Common Issues in Privacy Engineering Solutions

The Website Consent Problem: Too Many Tools, Too Little Harmony Websites rely on various third-party tools like analytics platforms, ad managers, and tag managers. While these tools are essential for functionality, each has unique privacy settings. The real challenge is ensuring they work together to honor user consent. When integration fails, consent flows break, leading to compliance risks and loss of trust. Websites often use over 20 different types of tools. Key categories of website tools: 1. Analytics tools Google Analytics and Adobe Analytics track user behavior and performance. They rely on settings like Google Consent Mode to operate compliantly. Without proper integration, they may collect data before consent. 2. Ad management platforms Prebid.js and Google Ad Manager manage ad delivery. They need frameworks like IAB TCF strings to serve personalized ads only with user consent. Misconfigurations can lead to tracking and legal risks. 3. Tag management systems (TMS) Google Tag Manager and Tealium control when other tools are deployed. The CMP (Consent Management Platform) must load first to capture consent preferences. Without proper setup, tools may fire prematurely. 4. Heatmaps and session recording tools Hotjar and FullStory track user interactions to improve experience. These tools collect sensitive data and should operate only with explicit consent. Poor configurations can result in privacy issues. Why honoring consent is a challenge? - Fragmented ecosystem Most tools operate in silos, making it hard to create a unified consent flow. Without integration, tools don’t respect shared consent signals. - Regulatory complexity Privacy laws vary across regions, requiring different approaches for compliance (e.g., opt-in vs. opt-out). Configuring tools to meet global regulations adds complexity. - Lack of real-time monitoring Consent flows change as tools are updated or replaced. Without regular monitoring, settings can become outdated, leading to unauthorized data collection. - Misaligned priorities Revenue goals often take precedence over compliance. This results in shortcuts like firing tracking scripts before consent is obtained, risking penalties and user trust. What should Privacy Teams do? 1. Audit your website List all third-party tools and document their data flows. 2. Understand privacy settings Review each tool’s privacy settings and integration with the CMP. 3. Fix tag management systems Ensure the CMP loads first to capture user consent before other tags fire. 4. Verify CMP integration Confirm the CMP communicates consent signals to all tools for consistency. 5. Automate, automate, automate Manual consent flow monitoring is time-consuming and prone to errors. Work with tech teams to automate consent checks or use vendors specializing in consent monitoring automation. This will help in catching issues early on. #Privacy pros, How are you auditing your website’s tools and #consent flows?

Engineers love to build for scale, but ignore privacy until legal comes knocking. This costs MILLIONS. When engineers design data systems, privacy is often an afterthought. I don’t blame them. We aren’t taught privacy in engineering schools. We learn about performance, scalability, and reliability - but rarely about handling consent, compliance, or privacy by design. This creates a fundamental problem: We build data systems as horizontal solutions meant to store and process any data without considering the special requirements of CUSTOMER data. As a result, privacy becomes a bolt-on feature. This approach simply DOES NOT WORK for customer data. With customer data, privacy needs to be a first-class citizen in your architecture. You need to: 1. Track consent alongside every piece of customer data throughout the entire lifecycle 2. Build identity resolution with privacy in mind 3. Design data retention policies from day one 4. Implement access controls at a granular level When privacy is an afterthought, you'll always have leaks. And in today's regulatory environment, those leaks can cost millions. The solution isn't complicated, but it requires a shift in mindset. Start by recognizing that customer data isn't like other data. It has unique requirements that must be addressed in your core architecture. Then, design your systems with privacy, consent, and compliance as fundamental requirements, not nice-to-haves.

Your brand is likely misusing first-party data and violating customer trust. It's not your intention, but it's probably happening. Here are some common issues I've seen: 1.) Scattering customer data in too many locations - email vendors/CRMs - data warehouses - spreadsheets (eek) 2.) Ignoring permission ...or defaulting to "allow everything" 3.) Not rolling off/expiring data no longer necessary - long-gone churned customers - legacy systems - inactive contact lists 4.) Lack of transparency in how the customer data will be used ...vague or complex privacy/consent policies 5.) Giving too many employees access to sensitive/data ...not everyone needs access to PII/PHI info 6.) Low-security storage - employees accessing cust data on personal devices - lack of roles/permissions - lack of logging 7.) Sharing passwords - bypassing MFA/2FA w/ shared logins - passwords in shared Google Docs - sent via email (ugh) Get caught, and you could face: - significant fines (we're talking millions) - a damaged reputation - loss of customer trust But you can fix this. Here's what to do: - Ask customers what data they're okay sharing - Keep customer data in one secure place (CDP/warehouse) - Only collect what you need (data minimization) - Set clear rules for handling data (who/what) - Offer something in return for data (value trade) - Only let employees access what they need for their job - Use strong protection for all sensitive info - Give each person their own login Your customers will trust you more. Your legal team will be happy. ...and bonus, your marketing will work better. What other data mistakes have you seen? Drop a comment. #dataprivacy #security #consent #dataminimization

