AI Governance Practices

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

I’m so happy to see this! Yesterday, the ISO published a new standard, ISO/IEC 42001:2023 for AI Management Systems. My suspicion is that it will become as important to the AI world as ISO/IEC 27001 arguably became the most important standard for information security management systems. The standard provides a comprehensive framework for establishing, implementing, maintaining, and improving an artificial intelligence management system within organisations. It aims to ensure responsible AI development, deployment, and use, addressing ethical implications, data quality, and risk management. This set of guidelines is designed to integrate AI management with organisational processes, focusing on risk management and offering detailed implementation controls. Key aspects of the standard include performance measurement, emphasising both quantitative and qualitative outcomes, and the importance of AI systems’ effectiveness in achieving intended results. It mandates conformity to requirements and systematic audits to assess AI systems. The standard also highlights the need for thorough assessment of AI's impact on society and individuals, stressing data quality to meet organisational needs. Organisations are required to document controls for AI systems and rationalise their decisions, underscoring the role of governance in ensuring performance and conformance. The standard calls for adapting management systems to include AI-specific considerations like ethical use, transparency, and accountability. It also requires continuous performance evaluation and improvement, ensuring AI systems' benefits and safety. ISO/IEC 42001:2023 aligns closely with the EU AI Act. The AI Act classifies AI systems into prohibited and high-risk categories, each with distinct compliance obligations. ISO/IEC 42001:2023's focus on ethical AI management, risk management, data quality, and transparency aligns with these categories, providing a pathway for meeting the AI Act’s requirements. The AI Act's prohibitions include specific AI systems like biometric categorisation and untargeted scraping for facial recognition. The standard may help guide organisations in identifying and discontinuing such applications. For high-risk AI systems, the AI Act mandates comprehensive risk management, registration, data governance, and transparency, which the ISO/IEC 42001:2023 framework could support. It could assist providers of high-risk AI systems in establishing risk management frameworks and maintaining operational logs, ensuring non-discriminatory, rights-respecting systems. ISO/IEC 42001:2023 may also aid users of high-risk AI systems in fulfilling obligations like human oversight and cybersecurity. It could potentially assist in managing foundation models and General Purpose AI (GPAI), necessary under the AI Act. This new standard offers a comprehensive approach to managing AI systems, aiding organisations in developing AI that respects fundamental rights and ethical standards.

Many engineers can build an AI agent. But designing an AI agent that is scalable, reliable, and truly autonomous? That’s a whole different challenge.  AI agents are more than just fancy chatbots—they are the backbone of automated workflows, intelligent decision-making, and next-gen AI systems. However, many projects fail because they overlook critical components of agent design.  So, what separates an experimental AI from a production-ready one?  This Cheat Sheet for Designing AI Agents breaks it down into 10 key pillars:  🔹 AI Failure Recovery & Debugging – Your AI will fail. The question is, can it recover? Implement self-healing mechanisms and stress testing to ensure resilience.  🔹 Scalability & Deployment – What works in a sandbox often breaks at scale. Using containerized workloads and serverless architectures ensures high availability.  🔹 Authentication & Access Control – AI agents need proper security layers. OAuth, MFA, and role-based access aren’t just best practices—they’re essential.  🔹 Data Ingestion & Processing – Real-time AI requires efficient ETL pipelines and vector storage for retrieval—structured and unstructured data must work together.  🔹 Knowledge & Context Management – AI must remember and reason across interactions. RAG (Retrieval-Augmented Generation) and structured knowledge graphs help with long-term memory.  🔹 Model Selection & Reasoning – Picking the right model isn't just about LLM size. Hybrid AI approaches (symbolic + LLM) can dramatically improve reasoning.  🔹 Action Execution & Automation – AI isn't useful if it just predicts—it must act. Multi-agent orchestration and real-world automation (Zapier, LangChain) are key.  🔹 Monitoring & Performance Optimization – AI drift and hallucinations are inevitable. Continuous tracking and retraining keeps your AI reliable.  🔹 Personalization & Adaptive Learning – AI must learn dynamically from user behavior. Reinforcement learning from human feedback (RHLF) improves responses over time.  🔹 Compliance & Ethical AI – AI must be explainable, auditable, and regulation-compliant (GDPR, HIPAA, CCPA). Otherwise, your AI can’t be trusted.  An AI agent isn’t just a model—it’s an ecosystem. Designing it well means balancing performance, reliability, security, and compliance.  The gap between an experimental AI and a production-ready AI is strategy and execution.  Which of these areas do you think is the hardest to get right?

