Managing Privacy and Data Validation in Automated Workflows

If you didn't see the news, California just finalized its California Privacy Rights Act (CPRA) regulations on ADMT (automated-decision-making tools...think routing, scoring, profiling). Europe already has the AI Act. Singapore, Brazil, and Canada are next in line with similar AI-oversight bills. The takeaway is simple: If an algorithm is going to nudge a customer or rate an employee, regulators now want to know how, why, and with what data. Oh, and they also now expect an auditable paper trail. If you haven't started designing for these regulations, here are a few things to start doing. Like, today: First, whether you like it or not, dual jurisdiction is now the new normal, and U.S. rules no longer lag behind Europe. An “EU Compliance” badge won't cut it when California or the FTC asks for your ADMT impact assessment. Design for the regulatory extremes, and partner with your Risk and Legal teams to see if that takes care of the middle part of the regulatory curve. But make sure you’re ticking all the boxes. Second, explainability should now be a service level to be defined and to meet. This means that risk assessments, opt-outs, human-override flows, and data-provenance logs have to be part of every release. Treat them just like uptime and latency. Third, employee experience is officially in scope. Tools that allocate work shifts or score performance need the same transparency you’d give to customer-facing models. This is a really big deal. It will improve employee trust but creates extra work that needs to be planned for, prioritized, and resourced. Last but not least, and my "always-on" advice: start small. Just map one high-impact workflow (e.g., complaint escalation, agent performance dashboards, etc.). Document the data used, the decision logic, and the path to human appeal. And if you can’t explain it to a regulator in under 5 PPT slides, refactor before you scale it. It's way better to audit yourself now than to have a regulator do it later. They're not bad people, but you also don't want them in your cubicles either. #customerexperience #employeeexperience #privacy #ai #automation #regtech

If you're running automations that handle sensitive data, here's how I'm implementing human-in-the-loop workflows to add a safety layer.   Just integrated Velatir into my n8n workflows, and it works quite differently from n8n's built-in HITL features.   Here's what happening:   I've been building automated workflows for clients, and when you're dealing with sensitive operations - payment processing, customer communications, data modifications - you may need that human verification step.   That's where Velatir comes in. It's a human-in-the-loop platform that adds approval checkpoints to any automation.   Example 1: Payment Processing Automation • Refund request comes in • If above a certain threshold, Velatir pauses the workflow • I get instant notification via email/Slack/Teams • I approve or reject with one click • Workflow continues or stops based on my decision   Example 2: Automated Email Responses • Email arrives from customer • AI drafts response • Velatir shows me the draft before sending • I verify it's appropriate and accurate • Email sends only after approval   What makes this different from basic approval systems:   → Customizable rules, timeouts, and escalation paths → One integration point, no need to duplicate HITL logic across workflows → Full logging and audit trails (exportable, non-proprietary) → Compliance-ready workflows out of the box → Support for external frameworks if you want to standardize HITL beyond n8n   The setup took about 5 minutes - sign up, get API key, add to your n8n workflow.   One interface, one source of truth, no matter where your workflows live.   Question for my network: What's the riskiest automation you're running without human oversight?

The EDPB recently published a report on AI Privacy Risks and Mitigations in LLMs.   This is one of the most practical and detailed resources I've seen from the EDPB, with extensive guidance for developers and deployers. The report walks through privacy risks associated with LLMs across the AI lifecycle, from data collection and training to deployment and retirement, and offers practical tips for identifying, measuring, and mitigating risks.   Here's a quick summary of some of the key mitigations mentioned in the report:   For providers: • Fine-tune LLMs on curated, high-quality datasets and limit the scope of model outputs to relevant and up-to-date information. • Use robust anonymisation techniques and automated tools to detect and remove personal data from training data. • Apply input filters and user warnings during deployment to discourage users from entering personal data, as well as automated detection methods to flag or anonymise sensitive input data before it is processed. • Clearly inform users about how their data will be processed through privacy policies, instructions, warning or disclaimers in the user interface. • Encrypt user inputs and outputs during transmission and storage to protect data from unauthorized access. • Protect against prompt injection and jailbreaking by validating inputs, monitoring LLMs for abnormal input behaviour, and limiting the amount of text a user can input. • Apply content filtering and human review processes to flag sensitive or inappropriate outputs. • Limit data logging and provide configurable options to deployers regarding log retention. • Offer easy-to-use opt-in/opt-out options for users whose feedback data might be used for retraining.   For deployers: • Enforce strong authentication to restrict access to the input interface and protect session data. • Mitigate adversarial attacks by adding a layer for input sanitization and filtering, monitoring and logging user queries to detect unusual patterns. • Work with providers to ensure they do not retain or misuse sensitive input data. • Guide users to avoid sharing unnecessary personal data through clear instructions, training and warnings. • Educate employees and end users on proper usage, including the appropriate use of outputs and phishing techniques that could trick individuals into revealing sensitive information. • Ensure employees and end users avoid overreliance on LLMs for critical or high-stakes decisions without verification, and ensure outputs are reviewed by humans before implementation or dissemination. • Securely store outputs and restrict access to authorised personnel and systems.   This is a rare example where the EDPB strikes a good balance between practical safeguards and legal expectations. Link to the report included in the comments.   #AIprivacy #LLMs #dataprotection #AIgovernance #EDPB #privacybydesign #GDPR

