How to Standardize AI Development Processes

#GRC Today I led a session focused on rolling out a new Standard Operating Procedure (SOP) for the use of artificial intelligence tools, including generative AI, within our organization. AI tools offer powerful benefits (faster analysis, automation, improved communication) but without guidance, they can introduce major risks: • Data leakage • IP exposure • Regulatory violations • Inconsistent use across teams That’s why a well-crafted SOP isn’t just nice to have .. it’s a requirement for responsible AI governance. I walked the team through the objective: 1. To outline clear expectations and minimum requirements for engaging with AI tools in a way that protects company data, respects ethical standards, and aligns with core values. We highlighted the dual nature of AI (high value, high risk) and positioned the SOP as a safeguard, not a blocker. 2. Next, I made sure everyone understood who this applied to: • All employees • Contractors • Anyone using or integrating AI into business operations We talked through scenarios like writing reports, drafting code, automating tasks, or summarizing client info using AI. 3. We broke down risk into: • Operational Risk: Using AI tools that aren’t vendor-reviewed • Compliance Risk: Feeding regulated or confidential data into public tools • Reputational Risk: Inaccurate or biased outputs tied to brand use • Legal Risk: Violation of third-party data handling agreements 4. We outlined what “responsible use” looks like: • No uploading of confidential data into public-facing AI tools • Clear tagging of AI-generated content in internal deliverables • Vendor-approved tools only • Security reviews for integrations • Mandatory acknowledgment of the SOP 5. I closed the session with action items: • Review and digitally sign the SOP • Identify all current AI use cases on your team • Flag any tools or workflows that may require deeper evaluation Don’t assume everyone understands the risk just because they use the tools. Frame your SOP rollout as an enablement strategy, not a restriction. Show them how strong governance creates freedom to innovate .. safely. Want a copy of the AI Tool Risk Matrix or the Responsible Use Checklist? Drop a comment below.

The ISO - International Organization for Standardization has adopted ISO/IEC 42001, the world's first "Artificial Intelligence Management System (#AIMS)." #ISO42001 is an international #standard that specifies requirements for establishing, implementing, maintaining, and continually improving an #artificialintelligence management system for entities providing or utilizing #AI-based products or services. The #ISO 42001 aims to provide a comprehensive approach for organizations to systematically address and control the #risks related to the development and deployment of AI. The standard emphasizes a commitment to responsible AI practices, fostering global interoperability, and setting a foundation for the development and deployment of #responsibleAI. The new standard is based on the High-Level Structure (#HLS) of ISO/IEC, which gives management system standards a uniform structure and similar core content. It also provides a list of #controls for organizations to choose which ones they deem relevant for implementation. At a glance, implementation of ISO/IEC 42001 requires: - Integrating AI management with the current systems and structures in your organization (AIMS). - Performing an impact analysis evaluating how AI systems affect individuals and society as a whole, taking safety, transparency, and fairness into account. - Creating and enforcing AI-related policies, with an emphasis on internal structure, AI resources, and the lifecycle of AI systems. - Managing data responsibly, including training data preparation and management, utilized in AI systems. - Monitoring and ongoing development to make sure that the AI systems in use are in line with organizational objectives. https://lnkd.in/eiVEuxSY