Most companies treat privacy as policy. The best treat it as code. Every week, high-performing legal, product, and engineering teams sit down to align on responsible data use. And every week, they run into the same problem: No shared language. It’s not a communication failure. It’s a translation failure. A privacy policy that seems clear in a spec doc becomes a tangle of implementation questions: → How are preferences modeled across systems? → What’s a valid state change when consent is updated? → What’s the source of truth when systems conflict? → How do we avoid race conditions in enforcement? Policy teams speak in rights, obligations, and business logic. Engineers work in schemas, state machines, and systems design. Product tries to mediate, often without reliable infrastructure beneath them. The result? Requirements that feel legally sound but defy implementation. Code that looks complete but misses the mark in spirit or scope. What’s missing isn’t collaboration. It’s a common operational foundation, a shared semantic layer between policy and execution. This is why privacy must be treated as a systems problem. Not solved in docs. Enforced in code. It’s the core idea behind infrastructure like Fides, where legal definitions, business policies, and data models all converge into one executable framework. So obligations aren’t just written. They’re enforced - reliably, automatically, and at scale. The companies that get this right won’t be the ones with the most policy meetings. They’ll be the ones who operationalize trust as part of their stack. Because when policy is embedded directly in infrastructure: → Legal can write once and enforce everywhere. → Engineers ship faster with clarity and confidence. → Product stops negotiating tradeoffs between trust and velocity. That’s not just better governance - it’s a better growth model. Instead of being boxed in by complexity, teams unlock the ability to safely innovate with sensitive data; across AI, analytics, personalization, and more. This is how enterprises stop playing defense with privacy and start building forward. 👇 Drop a note below or DM. I’d love to hear your perspective.

As a legal counsel navigating AI, data privacy, and cybersecurity frameworks, I’ve come to a critical insight: the lack of harmonization across these regimes isn’t just a policy gap, it’s a strategic, legal, and operational challenge. While each area has matured independently, what’s missing is the connective tissue - a common operational layer that enables alignment across sectors, teams, and borders. 🔍 Key Challenges: 1. Regulatory dissonance – AI laws (e.g., EU AI Act), privacy rules (e.g., GDPR, HIPAA), and cybersecurity standards (e.g., NIST, ISO 27001) often conflict or overlap, creating uncertainty for global organizations. 2. Reactive compliance– Legal and risk teams are pulled into product cycles *after* key architectural decisions are made. 3. Unmapped risk ownership – Organizations struggle to assign clear accountability between engineering, legal, and product on AI-related harms or breaches. 4. Data localization vs. model scalability– Privacy laws requiring data residency clash with AI model training needs across borders. 5. Ethical gaps in automation – AI systems make decisions that implicate rights (e.g., profiling, surveillance), but legal frameworks don’t clearly define thresholds for legality or ethical use. 💡 Legal Counsel’s Strategic Solutions: 1. Design “bridge governance” – Implement internal controls that sit at the intersection of AI, privacy, and cybersecurity like automated DPIAs that also flag algorithmic risks. 2. Create pre-build legal design templates– Develop legal “playbooks” embedded into product dev cycles to address compliance by design, not by review. 3. Cross-train legal & tech teams– Legal teams need to understand basic ML models; tech teams should learn key regulatory principles (like lawful basis or data minimization). 4. Advocate for AI-specific incident response plans – Traditional data breach protocols aren’t enough; we need AI failure playbooks that address explainability and traceability. 5. Push for unified risk taxonomies – Use consistent definitions of risk (legal, ethical, operational) across teams to align decisions and reporting. The goal is no longer just legal compliance, it's legal foresight. Those who operationalize law as infrastructure, not afterthought, will lead responsibly in the AI age. Prodago is tool I recommend for all the above. #AI #Privacy #Cybersecurity #LegalStrategy #InHouseCounsel #ResponsibleAI #ComplianceByDesign #AIRegulation #TechGovernance #privacylaw #legalcounsel #aigovernance #aisystems #aiact #gdpr