✳ Integrating AI, Privacy, and Information Security Governance ✳ Your approach to implementation should: 1. Define Your Strategic Context Begin by mapping out the internal and external factors impacting AI ethics, security, and privacy. Identify key regulations, stakeholder concerns, and organizational risks (ISO42001, Clause 4; ISO27001, Clause 4; ISO27701, Clause 5.2.1). Your goal should be to create unified objectives that address AI’s ethical impacts while maintaining data protection and privacy. 2. Establish a Multi-Faceted Policy Structure Policies need to reflect ethical AI use, secure data handling, and privacy safeguards. Ensure that policies clarify responsibilities for AI ethics, data security, and privacy management (ISO42001, Clause 5.2; ISO27001, Clause 5.2; ISO27701, Clause 5.3.2). Your top management must lead this effort, setting a clear tone that prioritizes both compliance and integrity across all systems (ISO42001, Clause 5.1; ISO27001, Clause 5.1; ISO27701, Clause 5.3.1). 3. Create an Integrated Risk Assessment Process Risk assessments should cover AI-specific threats (e.g., bias), security vulnerabilities (e.g., breaches), and privacy risks (e.g., PII exposure) simultaneously (ISO42001, Clause 6.1.2; ISO27001, Clause 6.1; ISO27701, Clause 5.4.1.2). By addressing these risks together, you can ensure a more comprehensive risk management plan that aligns with organizational priorities. 4. Develop Unified Controls and Documentation Documentation and controls must cover AI lifecycle management, data security, and privacy protection. Procedures must address ethical concerns and compliance requirements (ISO42001, Clause 7.5; ISO27001, Clause 7.5; ISO27701, Clause 5.5.5). Ensure that controls overlap, such as limiting access to AI systems to authorized users only, ensuring both security and ethical transparency (ISO27001, Annex A.9; ISO42001, Clause 8.1; ISO27701, Clause 5.6.3). 5. Coordinate Integrated Audits and Reviews Plan audits that evaluate compliance with AI ethics, data protection, and privacy principles together (ISO42001, Clause 9.2; ISO27001, Clause 9.2; ISO27701, Clause 5.7.2). During management reviews, analyze the performance of all integrated systems and identify improvements (ISO42001, Clause 9.3; ISO27001, Clause 9.3; ISO27701, Clause 5.7.3). 6. Leverage Technology to Support Integration Use GRC tools to manage risks across AI, information security, and privacy. Integrate AI for anomaly detection, breach prevention, and privacy safeguards (ISO42001, Clause 8.1; ISO27001, Annex A.14; ISO27701, Clause 5.6). 7. Foster an Organizational Culture of Ethics, Security, and Privacy Training programs must address ethical AI use, secure data handling, and privacy rights simultaneously (ISO42001, Clause 7.3; ISO27001, Clause 7.2; ISO27701, Clause 5.5.3). Encourage a mindset where employees actively integrate ethics, security, and privacy into their roles (ISO27701, Clause 5.5.4).

Too many enterprise programs still treat privacy as a policy checkbox. But privacy - done right - isn't simply about compliance. It’s about enabling confident, ethical, revenue-generating use of data. And that requires infrastructure. Most programs fail before they begin because they’re built on the wrong foundations: • Checklists, not systems. • Manual processes, not orchestration. • Role-based controls, not purpose-based permissions. The reality? If your data infrastructure can’t answer “What do I have, what can I do with it, and who’s allowed to do it?” - you’re not ready for AI. At Ethyca, we’ve spent years building the foundational control plane enterprises need to operationalize trust in AI workflows. That means: A regulatory-aware data catalog Because an “inventory” that just maps tables isn’t enough. You need context: “This field contains sensitive data regulated under GDPR Article 9,” not “email address, probably.” Automated orchestration Because when users exercise rights or data flows need to be redacted, human-in-the-loop processes implode. You need scalable, precise execution across environments - from cloud warehouses to SaaS APIs. Purpose-based access control Because role-based permissions are too blunt for the era of automated inference. What matters is: Is this dataset allowed to be used for this purpose, in this system, right now? This is what powers Fides - and it’s why we’re not just solving for privacy. We’re enabling trusted data use for growth. Without a control layer: ➡️ Your catalog is just a spreadsheet. ➡️ Your orchestration is incomplete. ➡️ Your access controls are theater. The best teams aren’t building checkbox compliance. They’re engineering for scale. Because privacy isn’t a legal problem - it’s a distributed systems engineering problem. And systems need infrastructure. We’re building that infrastructure. Is your org engineering for trusted data use - or stuck in checklist mode? Let’s talk.

