NIST AI Risk Management Framework Workshop
Apply GOVERN, MAP, MEASURE and MANAGE to real enterprise AI
Workshop Objective
AI adoption introduces new risks across cybersecurity, privacy, fairness, reliability, legal compliance, operations and organisational reputation. Traditional IT risk management approaches may not fully address the unique challenges associated with AI systems.
This workshop provides a structured approach to understanding and applying the NIST AI Risk Management Framework through its four core functions: GOVERN, MAP, MEASURE and MANAGE. Participants will explore practical enterprise scenarios, evaluate AI risk management gaps and learn how to develop risk documentation, AI RMF Profiles and prioritised implementation roadmaps.
Workshop Highlights
Note: CPE credits apply only to participants attending the live instructor-led sessions. Recording access does not qualify for CPE credits.
Workshop Coverage
NIST AI Risk Management Framework Workshop
Day 1: AI Risk Foundations, Governance & Context Mapping
Understanding AI Risk in the Enterprise
- Identifying AI systems across traditional AI, Generative AI, automation and intelligent decision-making
- Understanding how AI introduces new business, operational and technology risks
- Exploring privacy, cybersecurity, legal, ethical and reputational risk scenarios
- Examining the limitations of conventional IT and enterprise risk management approaches when applied to AI
Principles of Responsible and Trustworthy AI
- Establishing the foundations of responsible AI adoption
- Evaluating fairness, transparency, explainability, accountability, safety and reliability
- Understanding the relationship between AI trustworthiness and business outcomes
- Identifying common weaknesses in organisational AI governance and oversight
Navigating the NIST AI RMF
- Understanding the purpose, scope and practical application of NIST AI RMF 1.0
- Exploring the framework's structure: Functions, Categories, Subcategories and Profiles
- Understanding how the framework supports risk-based AI governance throughout the system lifecycle
- Mapping organisational AI risk management needs to the NIST AI RMF structure
GOVERN: Establishing AI Governance and Accountability
- Defining AI governance structures, ownership and decision-making authority
- Developing AI policies, approval processes and oversight mechanisms
- Aligning AI risk management with organisational objectives, risk appetite and accountability
- Establishing governance requirements for internally developed and externally sourced AI systems
MAP: Defining AI Context, Dependencies and Potential Impact
- Profiling AI systems based on their purpose, capabilities, intended users and operating environment
- Mapping stakeholders, data flows, third-party dependencies and affected populations
- Evaluating potential impacts on business processes, individuals and organisational operations
- Documenting system assumptions, limitations, intended use and foreseeable misuse
Day 2: AI Risk Assessment, Mitigation & Enterprise Implementation
MEASURE: Evaluating AI Risk and Trustworthiness
- Establishing risk indicators, evaluation criteria and measurable performance thresholds
- Assessing bias, accuracy, robustness, privacy, security and reliability
- Applying qualitative and quantitative risk assessment techniques
- Interpreting assessment results to support risk decisions and governance priorities
- Understanding testing, evaluation, verification and validation (TEVV) in AI risk management
MANAGE: Risk Treatment, Response and Continuous Monitoring
- Prioritising AI risks according to severity, likelihood, business impact and risk tolerance
- Selecting appropriate risk treatment strategies, safeguards and mitigation measures
- Defining risk ownership, escalation procedures and residual risk acceptance
- Establishing post-deployment monitoring, incident handling and corrective action processes
- Reviewing AI systems when data, models, operating conditions or intended use change
AI RMF Profiles: From Current-State Assessment to Target-State Planning
- Understanding Current Profile and Target Profile development
- Assessing existing AI risk management practices against desired outcomes
- Identifying gaps and prioritising improvements across the four core functions
- Adapting Profiles to different AI use cases, organisational needs and risk levels
- Translating assessment findings into a prioritised AI governance implementation roadmap
Applying the Framework to Enterprise and Generative AI
- Assessing risks associated with enterprise AI assistants, LLMs and AI-enabled business applications
- Addressing model-related risks, sensitive data exposure, unreliable outputs and inappropriate use
- Evaluating third-party AI providers, external models and AI supply-chain dependencies
- Examining practical AI risk scenarios and identifying governance failures and control gaps
- Preparing risk documentation and evidence to support internal assurance and applicable regulatory obligations
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
Who Should Attend
Suitable for professionals involved in AI adoption, risk oversight, technology management and organisational governance.
Why Choose This Workshop?
Practical Framework Application
Learn to apply the NIST AI Risk Management Framework (AI RMF 1.0) to real-world organisational AI use cases.
End-to-End AI Risk Management
Understand how to identify, assess, prioritise and mitigate AI risks throughout the system lifecycle.
Hands-On Risk Assessment
Develop an understanding of AI risk registers, evaluation criteria, mitigation strategies and AI RMF Profiles.
Expert-Led Sessions
Gain insights from an experienced cybersecurity professional with over 10 years of industry experience.
Actionable Takeaways
Build a practical foundation for integrating AI risk management into existing organisational governance and security practices.
Real-World Enterprise Scenarios
Examine practical AI and Generative AI risk scenarios, including sensitive data exposure, unreliable outputs and third-party dependencies.
Prerequisites
Trainer Details
MNMadhusmita Nayak
Cybersecurity professional with 10+ years of experience in threat and vulnerability management, risk management, and program management. Specialises in strengthening organisational security, identifying and mitigating cyber risks, and developing practical, risk-aligned security strategies.
Committed to enhancing organisational resilience through effective security controls, collaborative problem-solving, and strategic risk management. Actively contributes to the cybersecurity community by supporting knowledge-sharing, professional collaboration, and greater participation in the field.
Connect on LinkedIn →Frequently Asked Questions
Will I receive a certificate upon completion?
Yes, all participants who successfully complete the workshop will receive a Certificate of Completion from CyberFrat.
Will I get access to recordings?
Yes, all registered participants will get access to session recordings for one year.
Can this Workshop help me earn CPE credits?
Yes, CPE credits will be applicable only for participants attending the live instructor-led workshop, subject to the requirements of their respective certification body.
Are there any prerequisites?
This is an AI risk management and governance workshop, not a programming course. Advanced coding, machine learning development or mathematical expertise is not a prerequisite. However, prior exposure to AI systems and basic enterprise risk concepts will help participants gain greater value from the sessions.
Ready to Govern AI Risk?
Registration details for the 5th & 6th December 2026 workshop.
NIST AI RMF Workshop
Two live days on Zoom, 5th & 6th December 2026, 10:00 AM - 4:00 PM IST.
- 10 hours live (2 days × 5 hours, excluding breaks)
- 10 CPE credits
- Certificate of Completion & 1 year recording access
NIST AI RMF Workshop
Two live days on Zoom, 5th & 6th December 2026, 10:00 AM - 4:00 PM IST.
- 10 hours live (2 days × 5 hours, excluding breaks)
- 10 CPE credits
- Certificate of Completion & 1 year recording access