AI governance isn't a policy document. It's 6 layers, and most skip the first 5: Teams rush to write a policy and call it governance. But a policy you can't enforce is just a PDF. And you can't audit what you never inventoried in the first place. I've spent the last 8 years building AI products... And governance is the part nobody puts on the roadmap. Here's the full 6-layer stack 👇 (See the visual for all 30 components) 1️⃣ AI Inventory ↳ You can't govern what you can't see. Run a shadow AI pass and list every AI tool in use that nobody officially approved. ↳ Then tag each system with an owner and a risk tier. 2️⃣ Data Foundation ↳ Track where every training input comes from, and screen it for bias before it touches a model. ↳ Stale data is its own failure mode. Monitor freshness. 3️⃣ Data Security & Access ↳ Encryption, anonymization, role-based access. ↳ Least privilege by default. Not everyone needs the model keys. 4️⃣ Model Assurance ↳ Write a model card for everything in production: what it does, what it trained on, where it breaks. ↳ Then red-team it and watch for drift. 5️⃣ Human Oversight ↳ Name who can override the model, and who's accountable when it's wrong. In writing, before you need it. 6️⃣ Compliance & Audit ↳ EU AI Act mapping, GDPR alignment, audit trails. ↳ This is the layer everyone starts with. It only holds if the 5 below it exist. Most teams have a governance policy. Almost none have governance. The difference is the 5 layers nobody sees. And the EU AI Act won't wait for you to build them. Which layer is your org already implementing? Drop a comment below. ♻️ Repost to help the builders and PMs in your network. And follow Basia Kubicka for more on building AI responsibly.

Over the past 10+ years, I’ve had the opportunity to author or contribute to over 100 #datagovernance strategies and frameworks across all kinds of industries and organizations. Every one of them had its own challenges, but I started to notice something: there’s actually a consistent way to approach #data governance that seems to work as a starting point, no matter the region or the sector. I’ve put that into a single framework I now reuse and adapt again and again. Why does it matter? Getting this framework in place early is one of the most important things you can do. It helps people understand what data governance is (and what it isn’t), sets clear expectations, and makes it way easier to drive adoption across teams. A well-structured framework provides a simple, repeatable visual that you can use over and over again to explain data governance and how you plan to implement it across the organization. You’ll find the visual attached. I broke it down into five core components: 🔹 #Strategy – This is the foundation. It defines why data governance matters in your org and what you’re trying to achieve. Without it, governance will be or become reactive and fragmented. 🔹 #Capability areas – These are the core disciplines like policies & standards, data quality, metadata, architecture, and more. They serve as the building blocks of governance, making sure that all the essential topics are covered in a clear and structured way. 🔹 #Implementation – This one is a bit unique because most high-level frameworks leave it out. It’s where things actually come to life. It’s about defining who’s doing what (roles) and where they’re doing it (domains), so governance is actually embedded in the business, not just talked about. This is where your key levers of adoption sit. 🔹 #Technology enablement – The tools and platforms that bring governance to life. From catalogs to stewardship platforms, these help you scale governance across teams, systems, and geographies. 🔹 #Governance of governance – Sounds meta, but it’s essential. This is how you make sure the rest of the framework is actually covered and tracked — with the right coordination, forums, metrics, and accountability to keep things moving and keep each other honest. In next weeks, I’ll go a bit deeper into one or two of these. For the full article ➡️ https://lnkd.in/ek5Yue_H

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

BREAKING! The FDA just released this draft guidance, titled Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations, that aims to provide industry and FDA staff with a Total Product Life Cycle (TPLC) approach for developing, validating, and maintaining AI-enabled medical devices. The guidance is important even in its draft stage in providing more detailed, AI-specific instructions on what regulators expect in marketing submissions; and how developers can control AI bias. What’s new in it? 1) It requests clear explanations of how and why AI is used within the device. 2) It requires sponsors to provide adequate instructions, warnings, and limitations so that users understand the model’s outputs and scope (e.g., whether further tests or clinical judgment are needed). 3) Encourages sponsors to follow standard risk-management procedures; and stresses that misunderstanding or incorrect interpretation of the AI’s output is a major risk factor. 4) Recommends analyzing performance across subgroups to detect potential AI bias (e.g., different performance in underrepresented demographics). 5) Recommends robust testing (e.g., sensitivity, specificity, AUC, PPV/NPV) on datasets that match the intended clinical conditions. 6) Recognizes that AI performance may drift (e.g., as clinical practice changes), therefore sponsors are advised to maintain ongoing monitoring, identify performance deterioration, and enact timely mitigations. 7) Discusses AI-specific security threats (e.g., data poisoning, model inversion/stealing, adversarial inputs) and encourages sponsors to adopt threat modeling and testing (fuzz testing, penetration testing). 8) And proposed for public-facing FDA summaries (e.g., 510(k) Summaries, De Novo decision summaries) to foster user trust and better understanding of the model’s capabilities and limits.

