How to Manage AI Training Data Privacy Settings

AI is not failing because of bad ideas; it’s "failing" at enterprise scale because of two big gaps: 👉 Workforce Preparation 👉 Data Security for AI While I speak globally on both topics in depth, today I want to educate us on what it takes to secure data for AI—because 70–82% of AI projects pause or get cancelled at POC/MVP stage (source: #Gartner, #MIT). Why? One of the biggest reasons is a lack of readiness at the data layer. So let’s make it simple - there are 7 phases to securing data for AI—and each phase has direct business risk if ignored. 🔹 Phase 1: Data Sourcing Security - Validating the origin, ownership, and licensing rights of all ingested data. Why It Matters: You can’t build scalable AI with data you don’t own or can’t trace. 🔹 Phase 2: Data Infrastructure Security - Ensuring data warehouses, lakes, and pipelines that support your AI models are hardened and access-controlled. Why It Matters: Unsecured data environments are easy targets for bad actors making you exposed to data breaches, IP theft, and model poisoning. 🔹 Phase 3: Data In-Transit Security - Protecting data as it moves across internal or external systems, especially between cloud, APIs, and vendors. Why It Matters: Intercepted training data = compromised models. Think of it as shipping cash across town in an armored truck—or on a bicycle—your choice. 🔹 Phase 4: API Security for Foundational Models - Safeguarding the APIs you use to connect with LLMs and third-party GenAI platforms (OpenAI, Anthropic, etc.). Why It Matters: Unmonitored API calls can leak sensitive data into public models or expose internal IP. This isn’t just tech debt. It’s reputational and regulatory risk. 🔹 Phase 5: Foundational Model Protection - Defending your proprietary models and fine-tunes from external inference, theft, or malicious querying. Why It Matters: Prompt injection attacks are real. And your enterprise-trained model? It’s a business asset. You lock your office at night—do the same with your models. 🔹 Phase 6: Incident Response for AI Data Breaches - Having predefined protocols for breaches, hallucinations, or AI-generated harm—who’s notified, who investigates, how damage is mitigated. Why It Matters: AI-related incidents are happening. Legal needs response plans. Cyber needs escalation tiers. 🔹 Phase 7: CI/CD for Models (with Security Hooks) - Continuous integration and delivery pipelines for models, embedded with testing, governance, and version-control protocols. Why It Matter: Shipping models like software means risk comes faster—and so must detection. Governance must be baked into every deployment sprint. Want your AI strategy to succeed past MVP? Focus and lock down the data. #AI #DataSecurity #AILeadership #Cybersecurity #FutureOfWork #ResponsibleAI #SolRashidi #Data #Leadership

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.

Whether you’re integrating a third-party AI model or deploying your own, adopt these practices to shrink your exposed surfaces to attackers and hackers: • Least-Privilege Agents – Restrict what your chatbot or autonomous agent can see and do. Sensitive actions should require a human click-through. • Clean Data In, Clean Model Out – Source training data from vetted repositories, hash-lock snapshots, and run red-team evaluations before every release. • Treat AI Code Like Stranger Code – Scan, review, and pin dependency hashes for anything an LLM suggests. New packages go in a sandbox first. • Throttle & Watermark – Rate-limit API calls, embed canary strings, and monitor for extraction patterns so rivals can’t clone your model overnight. • Choose Privacy-First Vendors – Look for differential privacy, “machine unlearning,” and clear audit trails—then mask sensitive data before you ever hit Send. Rapid-fire user checklist: verify vendor audits, separate test vs. prod, log every prompt/response, keep SDKs patched, and train your team to spot suspicious prompts. AI security is a shared-responsibility model, just like the cloud. Harden your pipeline, gate your permissions, and give every line of AI-generated output the same scrutiny you’d give a pull request. Your future self (and your CISO) will thank you. 🚀🔐