To meet the ISO 42001 requirements, you will need to thoroughly document specific information to demonstrate effective control, governance, and monitoring of your Artificial Intelligence Management System (AIMS). Below are some of the more critical aspects to be included. 1. AIMS Policy and Objectives: · Document the policy that aligns with the organization's strategic goals and risk appetite. · Specify the objectives guiding the organization's AI-related activities and how they meet legal, regulatory, and risk management requirements. 2. AI System Impact Assessments: · Provide comprehensive impact assessments considering legal, social, and ethical effects. · Detail potential impacts on individuals and societies and actions to mitigate risks. 3. Roles and Responsibilities: · Clearly define the roles and responsibilities involved in the AI system's design, development, and operation. · Ensure accountability for AI governance, including human oversight mechanisms. 4. System Design and Development: · Document the AI system's design and architecture, including data flow diagrams and security controls. · Outline the rationale for the chosen algorithms and how data is collected, processed, and used. 5. Resource Documentation: · Provide detailed information on AI system resources, such as computing infrastructure, algorithms, data sets, and human resources. · Ensure that resource requirements are aligned with system specifications and security measures. 6. Technical and User Documentation: · Include technical manuals detailing system architecture, usage instructions, and resource requirements. · Provide user-facing information on system interactions, limitations, and reporting procedures. 7. Risk Management and Security Controls: · Document identified risks and the control measures implemented to mitigate them. · Include details of the data security and privacy measures used throughout the system's lifecycle. 8. Monitoring and Review: · Record processes for the ongoing monitoring, evaluation, and improvement of the AI system's performance. · Document incident response procedures and corrective actions for system failures. 9. Supplier and Customer Management: · Document supplier evaluation, selection, and performance monitoring processes. · Provide information on customer requirements, use guidelines, and risk assessments. 10. System Operation and Maintenance: · Provide documentation for system operation, including event logging, user training, and system health monitoring. · Record maintenance schedules, system updates, and performance reviews. Though the above listing is not fully comprehensive, these documentation requirements can aid in ensuring that your organization's AIMS provides robust, transparent, and effective management, adhering to ISO 42001 standards and safeguarding organizational and societal interests. Please reach out if you'd like to discuss! A-LIGN #iso42001 #TheBusinessofCompliance #ComplianceAlignedtoYou

AI adoption is accelerating across every enterprise. But as use scales, so does complexity—fast. 𝗪𝗵𝗮𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗮𝘀 𝘀𝗶𝗺𝗽𝗹𝗲 𝗺𝗼𝗱𝗲𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 𝗯𝗲𝗰𝗮𝗺𝗲 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲: —> Inconsistent APIs, shifting quotas, unpredictable latency, opaque costs and fragile governance. 𝗘𝗮𝗰𝗵 𝗻𝗲𝘄 𝗺𝗼𝗱𝗲𝗹, 𝗲𝗮𝗰𝗵 𝗻𝗲𝘄 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝗿, 𝗲𝗮𝗰𝗵 𝗻𝗲𝘄 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲—𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗹𝗮𝘆𝗲𝗿 𝗼𝗳 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗼𝘃𝗲𝗿𝗵𝗲𝗮𝗱. —> Engineering teams began stitching together custom logic just to keep things running. 𝗕𝘂𝘁 𝘀𝘁𝗶𝘁𝗰𝗵𝗶𝗻𝗴 𝗱𝗼𝗲𝘀𝗻’𝘁 𝘀𝗰𝗮𝗹𝗲. And scattered wrappers don’t create resilience, observability or compliance. Enterprises need more than just access to models—they need control over how models were used. flexibility with enforceability. access and accountability. 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗔𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 𝗰𝗼𝗺𝗲𝘀 𝗶𝗻. It’s not a router. It’s the control layer—the policy, security and reliability surface for modern AI systems. It unifies model access, standardizes interaction, and governs usage in real time. Latency-aware routing, semantic caching, role-based throttling, token-level cost tracking—all in one place. And it doesn't stop at models. 𝗧𝗵𝗲 𝗿𝗶𝘀𝗲 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲𝗱 𝗮 𝗻𝗲𝘄 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻: —> agents coordinating across systems, invoking tools, and completing tasks autonomously. These agents need structure, guardrails, and secure interoperability. So the Gateway expands—mediating with Model Context Protocol (MCP) and enabling safe Agent-to-Agent (A2A) communication. It becomes the backbone for intelligent orchestration. Every prompt, tool call, fallback and output routed through a governed, observable path. Security policies are enforced in the execution path—not after the fact. And every action is logged, attributed, and auditable by design. This isn’t theory—it’s how AI is being deployed at scale today. Across public cloud, private clusters, hybrid environments and compliance heavy industries (financial services, healthcare, insurance). Yes, you can build something lightweight to get started. 𝗕𝘂𝘁 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝗹𝗼𝗻𝗴 𝗴𝗮𝗺𝗲—𝗮𝗻𝗱 𝗶𝘁 𝗱𝗲𝗺𝗮𝗻𝗱𝘀 𝗿𝗲𝗮𝗹 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. The question isn't whether to adopt a control layer… It's whether that layer is ready for the scale, risk and opportunity in front of you. 𝗜𝗻 𝟮𝟬𝟮𝟱, 𝗲𝘃𝗲𝗿𝘆 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘄𝗶𝗹𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗔𝗜. 𝗢𝗻𝗹𝘆 𝗮 𝗳𝗲𝘄 𝘄𝗶𝗹𝗹 𝗱𝗼 𝗶𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝘀𝗽𝗲𝗲𝗱 𝘁𝗼 𝗹𝗮𝘀𝘁...

