NIST AI Risk Management Framework Bootcamp
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AI adoption is accelerating across business, security, compliance, and operations, but unmanaged AI can introduce serious risks. This bootcamp helps professionals apply the NIST AI Risk Management Framework to identify, assess, govern, and monitor AI risks using practical templates, risk matrices, profiles, and governance-focused implementation approaches.
Sujay P
25+ Years of ExperienceSujay is a cybersecurity and technology leader with 25+ years of experience in cloud security, enterprise risk management, cybersecurity governance, and AI-driven security. He has implemented SOC centers, SIEM solutions, ISO 42001/27001 programs, cloud security strategies, and large-scale cybersecurity projects, bringing practical expertise to AI risk and governance implementation.
- AI security governance and risk management aligned with ISO 42001 and emerging AI assurance practices
- Cloud security architecture, secure cloud migrations, and hybrid/multi-cloud risk management
- Cybersecurity governance, compliance audits, SOC 2, GDPR, data protection, and enterprise risk management
- SOC implementation, SIEM deployment, security monitoring, and incident response programs
- Threat modeling, vulnerability assessments, penetration testing, and red team/blue team practices
- ISO 27001 implementation, security control design, and governance framework adoption
- AI-driven threat intelligence, security automation, and cloud-native security operations
Special Offer! Limited Time Only
Module 1: Understanding AI Risks in Modern Organizations
- What qualifies as an AI system
- Key risks across AI, GenAI, automation, and decision-support systems
- Business, legal, ethical, privacy, security, and operational risk areas
- Why traditional risk management needs to evolve for AI
Module 2: Responsible and Trustworthy AI Concepts
- Core principles of responsible AI governance
- Fairness, transparency, accountability, privacy, safety, and reliability
- Trustworthy AI characteristics in business contexts
- Common gaps in responsible AI adoption
Module 3: Introduction to the NIST AI Risk Management Framework
- Purpose and structure of the NIST AI RMF
- Core framework components: Functions, Categories, and Profiles
- How the framework supports AI risk and governance programs
- Using NIST AI RMF across AI development, deployment, and monitoring
Module 4: GOVERN Function: Building AI Governance Foundations
- AI governance roles, responsibilities, and accountability
- Policies, oversight mechanisms, and approval workflows
- Aligning AI governance with business objectives and risk appetite
- Governance for internal and third-party AI systems
Module 5: MAP Function: Understanding AI Context & Impact
- Identifying AI system purpose, users, data, and environment
- Mapping stakeholders, dependencies, and impacted groups
- Assessing business, process, data, and outcome-level impact
- Documenting assumptions, limitations, and risk context
Module 6: MEASURE Function: Assessing AI Risks
- Defining measurable AI risk indicators
- Evaluating bias, accuracy, reliability, privacy, and security concerns
- Using risk scoring and assessment matrices
- Linking measurement results to governance decisions
Module 7: MANAGE Function: Treating and Monitoring AI Risks
- Prioritizing AI risks based on likelihood, impact, and business criticality
- Selecting mitigation actions and controls
- Monitoring AI systems after deployment
- Escalating and communicating high-impact AI risks
Module 8: Creating NIST AI RMF Profiles
- Understanding current-state and target-state AI RMF Profiles
- Using profiles to prioritize governance improvements
- Applying profiles across AI use cases and maturity levels
- Translating profiles into an implementation roadmap
Module 9: Applying NIST AI RMF to Real-World AI Use Cases
- AI risk considerations for enterprise and GenAI tools
- Governance challenges in vendor-provided AI systems
- Lessons from AI adoption and governance failures
- Preparing for audits, assurance, and regulatory expectations
Module 10: Bootcamp Wrap-Up & AI Governance Action Plan
- Key lessons from the NIST AI RMF
- Common AI governance implementation challenges
- Building a practical AI risk and governance roadmap
- Q&A and guided discussion
*Note: Participants will have access to session recordings for a period of 60 days.
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