Privacy isn’t a policy layer in AI. It’s a design constraint. The new EDPB guidance on LLMs doesn’t just outline risks. It gives builders, buyers, and decision-makers a usable blueprint for engineering privacy - not just documenting it. The key shift? → Yesterday: Protect inputs → Today: Audit the entire pipeline → Tomorrow: Design for privacy observability at runtime The real risk isn’t malicious intent. It’s silent propagation through opaque systems. In most LLM systems, sensitive data leaks not because someone intended harm but because no one mapped the flows, tested outputs, or scoped where memory could resurface prior inputs. This guidance helps close that gap. And here’s how to apply it: For Developers: • Map how personal data enters, transforms, and persists • Identify points of memorization, retention, or leakage • Use the framework to embed mitigation into each phase: pretraining, fine-tuning, inference, RAG, feedback For Users & Deployers: • Don’t treat LLMs as black boxes. Ask if data is stored, recalled, or used to retrain • Evaluate vendor claims with structured questions from the report • Build internal governance that tracks model behaviors over time For Decision-Makers & Risk Owners: • Use this to complement your DPIAs with LLM-specific threat modeling • Shift privacy thinking from legal compliance to architectural accountability • Set organizational standards for “commercial-safe” LLM usage This isn’t about slowing innovation. It’s about future-proofing it. Because the next phase of AI scale won’t just be powered by better models. It will be constrained and enabled by how seriously we engineer for trust. Thanks European Data Protection Board, Isabel Barberá H/T Peter Slattery, PhD

The Oregon Department of Justice released new guidance on legal requirements when using AI. Here are the key privacy considerations, and four steps for companies to stay in-line with Oregon privacy law. ⤵️ The guidance details the AG's views of how uses of personal data in connection with AI or training AI models triggers obligations under the Oregon Consumer Privacy Act, including: 🔸Privacy Notices. Companies must disclose in their privacy notices when personal data is used to train AI systems. 🔸Consent. Updated privacy policies disclosing uses of personal data for AI training cannot justify the use of previously collected personal data for AI training; affirmative consent must be obtained. 🔸Revoking Consent. Where consent is provided to use personal data for AI training, there must be a way to withdraw consent and processing of that personal data must end within 15 days. 🔸Sensitive Data. Explicit consent must be obtained before sensitive personal data is used to develop or train AI systems. 🔸Training Datasets. Developers purchasing or using third-party personal data sets for model training may be personal data controllers, with all the required obligations that data controllers have under the law. 🔸Opt-Out Rights. Consumers have the right to opt-out of AI uses for certain decisions like housing, education, or lending. 🔸Deletion. Consumer #PersonalData deletion rights need to be respected when using AI models. 🔸Assessments. Using personal data in connection with AI models, or processing it in connection with AI models that involve profiling or other activities with heightened risk of harm, trigger data protection assessment requirements. The guidance also highlights a number of scenarios where sales practices using AI or misrepresentations due to AI use can violate the Unlawful Trade Practices Act. Here's a few steps to help stay on top of #privacy requirements under Oregon law and this guidance: 1️⃣ Confirm whether your organization or its vendors train #ArtificialIntelligence solutions on personal data.  2️⃣ Validate your organization's privacy notice discloses AI training practices. 3️⃣ Make sure organizational individual rights processes are scoped for personal data used in AI training. 4️⃣ Set assessment protocols where required to conduct and document data protection assessments that address the requirements under Oregon and other states' laws, and that are maintained in a format that can be provided to regulators.