Don’t let your AI project die in a notebook. You don’t need more features. You need structure. This is the folder setup that actually ships from day one. 📁 𝗧𝗵𝗲 𝗳𝗼𝗹𝗱𝗲𝗿 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀 Forget monolithic scripts. You need this: /config 🔹YAML files for models, prompts, logs 🔹Config lives outside the code, always /src 🔹Modular logic: llm/, utils/, handlers/ 🔹Clean, testable, scalable from day one /data 🔹Cached outputs, embeddings, prompt responses 🔹Cut latency + save on API costs instantly /notebooks 🔹For testing, analysis, and iteration 🔹Never pollute your main codebase again 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘀𝗼𝗹𝘃𝗲𝘀 ▪️Prompt versioning is built in ▪️Rate limiting and caching come standard ▪️Error handling is modular ▪️Experiments stay reproducible ▪️Deployment is one Dockerfile away 𝗕𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗯𝗮𝗸𝗲𝗱 𝗶𝗻 1. Prompts are versioned by default ▪️Stored in prompt_templates.yaml + templates⋅py ▪️Track, test, roll back 2. Rate limiting is pre-integrated ▪️rate_limiter⋅py stops API overloads and surprise bills 3. Caching is plug-and-play ▪️Duplicate calls get stored in /data/cache ▪️Cut costs by 70% on day one 4. Each module does one thing only ▪️Models in llm/, logs in utils/, errors in handlers/ ▪️No sprawl 5. Notebooks are safely isolated ▪️Run tests and explorations in prompt_testing.ipynb ▪️Nothing leaks into production logic ⚙️ Clone the github template below - in first comment This structure ships faster, costs less and scales without rewrites. ------------ ⚡I’m Nina. I build with AI and share how it’s done weekly. #aiagents #softwaredevelopment #MCP #genai #promptengineering

AI Governance Isn't a Policy Document. It's a System. Most organizations approach AI governance by writing policies. The leaders are building governance into the architecture itself. As AI adoption accelerates, governance can no longer be treated as a compliance checkbox. It needs to be embedded across the entire AI lifecycle. A practical way to think about it is through these 6 layers of AI Governance: 1. AI Inventory You can't govern what you don't know exists. Maintain visibility into AI systems through model registries, risk tiering, ownership assignment, system classification, and shadow AI detection. 2. Data Foundation The quality of AI outcomes depends on the quality of the data behind them. Track data sources, lineage, freshness, quality, and potential bias before they become business risks. 3. Data Security & Access Governance starts with controlling who can access what. Encryption, anonymization, role-based access controls, least-privilege principles, and strong key management form the foundation of trust. 4. Model Assurance Models should be continuously evaluated, not just deployed. Performance benchmarking, fairness testing, red teaming, drift detection, and model documentation help ensure reliability over time. 5. Human Oversight AI should support decisions, not operate without accountability. Decision reviews, escalation paths, override authority, output validation, and accountability mapping keep humans in control when it matters most. 6. Compliance & Audit Regulations are evolving rapidly. Organizations need clear audit trails, policy enforcement mechanisms, incident reporting processes, and alignment with frameworks such as GDPR and the EU AI Act. The biggest challenge in AI governance isn't technology. It's creating a framework where innovation can move fast without compromising security, compliance, accountability, or trust. The organizations that get this right will scale AI confidently. The ones that don't may spend more time managing risk than creating value. Which of these six layers do you think organizations struggle with the most today? #AIGovernance #AI #ResponsibleAI #GenAI 

Most "AI governance" frameworks are data governance with a fresh coat of paint. I keep seeing the same checklist. Access control. Data quality. Audit logs. Inventory. Security. Lineage. This is very 2015. None of it tells you whether your model is safe to ship. Whether it's quietly drifting in production. What your agent is allowed to do when no one's watching. That's the AI part. And it's usually the part that's missing. Here's the version I'd build. Eight pillars: 1. AI Inventory & Ownership Every system, tiered by risk, with a named owner. 2. Data Foundation Where the data came from, whether it's clean, whether you're allowed to use it. 3. Model Lifecycle Test it before it ships. Watch it after. You need to be able to pull it back. A model that passed review in March can be dangerous by June. 4. Data Security & Privacy Protect the data, and the model, against attacks aimed at it. 5. Access Control Who/what can access data/APIs, on behalf of whom, and until when? 6. Agent Governance Tool permissions, action limits, a human in the loop, a kill switch. The moment it can act on your behalf, "what could it do" stops being hypothetical. 7. Human Oversight A person owns the outcome. 8. Compliance & Audit Meet the rules, document the system, keep a record you can defend. Two of these are new in the AI age: Model Lifecycle and Agent Governance. They're new because they're the hard part. The part that fails in production. The data checklist keeps auditors happy. The model and the agent are what keep you up at night. Govern those. What's on your governance list that isn't just data governance? -- I’m building a newsletter to go deeper: Build What Matters. Weekly drops on AI agents + emerging workflows. Subscribe Here 👉 https://lnkd.in/dsP2uu3k ♻️ Repost if someone in your network needs to read this. ➕ Follow Luís Rodrigues for practical AI + Business insights