Building a Privacy-Centric AI Culture

⚠️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 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

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.