SAP Customer Data security when using 3rd party LLM's SAP ensures the security of customer data when using third-party large language models (LLMs) through a combination of robust technical measures, strict data privacy policies, and adherence to ethical guidelines. Here are the key strategies SAP employs: 1️⃣ Data Anonymization ↳ SAP uses data anonymization techniques to protect sensitive information. ↳ The CAP LLM Plugin, for example, leverages SAP HANA Cloud's anonymization capabilities to remove or alter personally identifiable information (PII) from datasets before they are processed by LLMs. ↳ This ensures that individual privacy is maintained while preserving the business context of the data. 2️⃣ No Sharing of Data with Third-Party LLM Providers ↳ SAP's AI ethics policy explicitly states that they do not share customer data with third-party LLM providers for the purpose of training their models. ↳ This ensures that customer data remains secure and confidential within SAP's ecosystem. 3️⃣ Technical and Organizational Measures (TOMs) ↳ SAP constantly improves upon its Technical and Organizational Measures (TOMs) to protect customer data against unauthorized access, changes, or deletions. ↳ These measures include encryption, access controls, and regular security audits to ensure compliance with global data protection laws. 4️⃣ Compliance with Global Data Protection Laws ↳ SAP adheres to various global data protection regulations, such as GDPR, CCPA, and others. ↳ They have implemented a Data Protection Management System (DPMS) to ensure compliance with these laws and to protect the fundamental rights of individuals whose data is processed by SAP. 5️⃣ Ethical AI Development ↳ SAP's AI ethics policy emphasizes the importance of data protection and privacy. They follow the 10 guiding principles of the UNESCO ↳ Recommendation on the Ethics of Artificial Intelligence, which include privacy, human oversight, and transparency. ↳ This ethical framework governs the development and deployment of AI solutions, ensuring that customer data is handled responsibly. 6️⃣ Security Governance and Risk Management ↳ SAP employs a risk-based methodology to support planning, mitigation, and countermeasures against potential threats. ↳ They integrate security into every aspect of their operations, from development to deployment, following industry standards like NIST and ISO. SAP ensures the security of customer data when using third-party LLMs through data anonymization, strict data sharing policies, robust technical measures, compliance with global data protection laws, ethical AI development, and comprehensive security governance. #sap #saptraining #zarantech #AI #LLM #DataSecurity #india #usa #technology Disclaimer: Image generated using AI tool.

21/86: 𝗜𝘀 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗼𝗻 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮? Your AI needs data, but is it using personal data responsibly? 🛑Threat Alert: If your AI model trains on data linked to individuals, you risk: Privacy violations, Legal & regulatory consequences, and Erosion of digital trust. 🔍 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗔𝘀𝗸 𝗕𝗲𝗳𝗼𝗿𝗲 𝗨𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗔𝗜 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 📌 Is personal data necessary? If not essential, don't use it. 📌 Are unique identifiers included? Consider pseudonymization or anonymization. 📌 Do you have a legal basis? If the model uses PII, document your justification. 📌 Are privacy risks documented & mitigated? Ensure privacy impact assessments (PIAs) are conducted. ✅ What You Should Do ➡️ Minimize PII usage – Only use personal data when absolutely necessary. ➡️ Apply de-identification techniques – Use pseudonymization, anonymization, or differential privacy where possible. ➡️ Document & justify your approach – Keep records of privacy safeguards & compliance measures. ➡️ Align with legal & ethical AI principles – Ensure your model respects privacy, fairness, and transparency. Privacy is not a luxury, it’s a necessity for AI to be trusted. Protecting personal data strengthens compliance, ethics, and public trust in AI systems. 💬 How do you ensure AI models respect privacy? Share your thoughts below! 👇 🔗 Follow PALS Hub and Amaka Ibeji for more AI risk insights! #AIonAI #AIPrivacy #DataProtection #ResponsibleAI #DigitalTrust 

The Cybersecurity and Infrastructure Security Agency together with the National Security Agency, the Federal Bureau of Investigation (FBI), the National Cyber Security Centre, and other international organizations, published this advisory providing recommendations for organizations in how to protect the integrity, confidentiality, and availability of the data used to train and operate #artificialintelligence. The advisory focuses on three main risk areas: 1. Data #supplychain threats: Including compromised third-party data, poisoning of datasets, and lack of provenance verification. 2. Maliciously modified data: Covering adversarial #machinelearning, statistical bias, metadata manipulation, and unauthorized duplication. 3. Data drift: The gradual degradation of model performance due to changes in real-world data inputs over time. The best practices recommended include: - Tracking data provenance and applying cryptographic controls such as digital signatures and secure hashes. - Encrypting data at rest, in transit, and during processing—especially sensitive or mission-critical information. - Implementing strict access controls and classification protocols based on data sensitivity. - Applying privacy-preserving techniques such as data masking, differential #privacy, and federated learning. - Regularly auditing datasets and metadata, conducting anomaly detection, and mitigating statistical bias. - Securely deleting obsolete data and continuously assessing #datasecurity risks. This is a helpful roadmap for any organization deploying #AI, especially those working with limited internal resources or relying on third-party data.