Why Privacy Engineering Is More Critical Than Ever in the Age of AI. Reason 2️⃣ - New Threats, Same Privacy Basics AI brought us a ton of new terminology: Training Data, Prompts, AI Models, … And with these terms also new threats: Prompt Injection, Data Poisoning, Bias, etc. So, does that mean that we also need a completely new way to look for threats? Ha! Nope. Sure, we’ll need to understand the basics of AI to analyze it properly, but more essentially, we need to properly grasp security and privacy engineering fundamentals. Because, let’s face it, all these fancy new AI-related threats are still instantiations of basic STRIDE and LINDDUN security and privacy threat types. 🔄 Prompt Injection (LLM01:2025) (User prompts alter the LLM’s behavior or output in unintended ways.) - An instance of a Tampering threat (STRIDE category - intentional modification of (data in) the system) 🔄 Sensitive Information Disclosure (LLM02:2025) (Sensitive data, such as personal data can be leaked) - An instance of Information Disclosure (STRIDE category - obtaining data without proper authorization) and related to Data Disclosure (LINDDUN category - excessive use of personal data) 🔄 Membership Inference (Attackers can detect whether specific data was used to train the model) - An instance of a Detecting threat (LINDDUN category - deducing information based on existence of data or actions, without having access to the actual data). 🔄 Prompt Fingerprinting (The uniqueness of input-output pairs may serve as implicit proof of activity.) - An instance of a Linking threat (LINDDUN category - Combining data items to learn more about an individual or set of individuals). There’s a long list of privacy (and security) AI threats to follow. And there are likely emerging new AI-specific threats every day. So, what to do now? Trying to keep up-to-date about the latest and greatest AI threats that were just discovered? Sure, you can. And it is a good idea to stay aware of new evolutions in the threat landscape. But, what about, you know, start by understanding the basics of privacy engineering. So, when you’re analyzing a new feature or system, you’re not limited by that list of latest AI-specific threats, but you are empowered by all the necessary knowledge to actually understand all the privacy consequences of each flow, action, and activity. By having a broad generic privacy engineering expertise, you can analyze each product, feature, AI system, blockchain application, or whatever the next hype on the horizon will be. --------------------------------------------- Curious about the other reasons? I'll be posting them in the following days. Or you can read about all of them in the full write-up (see comments for link)

You Might Be Violating Your Company’s Privacy Policy Without Even Knowing It Every time you upload a confidential PDF, report, or internal document to an AI tool, you could be unintentionally leaking sensitive company data—violating privacy policies, breaching contracts, or exposing intellectual property to third-party systems. Let that sink in: a single upload to the wrong AI platform could be a compliance violation with serious legal and professional consequences. ⸻ Now, let’s be clear: AI is a powerful ally. Tools like ChatGPT, Claude.ai, Google NotebookLM, and ChatPDF have transformed the way we study, analyze, and understand complex documents. Whether it’s summarizing lengthy reports, extracting insights from technical files, or making learning more efficient, AI is revolutionizing productivity in every sector—including construction, engineering, law, and finance. But that power comes with responsibility. ⸻ AI tools are not all safe havens. Platforms such as NotebookLM, ChatPDF, AskYourPDF, SciSummary, PDFGPT, DocAnalyzer, and others often store, index, or process your files on external servers. Many of these tools do not guarantee complete data privacy. What you upload may be analyzed—or retained. Never upload files such as: • Client contracts or project tenders • Financial statements, audits, or payroll data • Engineering models, shop drawings, or specifications • Legal documents or internal dispute records • Business strategies, confidential memos, or internal reports What’s safer to upload: • Public codes and standards (e.g., ISO, ASTM) • General technical training documents • Published articles and case studies • Fully redacted or anonymized content • Public domain or marketing material Before uploading, ask yourself: • Am I authorized to share this externally? • Does this AI tool retain or train on uploaded content? • Could this expose my organization or clients to risk? ⸻ AI enhances efficiency, but never at the cost of data security. Use it smartly. Use it responsibly. ⸻ Have you addressed this in your team or organization? Let’s open the discussion in the comments. #AIrisks #DataPrivacy #DocumentSecurity #EngineeringEthics #Compliance #AIAwareness #CyberSecurity #ConstructionTech #InformationGovernance #AItools #ResponsibleAI #PrivacyMatters #DigitalSecurity #TechCompliance #DataProtection #SmartEngineering #AIEthics #WorkplaceSecurity #ConfidentialData #InformationManagement

Solving the Linkage Problem is the missing piece in many Privacy Enhancing Technologies (PETs). PETs are evolving, but many still miss addressing the biggest challenge: linking data across partners without exposing identities. PETs like federated learning, differential privacy, fully homomorphic encryption, and synthetic data have strengths, but they focus on already integrated data and don’t solve the linkage problem. And data clean rooms, while being touted as a privacy solution, still require centralizing data with a third party. True privacy-first data collaboration requires new approaches—ones that don’t sacrifice accuracy for security. The future belongs to solutions that can link datasets without exposing sensitive information.

In many companies, the Privacy Office still operates in a silo. Often, with a legal compliance focus, the Privacy Office can be detached from the actual processing operations that occur far away in the business. Far away in mindset and engagement. A symptom of this scenario is lack of buy-in and support from the business for the privacy program. The Privacy Office's legal focus is extremely important. But if privacy and data protection practices are generic or theoretical, too large a gap may exist with where the potentially high-risk processing is carried out. Boxes may be ticked, but privacy threats and privacy violations go unnoticed. As with most organisational challenges, the solution lies in a combination of people, process and technology. People in your team who are more diverse in terms of competences, recognising the non-legal competences that are needed in the team. Process in terms of shifting the privacy teams role from gatekeeper to more active participant in early stages of new projects. And finally, technologies that support these changes while understanding that resources are limited. At TrustWorks, we work on solving these problems everyday for Privacy teams. Our most recent innovation, TrustWorks #Engage (link in comment), automates and operationalises engagement activities for privacy teams. These challenges are hard but the rewards for those who build privacy programs that matter are great.