This new white paper "Introduction to AI assurance" by the UK Department for Science, Innovation, and Technology from Feb 12, 2024, provides an EXCELLENT overview of assurance methods and international technical standards that can be utilized to create and implement ethical AI systems. The new guidance is based on the UK AI governance framework, laid out in the 2023 white paper "A pro-innovation approach to AI regulation". This white paper defined 5 universal principles applicable across various sectors to guide and shape the responsible development and utilization of AI technologies throughout the economy: - Safety, Security, and Robustness - Appropriate Transparency and Explainability - Fairness - Accountability and Governance - Contestability and Redress The 2023 white paper also introduced a suite of tools designed to aid organizations in understanding "how" these outcomes can be achieved in practice, emphasizing tools for trustworthy AI, including assurance mechanisms and global technical standards. See: https://lnkd.in/gydvi9Tt The new publication, "Introduction to AI assurance," is a deep dive into these assurance mechanisms and standards. AI assurance encompasses a spectrum of techniques for evaluating AI systems throughout their lifecycle. These range from qualitative assessments for evaluating potential risks and societal impacts to quantitative assessments for measuring performance and legal compliance. Key techniques include: - Risk Assessment: Identifies potential risks like bias, privacy, misuse of technology, and reputational damage. - Impact Assessment: Anticipates broader effects on the environment, human rights, and data protection. - Bias Audit: Examines data and outcomes for unfair biases. - Compliance Audit: Reviews adherence to policies, regulations, and legal requirements. - Conformity Assessment: Verifies if a system meets required standards, often through performance testing. - Formal Verification: Uses mathematical methods to confirm if a system satisfies specific criteria. The white paper also explains how organizations in the UK can ensure their AI systems are responsibly governed, risk-assessed, and compliant with regulations: 1.) For demonstrating good internal governance processes around AI, a conformity assessment against standards like ISO/IEC 42001 (AI Management System) is recommended. 2.) To understand the potential risks of AI systems being acquired, an algorithmic impact assessment by a accredited conformity assessment body is advised. This involves (self) assessment against a proprietary framework or responsible AI toolkit. 3.) Ensuring AI systems adhere to existing data protection regulations involves a compliance audit by a third-party assurance provider. This white paper also has exceptional infographics! Pls, check it out, and TY Victoria Beckman for posting and providing us with great updates as always!

A New Path for Agile AI Governance To avoid the rigid pitfalls of past IT Enterprise Architecture governance, AI governance must be built for speed and business alignment. These principles create a framework that enables, rather than hinders, transformation: 1. Federated & Flexible Model: Replace central bottlenecks with a federated model. A small central team defines high-level principles, while business units handle implementation. This empowers teams closest to the data, ensuring both agility and accountability. 2. Embedded Governance: Integrate controls directly into the AI development lifecycle. This "governance-by-design" approach uses automated tools and clear guidelines for ethics and bias from the project's start, shifting from a final roadblock to a continuous process. 3. Risk-Based & Adaptive Approach: Tailor governance to the application's risk level. High-risk AI systems receive rigorous review, while low-risk applications are streamlined. This framework must be adaptive, evolving with new AI technologies and regulations. 4. Proactive Security Guardrails: Go beyond traditional security by implementing specific guardrails for unique AI vulnerabilities like model poisoning, data extraction attacks, and adversarial inputs. This involves securing the entire AI/ML pipeline—from data ingestion and training environments to deployment and continuous monitoring for anomalous behavior. 5. Collaborative Culture: Break down silos with cross-functional teams from legal, data science, engineering, and business units. AI ethics boards and continuous education foster shared ownership and responsible practices. 6. Focus on Business Value: Measure success by business outcomes, not just technical compliance. Demonstrating how good governance improves revenue, efficiency, and customer satisfaction is crucial for securing executive support. The Way Forward: Balancing Control & Innovation Effective AI governance balances robust control with rapid innovation. By learning from the past, enterprises can design a resilient framework with the right guardrails, empowering teams to harness AI's full potential and keep pace with business. How does your Enterprise handle AI governance?

