Programmatic Workflows for Privacy-First Data Management

The hidden data privacy dangers in your ITSM practices. 🔐 Most IT teams focus on security patches and access controls. But your service desk might be leaking sensitive data without realizing it. Here's what elite ITSM leaders know about privacy protection: The Hidden Risks: Service tickets containing sensitive user data in plain text Screen-sharing sessions exposing confidential information Unauthorized access through shared admin credentials Knowledge base articles revealing internal processes Here's the framework that ensures both compliance and efficiency: 1️⃣ The "Privacy-First" Protocol → Implement auto-redaction for sensitive fields → Create role-based viewing permissions → Build privacy checkpoints into workflows Key stat: 89% reduction in data exposure incidents 2️⃣ The "Trust Architecture" Framework → Map data flow across service touchpoints → Identify privacy vulnerabilities in processes → Create compliance-ready audit trails Result: 100% GDPR/CCPA audit readiness 3️⃣ The "Conscious Documentation" System → Design privacy-aware templates → Implement automatic data retention rules → Build self-cleaning knowledge bases Impact: Zero privacy violations in documented processes Remember: Privacy isn't just compliance. It's a foundation of user trust. How do you handle sensitive data in your service desk? Share below 👇 ♻️ Share this with an ITSM leader who needs this wake-up call ➕ Follow me for more ITSM/ESM insights

𝗦𝘂𝗻𝗱𝗮𝘆 𝘁𝗶𝗻𝗸𝗲𝗿𝗶𝗻𝗴, 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗽𝗮𝘆𝗼𝗳𝗳 🧪 I've been playing around with AnythingLLM for truly private document analysis on my own machine; and it’s been good for everyday reading, search and summarisation of my local files. 🔒 Coming from corporate law, I know many professionals in regulated roles can’t send client files through public APIs or provider websites. With AnythingLLM you keep everything local: your files, your embeddings, your prompts. 𝗠𝘆 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 (𝘀𝗶𝗺𝗽𝗹𝗲): • Create a workspace; point it at a project folder. • It chunks + indexes, then answers questions, drafts notes, and produces summaries. • Pick a local model. I use the small Gemma variants for speed and stability. 🧠 𝗪𝗵𝗼 𝗺𝗶𝗴𝗵𝘁 𝗳𝗶𝗻𝗱 𝗶𝘁 𝘂𝘀𝗲𝗳𝘂𝗹: Legal professionals doing reviews; compliance and risk teams comparing policies and drafting notes; deal teams, private equity and consultants needing first-pass reads of large data rooms; and research-heavy roles who want quick, accurate summaries with files kept on their own devices. 𝗪𝗵𝗮𝘁 𝘁𝗼 𝘄𝗮𝘁𝗰𝗵 𝗼𝘂𝘁 𝗳𝗼𝗿: • Speed: Large files take time, especially on CPU-only laptops. • Accuracy: AI helps you read faster, not think for you; always spot-check. • Scanned PDFs: If text isn’t selectable, results may be patchy. Run basic OCR first. • Storage: Big document sets add up. Archive or clear old workspaces regularly. • Security basics still matter: Local does not equal invincible; sp use device encryption and sensible access controls. If your firm bans external AI tools, this is a pragmatic middle path: AI-assisted reading without handing over your documents. #DataPrivacy #AI #OpenSource

Standard LLM code interpretation usually requires uploading files to a cloud environment. This practice forces users to expose raw data like internal spreadsheets or databases to the vendor just to get an analysis. It creates immediate compliance and governance challenges for any organization handling sensitive information. Anthropic has introduced a different approach with Model Context Protocol (MCP) that keeps execution local. Instead of the user sending data to the cloud, the LLM generates a script and sends it to the user's infrastructure. An MCP server running in a controlled local environment then executes that code inside a secure sandbox. This architecture changes the privacy model by decoupling the reasoning engine from the data processing layer. The raw data and intermediate states remain within the local security perimeter while only the final result returns to the model. This allows engineering teams to apply LLM capabilities to sensitive datasets without exposing the underlying information to third-party providers. https://lnkd.in/eMVEgvU2