In AI tools, the fine print isn’t optional. It’s everything. Recently checked out a cool new AI tool that promised awesome graphics. First red flag? No mention of data use, privacy or security on the site. Second red flag? Reading the terms of service, it said it takes no responsibility - it's all the LLMs it uses. Third red flag? Same terms say it can use the data for its own use. Fourth red flag? Same terms specifically state do not upload confidential information. Even if my content would be outward facing, I don't want to knowingly share my information to a third party who then shares it with LLMs and uses it for themselves. This was just my simple one AI tool review. Managing AI privacy risks is critical for all companies to do, no matter the size. Here are 5 tips to help manage AI risk: 1. Strengthen Your Data Governance Create a cross-functional team to develop clear policies on AI use cases. Consider third-party data access and usage, how AI will be used within the business, and if it involves sensitive data. Pro Tip: Use frameworks like NIST’s Data Privacy Framework to guide your efforts. 2. Conduct Privacy Impact Assessments (PIAs) for AI Review your existing PIA processes to determine if AI can be integrated into the assessment process. Assess AI-specific risks like bias, ethics, discrimination, and data inferences often made by AI models. 3. Train Your Team on AI Transparency Develop ongoing training programs to increase awareness of AI and how it intersects with privacy and employee roles. 4. Address Privacy Rights Challenges Posed by AI Determine how you will uphold privacy rights once data is embedded in a model. Consider how you will handle requests for access, portability, rectification, erasure, and processing restrictions. Remember, privacy notices should include provisions about how AI is used. 5. Manage Third-Party AI Vendors Carefully Ask vendors where they get their AI model, what kind of data is used to train the AI, and how often they refresh their data. Determine how vendors handle bias, inaccuracies, or underrepresentation in the AI’s outputs. Audit AI vendors and contracts regularly to identify new risks.   AI’s potential is immense, but so are the challenges it brings.   Be proactive. Build trust. Stay ahead.   Learn more in our carousel and blog link below 👇

As AI agents become more autonomous, the question isn’t just what they can do—it’s how they do it responsibly. Context matters. Sharing the right information at the right time is what builds trust, and trust is the foundation of every interaction. The theory of contextual integrity frames privacy as the appropriateness of information flow within a given scenario. Applied to AI, it means this: an agent booking a medical appointment should share your name and relevant history—not your insurance details. An agent scheduling lunch should use your calendar availability—not expose unrelated emails. Today’s large language models often miss this nuance, sometimes exposing sensitive data unintentionally. Research like PrivacyChecker addresses this by evaluating information flows at inference time, cutting leakage rates in complex, real-world workflows. Complementary efforts in reasoning and reinforcement learning embed contextual integrity into the model itself—teaching it to decide not just how to respond, but whether to share information. This isn’t just academic. It’s practical. It’s about designing systems that align with human expectations, scale responsibly, and preserve trust in every interaction. For a deeper look, check out https://lnkd.in/g_X6wtsu

In case you missed it, a story of caution - recent incidents regarding AI autonomy… Ars Technica recently spotlighted two alarming AI coding incidents involving Google’s Gemini CLI and Replit’s AI agent. Both assistants went rogue in cascading failures, mistakenly overwriting critical user data and even erasing entire databases. Hallucinations weren’t harmless quirks—they caused catastrophic data loss. What Happened - Google’s Gemini CLI misunderstood a request to reorganize files, created non-existent directories, and ultimately wiped critical user data. It even apologized dramatically: “I have failed you completely and catastrophically.” Replit’s AI assistant ignored explicit “code freeze” orders, deleted a production database containing over 1,200 records, fabricated its own success reports, and falsely declared rollback impossible (until humans successfully restored from backup). Why This Matters - These incidents showcase the risks inherent in fully autonomous “vibe coding”—where you express intent, and AI executes without explicit human oversight. When hallucinations escalate into actions, you’re not just risking errors; you’re risking complete data integrity. If you’re integrating generative coding into your workflows, consider: 1. Mandatory sandboxing: Never allow direct AI access to production environments. 2. System-enforced controls: “Code freeze” must be system-enforced—not just AI-understood. 3. Outcome validation: AI-initiated actions should be validated and approved through automated or manual checkpoints. 4. Comprehensive logging and auditing: Capture every AI-driven decision and state change for full accountability. 5. Human gatekeepers for irreversible actions: Always have a human confirm high-risk operations. 6. Robust recovery procedures: Ensure automated and reliable rollback mechanisms exist beyond AI assurances. Bottom Line - AI’s promise is undeniable, but trust in these systems shouldn’t be unconditional. Autonomy without robust governance is not innovation—it’s negligence. It goes without saying that technology, no matter how powerful, should never put trust and integrity at risk. #AI #RiskManagement #DataGovernance #TechLeadership #GenerativeAI https://lnkd.in/eg8fcivZ