Are you curious about how to create safe and effective artificial intelligence and machine learning (AI/ML) devices? Let's demystify the essential guiding principles outlined by the U.S. FDA, Health Canada | Santé Canada, and the United Kingdom’s Medicines and Healthcare products Regulatory Agency (MHRA) for Good Machine Learning Practice (GMLP). These principles aim to ensure the development of safe, effective, and high-quality medical devices. 1. Multi-Disciplinary Expertise Drives Success: Throughout the lifecycle of a product, it's crucial to integrate expertise from diverse fields. This ensures a deep understanding of how a model fits into clinical workflows, its benefits, and potential patient risks. 2. Prioritize Good Software Engineering and Security Practices: The foundation of model design lies in solid software engineering practices, coupled with robust data quality assurance, management, and cybersecurity measures. 3.Representative Data is Key: When collecting clinical study data, it's imperative to ensure it accurately represents the intended patient population. This means capturing relevant characteristics and ensuring an adequate sample size for meaningful insights. 4.Independence of Training and Test Data: To prevent bias, training and test datasets should be independent. While the FDA permits multiple uses of training data, it's crucial to justify each use to avoid inadvertently training on test data. 5. Utilize Best Available Reference Datasets: Developing reference datasets based on accepted methods ensures the collection of clinically relevant and well-characterized data, understanding their limitations. 6. Tailor Model Design to Data and Intended Use: Designing the model should align with available data and intended device usage. Human factors and interpretability should be prioritized, focusing on the performance of the Human-AI team. 7. Test Under Clinically Relevant Conditions: Rigorous testing plans should be in place to assess device performance under conditions reflecting real-world usage, independent of training data. 8. Provide Clear Information to Users: Users should have access to clear, relevant information tailored to their needs, including the product’s intended use, performance characteristics, data insights, limitations, and user interface interpretation. 9. Monitor Deployed Models for Performance: Deployed models should be continuously monitored in real-world scenarios to ensure safety and performance. Additionally, managing risks such as overfitting, bias, or dataset drift is crucial for sustained efficacy. These principles provide a robust framework for the development of AI/ML-driven medical devices, emphasizing safety, efficacy, and transparency. For further insights, dive into the full paper from FDA, MHRA, and Health Canada. #AI #MachineLearning #HealthTech #MedicalDevices #FDA #MHRA #HealthCanada

Most companies fail at transforming their GenAI pilots into sustainable business value. This excellent overview from Stephan Bloehdorn and his team highlights some best practices for scaling AI solutions at enterprises: 1. 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡:  - Adopt a product & platform engineering model, focusing on cross-functional teams. - Design AI-powered digital workflows with a focus on clear business outcomes rather than just tech. 2. 𝐅𝐥𝐞𝐱𝐢𝐛𝐥𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞:  - Implement a modular Data & AI platform to adapt to future AI advancements, manage costs, and streamline integration. 3. 𝐒𝐨𝐥𝐢𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬:  - Embrace standardized processes across all Data & AI implementations, to guarantee quality, repeatability, and efficiency. - Common tactics include building templates and automations for data and model workflows. 4. 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞-𝐰𝐢𝐝𝐞 𝐋𝐢𝐭𝐞𝐫𝐚𝐜𝐲:  - Invest in upskilling all employees in Data & AI - Foster a culture ready to identify valuable use cases and leverage new AI tools 5. 𝐑𝐨𝐛𝐮𝐬𝐭 𝐀𝐈 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞:  - Develop comprehensive AI governance frameworks to ensure compliance, risk management, and model lifecycle oversight. - Support this with the right tools and checks 🤔 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐬𝐨𝐦𝐞 𝐨𝐭𝐡𝐞𝐫 𝐛𝐞𝐬𝐭 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐲𝐨𝐮'𝐯𝐞 𝐬𝐞𝐞𝐧? 🔎 Detailed case studies and additional info in comments. -------- 🔔 If you like this, please repost it and share it with anyone who should know this ♻️ and follow me Heena Purohit, for more AI insights and trends. #artificialintelligence #enterpriseai #aiforbusiness #aiapplications #aiadoption