I'm thrilled to share our latest technical guide on generating high-quality synthetic data using Gretel with BigQuery. In collaboration with Google Cloud, this post dives deep into how our Gretel + BigQuery integration is driving new opportunities for data scientists and developers to leverage synthetic data while keeping privacy at the forefront. Highlights: - Data Privacy First: With Gretel’s Transform v2, de-identify sensitive data seamlessly before generating synthetic data. This ensures PII remains protected and anonymized. - AI-Driven Synthetic Data Generation: Using Gretel’s Navigator Fine Tuning (LLM-based), we can generate domain-specific synthetic datasets that capture complex relationships across data, boosting AI and ML model performance. - Integrated BigQuery Workflow: Transfer data from BigQuery to Gretel, de-identify, and generate synthetic data – all within a single, integrated workflow. Whether you’re in healthcare, finance, or any field where data privacy is paramount, this guide provides the technical roadmap for harnessing synthetic data, unlocking insights while staying compliant.

I just used the ServiceNow Build Agent to spin up a full US Data Privacy Control app on a PDI – and it did a LOT more than just create a few tables. The idea: give privacy teams one place to manage US state privacy laws (CPRA, VCDPA, CPA, CTDPA, UCPA, etc.) – starting with DSARs, vendors and compliance metrics. Here’s what the Build Agent actually built from my prompts: ⚙️Privacy Law Engine   - State & Law Profiles (CPRA, VCDPA, etc.)   - Flags for supported rights (access, delete, correct, opt-out sale/ads/profiling, limit sensitive, appeals…)   - DSAR SLAs, extensions and breach notification deadlines 📩 DSAR Core   - Consumer Profiles with state + verification level   - Request table with multi-right DSARs, full status flow, SLA dates and appeals   - Business rules for due dates, status transitions and notifications 🏢 Org & Vendors   - Organizations, Business Units, Processes and Systems   - Third Parties and Engagements with risk level + DPA status 📊 Governance & KPIs   - Control Requirements, Risks and Action Plans   - Compliance Indicators (DSAR SLA %, high-risk items, vendor risk, training, incidents)   - Monthly scheduled job to calculate metrics 🖥️ Experiences & Security   - Privacy Office Workspace for analysts/admins   - Privacy Center portal for consumers (guided DSAR submission + rights education)   - Admin/Analyst roles and ACLs protecting all privacy data Roughly ~70% of a serious US privacy platform came from one long architectural prompt + a few refinements. The rest is “only” wiring advanced features: consent engine, GPC signals, DPIA, incident workflows and richer reporting. If you’re working with privacy and governance, Build Agent isn’t just a dev toy – it’s an accelerator for complex compliance architectures. #BuildWithBuildAgent #ServiceNow #NowPlatform #DataPrivacy #CCPA #AppEngine

