{"product_id":"ai-risk-management-implementing-the-nist-ai-rmf-in-practice","title":"AI Risk Management: Implementing the NIST AI RMF","description":"\u003cdiv\u003e\n\u003cp\u003eThis course provides a practical, enterprise-focused approach to managing the risks associated with artificial intelligence systems by applying the NIST AI Risk Management Framework (AI RMF) in real-world environments. Students will develop a structured understanding of how AI systems introduce unique risks—including bias, model drift, adversarial manipulation, model theft, and lack of transparency—and how those risks differ from traditional IT and cybersecurity challenges.\u003c\/p\u003e\r\n\u003cp\u003eThe course explores how to operationalize AI governance by integrating the AI RMF with existing enterprise frameworks, including ISO\/IEC 42001 Artificial Intelligence Management System and NIST Risk Management Framework SP 800-37. Learners will examine how to establish AI system inventories, classify risk, implement controls, and build governance processes that align with regulatory expectations and organizational risk tolerance. The course also introduces emerging high-assurance security concepts for AI systems, including architectural isolation, secure model handling, and advanced threat models inspired by frontier AI environments.\u003c\/p\u003e\r\n\u003cp\u003eThrough hands-on labs using open-source tools, students will assess model bias, evaluate explainability, detect model drift, and simulate real-world AI risk scenarios, including architecture design decisions around system exposure, isolation, and secure deployment models. The course emphasizes not just identifying risks, but implementing measurable controls, producing audit-ready evidence, and enabling continuous monitoring of AI systems in production environments.\u003c\/p\u003e\r\n\u003cp\u003eBy the end of the course, participants will be equipped to design, implement, and operate an AI risk management program that supports secure, compliant, and trustworthy AI adoption across the enterprise.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eAI Risk Management: Implementing the NIST AI RMF Benefits\u003c\/h3\u003e\n\u003cul\u003e\u003cli\u003e\n\u003cp\u003e\u003cb\u003eCourse Benefits\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eApply the NIST AI Risk Management Framework to real-world AI systems\u003c\/li\u003e\n\u003cli\u003eIdentify and classify AI systems and their associated risks\u003c\/li\u003e\n\u003cli\u003eImplement controls to address bias, drift, and adversarial threats\u003c\/li\u003e\n\u003cli\u003eIntegrate AI governance into enterprise risk management programs\u003c\/li\u003e\n\u003cli\u003eMonitor and audit AI systems using practical tools and techniques\u003c\/li\u003e\n\u003cli\u003eEvaluate AI system architectures to reduce exposure and align with high-assurance security principles\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003ePrerequisites\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eAttendees should have foundational knowledge in cybersecurity, risk management, or governance frameworks. Familiarity with machine learning concepts is helpful but not required.\u003c\/p\u003e\n\u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eNIST AI RMF Risk Management Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 1: The AI Risk Landscape\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eAI adoption trends across enterprise and government environments\u003c\/li\u003e\n\u003cli\u003eDifferences between AI systems and traditional software systems\u003c\/li\u003e\n\u003cli\u003eAI system lifecycle: data collection, training, deployment, monitoring\u003c\/li\u003e\n\u003cli\u003eRisk amplification through scale, automation, and data dependency\u003c\/li\u003e\n\u003cli\u003eGenerative AI and large language model (LLM) risk considerations\u003c\/li\u003e\n\u003cli\u003eDecision automation risks and impacts on business processes\u003c\/li\u003e\n\u003cli\u003eEthical, legal, operational, and reputational risk categories\u003c\/li\u003e\n\u003cli\u003eReal-world examples of AI failures and unintended consequences\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 2: The NIST AI Risk Management Framework\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eOverview of AI RMF core functions: Govern, Map, Measure, Manage\u003c\/li\u003e\n\u003cli\u003eEstablishing AI governance structures and accountability models\u003c\/li\u003e\n\u003cli\u003eRisk categorization aligned to business and mission impact\u003c\/li\u003e\n\u003cli\u003eAI system inventory and asset management strategies\u003c\/li\u003e\n\u003cli\u003eRisk measurement techniques and qualitative vs quantitative methods\u003c\/li\u003e\n\u003cli\u003eContinuous monitoring and lifecycle risk management\u003c\/li\u003e\n\u003cli\u003eCommunication of AI risk to stakeholders and leadership\u003c\/li\u003e\n\u003cli\u003eIntegration of AI RMF into existing governance frameworks\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 3: Mapping Traditional RMF to AI Systems\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eAligning NIST Risk Management Framework SP 800-37 with AI RMF\u003c\/li\u003e\n\u003cli\u003eTranslating Prepare, Categorize, Select, Implement, Assess, Authorize, Monitor\u003c\/li\u003e\n\u003cli\u003eCategorizing AI systems based on sensitivity and impact\u003c\/li\u003e\n\u003cli\u003eSelecting controls specific to AI models and data pipelines\u003c\/li\u003e\n\u003cli\u003eImplementing controls across the AI lifecycle\u003c\/li\u003e\n\u003cli\u003eAssessing AI systems for