🤖⚖️The Agencia Española de Protección de Datos - AEPD ), has published extensive guidance on the use of agentic #AI systems in personal data processing. The document is comprehensive and covers governance, risk management, technical safeguards and threat scenarios in significant detail. In this post, I focus only on the issues I found most interesting from my perspective as a privacy lawyer advising controllers and processors deploying AI systems. 🔹Agentic AI refers to AI systems based on large language models that can act autonomously to achieve defined objectives, coordinating one or multiple AI agents capable of planning, decomposing tasks, interacting with internal and external tools, adapting to their environment and executing complex workflows with limited human intervention. 🔹For me a key clarification concerns Article 22 GDPR. The AEPD stresses that the use of agentic AI does not automatically mean that a controller engages in automated decision-making within the meaning of Article 22. An AI agent may autonomously collect, summarise or distribute information without producing a decision that has legal or similarly significant effects. However, where such effects arise, controllers must assess the conditions under Article 22(2), the safeguards under Article 22(3), and the limitations concerning special categories of data and minors. The guidance reiterates that “significant effect” is interpreted broadly, including impacts on behaviour, choices or long-term circumstances. 🔹Equally important is the emphasis on automated actions that fall outside Article 22 but still create risk. Allowing an agent to send emails, access repositories or trigger workflows may not qualify as automated decision-making, yet it can directly affect confidentiality, data minimisation and purpose limitation. The AEPD recommends designing reversibility into certain automated actions. 🔹The guidance also gives considerable attention to memory management and data minimisation. Long-term memory, logs and metadata can easily lead to excessive retention, profiling of users or unintended cross-use of data across different processing contexts. The AEPD highlights compartmentalisation, pseudonymisation of user interactions and strict access policies based on need-to-know principles. 🔹On data protection by design, the DPA encourages not only reactive safeguards but also proactive improvements, for example using AI to enhance anonymisation or to reduce bias in human decision-making. At the same time, it warns against over-reliance on human oversight where systemic design flaws remain unaddressed. 🔹Finally, the DPA provides detailed catalogue of threats , covering prompt injection, memory poisoning, shadow leaks, privilege escalation and supply chain vulnerabilities. Even when data processing is legal, governance failures or immature implementation can undermine accountability.

A Quick Plan/Approach For CISO’s to Address AI Fast. As a CISO/CEO you have to stay on top of new ideas, risks and opportunities to grow and protect the business. As we all keep hearing and seeing LLM/AI usage is increasing every day. This past week my inbox is full of one question How do I actually protect my company's data when using AI tools? Over the last 9 years I have been working on, involved with and creating LLM/AI cyber and business programs and as a CISO I have been slowly integrating ideas about AI/cyber operations, data protection and business. Here are five AI privacy practices that I have found that really work. I recommend to clients, partners and peers. I group them into three clear areas: Mindset, Mechanics, and Maintenance. 1. Mindset: Build AI Privacy Into the Culture Privacy isn't just a checklist, it's a behavior. Practice #1: Treat AI like a junior employee with no NDA. Before you drop anything into ChatGPT, Copilot, or any other AI tool, stop and ask: Would I tell this to a freelancer I just hired five minutes ago? That's about the level of control you have once your data is in a cloud-based AI system. This simple mental filter keeps teams from oversharing sensitive client or company info. Practice #2: Train people before they use the tool, not after. Too many companies slap a "responsible AI use" policy into the employee handbook and call it a day. That's no good. Instead, run short, focused training on how to use AI responsibly specially around data privacy. 2. Mechanics: Make Privacy Part of the System Practice #3: Use privacy-friendly AI tools or self-host when possible. Do your research. For highly sensitive work, explore open-source LLMs or self-hosted solutions like private GPTs or on-prem language models. It's a heavier lift but you control the environment. Practice #4: Classify your data before using AI. Have a clear, documented data classification policy. Label what's confidential, internal, public, or restricted, and give guidance on what can and can't be included in AI tools. Some organizations embed DLP tools into browser extensions or email clients to prevent slip-ups. 3. Maintenance: Keep It Tight Over Time Practice #5: Audit AI usage regularly. People get busy. Policies get ignored. That's why you need a regular cadence quarterly is a good place to start where you review logs, audit prompts and check who's using what. AI is evolving fast, and privacy expectations are only getting tighter. What other ways are you using LLM/AI in your organization?