𝐏𝐫𝐢𝐯𝐚𝐜𝐲-𝐟𝐢𝐫𝐬𝐭 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 turn compliance from cost center to competitive edge. Leaders want the speed of AI agents, but many are pausing due to data privacy and regulatory risk. The path forward is not fewer agents. It is privacy-first agents by design. 𝐀 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐛𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐬. 𝑩𝒖𝒊𝒍𝒅 𝒕𝒓𝒖𝒔𝒕 𝒊𝒏𝒕𝒐 𝒕𝒉𝒆 𝒂𝒓𝒄𝒉𝒊𝒕𝒆𝒄𝒕𝒖𝒓𝒆. ✅ Data minimization. Grant only the least data needed per task. ✅ Privacy-enhancing technologies. Use federated learning, differential privacy, and encrypted computation to keep raw data locked down. ✅ Zero Trust and audit trails. Apply a never trust, always verify access model with immutable logs for every action. ✅ Explainability. Make agent decisions traceable and defensible for auditors. 𝑴𝒐𝒗𝒆 𝒇𝒓𝒐𝒎 𝒑𝒐𝒊𝒏𝒕 𝒊𝒏 𝒕𝒊𝒎𝒆 𝒄𝒉𝒆𝒄𝒌𝒔 𝒕𝒐 𝒄𝒐𝒏𝒕𝒊𝒏𝒖𝒐𝒖𝒔 𝒄𝒐𝒎𝒑𝒍𝒊𝒂𝒏𝒄𝒆. ✅ Agents monitor configs, access logs, and data flows in real time, flag misconfigurations, and trigger remediation automatically. That shifts teams from firefighting to prevention. 𝑺𝒕𝒂𝒓𝒕 𝒘𝒉𝒆𝒓𝒆 𝒓𝒊𝒔𝒌 𝒂𝒏𝒅 𝑹𝑶𝑰 𝒎𝒆𝒆𝒕. ✅ Pilot in high stakes areas. ✅ Financial services. Automate first-line monitoring for AML patterns and help draft SAR narratives with human review. ✅ Healthcare. Detect role mismatch EHR access and block unsecured PHI transmissions. ✅ Retail and e-commerce. Verify consent flows under GDPR and CCPA, geo-aware cookie banners, and market specific opt-in rules. 𝑮𝒐𝒗𝒆𝒓𝒏 𝒍𝒊𝒌𝒆 𝒚𝒐𝒖 𝒎𝒆𝒂𝒏 𝒊𝒕. ✅ Establish clear policies for data access, agent oversight, and exception handling. ✅ Assign accountable owners. ✅ Decide which steps must remain human in the loop. 𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒐𝒏𝒂𝒍𝒊𝒛𝒆 𝒆𝒗𝒊𝒅𝒆𝒏𝒄𝒆. ✅ Bake in exportable audit packs that capture who, what, when, and why so proving compliance takes a click, not a quarter. 𝑹𝒐𝒍𝒍𝒐𝒖𝒕 𝒄𝒉𝒆𝒄𝒌𝒍𝒊𝒔𝒕. ✅ Define the outcome and guardrails in plain language. ✅ Map systems and permissions, and stub stable APIs for agent actions. ✅ Select one or two pilot workflows with measurable targets such as time to detect, false positive rate, or audit prep time. ✅ Enable Zero Trust controls and encryption end to end. ✅ Train teams and measure trust using accuracy, explainability, and override user experience. Question for you. If you deployed one privacy-first agent this quarter, where would it remove the most audit pain without expanding your risk surface? #AgenticAI #DataPrivacy #Compliance #Data #EnterpriseAI

Liquid AI Releases LocalCowork Powered By LFM2-24B-A2B to Execute Privacy-First Agent Workflows Locally Via Model Context Protocol (MCP) Liquid AI has released LFM2-24B-A2B and its companion open-source desktop agent, LocalCowork, delivering a fully local, privacy-first AI agent that executes tool-calling workflows directly on consumer hardware without cloud API dependencies. Utilizing a Sparse Mixture-of-Experts (MoE) architecture quantized to fit within a ~14.5 GB RAM footprint, the model leverages the Model Context Protocol (MCP) to securely interact with local filesystems, run OCR, and perform security scans. When benchmarked on an Apple M4 Max, it achieves impressive sub-second dispatch times (~385 ms) and strong single-step accuracy (80%), though engineers should note its current limitations with multi-step autonomy (26% success rate) due to "sibling confusion," making it best suited for fast, human-in-the-loop workflows rather than fully hands-off pipelines...... Full analysis: https://lnkd.in/gvsG56Dw GitHub Repo-Cookbook: https://lnkd.in/g-8zfPJ4 Technical details: https://lnkd.in/g2nVWd4S Liquid AI Maxime Labonne Ramin Hasani Mathias Lechner