust with customers and stakeholders. They identify risks early, avoiding costly and reactive changes later. They help us create ethical and sustainable innovations. Let’s use this moment to recommit to our mission: protecting individuals’ rights and shaping a world where privacy is a foundation, not an afterthought. 🔗 Download the DPIA Guidance Document here with this post. In case you need a sample DPIA template, hit a comment for it. 👥 Let’s spark a conversation! What’s your biggest challenge or tip for conducting effective DPIAs? Share in the comments—let’s inspire and learn from each other. Together, we can champion privacy excellence! 💡💪 #PrivacyProfessionals #DPIA #DataProtection #PrivacyByDesign #TrustAndInnovation #GDPR #PDPL #DataPrivacy #privacychampions #privacypro