performance, fairness, and security\u003c\/li\u003e\n\u003cli\u003eAuthorization processes for AI deployment\u003c\/li\u003e\n\u003cli\u003eContinuous monitoring and reassessment strategies\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 4: AI Governance and Organizational Controls\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eEstablishing AI governance boards and risk committees\u003c\/li\u003e\n\u003cli\u003eDefining roles and responsibilities across stakeholders\u003c\/li\u003e\n\u003cli\u003eDeveloping AI policies, standards, and procedures\u003c\/li\u003e\n\u003cli\u003eModel lifecycle governance and approval workflows\u003c\/li\u003e\n\u003cli\u003eDocumentation requirements (model cards, data sheets, audit artifacts)\u003c\/li\u003e\n\u003cli\u003eRisk registers and accountability tracking\u003c\/li\u003e\n\u003cli\u003eAligning AI governance with enterprise risk management (ERM)\u003c\/li\u003e\n\u003cli\u003ePreparing for regulatory and compliance requirements\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 5: AI Risk Identification and Control Implementation\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIdentifying AI-specific risks: bias, drift, hallucinations, adversarial threats\u003c\/li\u003e\n\u003cli\u003eData quality and training data risk considerations\u003c\/li\u003e\n\u003cli\u003eModel asset protection, including risks related to model weights and intellectual property\u003c\/li\u003e\n\u003cli\u003eAI supply chain risks including third-party models, datasets, and dependencies\u003c\/li\u003e\n\u003cli\u003eBias detection and mitigation strategies\u003c\/li\u003e\n\u003cli\u003eModel validation and robustness testing\u003c\/li\u003e\n\u003cli\u003eExplainability and interpretability requirements\u003c\/li\u003e\n\u003cli\u003eTechnical controls vs governance controls vs operational controls\u003c\/li\u003e\n\u003cli\u003eControl mapping to risks and measurable outcomes\u003c\/li\u003e\n\u003cli\u003eCreating audit-ready evidence and documentation\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 6: Explainability, Transparency, and Trust\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eImportance of transparency in AI decision-making\u003c\/li\u003e\n\u003cli\u003eBlack-box vs interpretable model trade-offs\u003c\/li\u003e\n\u003cli\u003eFeature importance and decision traceability\u003c\/li\u003e\n\u003cli\u003eExplainability techniques such as SHAP and LIME\u003c\/li\u003e\n\u003cli\u003eCommunicating model behavior to technical and non-technical audiences\u003c\/li\u003e\n\u003cli\u003eSupporting audit, compliance, and legal requirements\u003c\/li\u003e\n\u003cli\u003eBuilding trust with stakeholders and end users\u003c\/li\u003e\n\u003cli\u003eLimitations and risks of explainability methods\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 7: AI Security and Adversarial Risks\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eData poisoning and model poisoning attack vectors\u003c\/li\u003e\n\u003cli\u003eAdversarial machine learning techniques and evasion attacks\u003c\/li\u003e\n\u003cli\u003eModel extraction and inference attacks\u003c\/li\u003e\n\u003cli\u003eModel weight protection and risks associated with model theft and misuse\u003c\/li\u003e\n\u003cli\u003eSecuring AI pipelines, datasets, and training environments\u003c\/li\u003e\n\u003cli\u003eSecure AI architecture patterns including isolation, restricted interfaces, and controlled environments\u003c\/li\u003e\n\u003cli\u003eThreat modeling for AI systems\u003c\/li\u003e\n\u003cli\u003eIntegrating AI risks into existing security operations\u003c\/li\u003e\n\u003cli\u003eDetection and response strategies for AI-specific threats\u003c\/li\u003e\n\u003cli\u003eIntroduction to high-assurance AI security models and emerging practices for protecting sensitive AI systems\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eChapter 8: AI Monitoring, Operations, and ISO 42001 Integration\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eDetecting model drift and data drift in production systems\u003c\/li\u003e\n\u003cli\u003eMonitoring performance degradation and reliability issues\u003c\/li\u003e\n\u003cli\u003eEstablishing retraining triggers and lifecycle management processes\u003c\/li\u003e\n\u003cli\u003eObservability and logging for AI systems\u003c\/li\u003e\n\u003cli\u003eOverview of ISO\/IEC 42001 Artificial Intelligence Management System\u003c\/li\u003e\n\u003cli\u003eAligning AI RMF with ISO 42001 control areas\u003c\/li\u003e\n\u003cli\u003eAI risk maturity models and progression from standard controls to high-assurance environments\u003c\/li\u003e\n\u003cli\u003eEvaluating when increased isolation and restricted architectures are appropriate\u003c\/li\u003e\n\u003cli\u003eContinuous improvement and governance maturity models\u003c\/li\u003e\n\u003cli\u003eBuilding and sustaining an enterprise AI risk management program\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"268D35US \/ 2026-08-05T09:00:00 \/ Herndon, VA","offer_id":51659936268578,"sku":"US-2079-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"269B09CN \/ 2026-09-16T09:00:00 \/ Ottawa","offer_id":51659936301346,"sku":"US-2079-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"26BC93US \/ 2026-11-04T09:00:00 \/ Herndon, VA","offer_id":51659936334114,"sku":"US-2079-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"26CA48CN \/ 2026-12-16T09:00:00 \/ Ottawa","offer_id":51659936366882,"sku":"US-2079-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"272B97US \/ 2027-02-03T09:00:00 \/ Herndon, 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