NIST GenAI Profile – Generative AI-Specific Risks and Treatments Training by Tonex
NIST GenAI Profile – Generative AI-Specific Risks and Treatments Training by Tonex provides professionals with a structured understanding of Generative Artificial Intelligence (GenAI) risks and risk treatments based on the NIST AI 600-1 Generative AI Profile. Participants examine risk identification, governance, measurement, monitoring, and treatment approaches across the GenAI lifecycle while connecting these practices to the NIST AI Risk Management Framework.
The course emphasizes cybersecurity implications including information security threats, prompt injection, data poisoning, phishing, malicious code generation, sensitive information exposure, and emerging attack surfaces created by GenAI adoption. Participants learn how effective GenAI governance and security controls can strengthen organizational cybersecurity resilience while reducing misuse and operational risk.
A practical training approach includes exercises, real-world case studies, and examples of processes and documentation used in Generative AI risk management projects.
Learning Objectives
Upon completion of this course, participants will be able to:
- Explain the purpose, structure, and organizational application of the NIST Generative AI Profile.
- Identify GenAI-specific and GenAI-amplified risks across development, deployment, operation, and third-party use.
- Apply the NIST Govern, Map, Measure, and Manage functions to Generative AI risk decisions.
- Evaluate risks involving confabulation, information integrity, privacy, harmful content, intellectual property, and human-AI interaction.
- Develop practical GenAI risk treatments using policies, controls, assessments, monitoring practices, and documented risk decisions.
- Assess third-party models, datasets, services, components, and dependencies for value-chain and integration risks.
- Strengthen cybersecurity by identifying GenAI-enabled threats, vulnerable AI components, sensitive data exposure, and appropriate security controls.
- Establish measurable criteria for monitoring GenAI performance, safety, trustworthiness, resilience, and residual risk.
Audience
This course is designed for:
- AI Governance and Responsible AI Professionals
- Cybersecurity Professionals
- AI Risk Management Professionals
- Information Security and Technology Leaders
- Governance, Risk, and Compliance Professionals
- Enterprise Risk Management Teams
- AI Product and Program Managers
- Data Governance and Privacy Professionals
- Internal Audit and Assurance Teams
- Legal, Policy, and Compliance Professionals
- Technology Procurement and Vendor Risk Teams
- Professionals responsible for adopting or overseeing Generative AI systems
Course Modules
Module 1: NIST GenAI Profile Foundations
- Purpose and scope of NIST AI 600-1 and its relationship to the NIST AI Risk Management Framework.
- Characteristics of Generative AI that introduce or amplify organizational, technical, societal, and operational risks.
- Roles and responsibilities of AI actors involved in developing, acquiring, deploying, evaluating, and overseeing GenAI systems.
- Application of the Govern, Map, Measure, and Manage functions to Generative AI risk management.
- Establishing organizational context, risk tolerance, accountability, and decision-making structures for GenAI adoption.
- Connecting GenAI risk management activities with existing enterprise governance, compliance, security, and assurance processes.
Module 2: GenAI Risk Landscape
- Identification of chemical, biological, radiological, and nuclear information risks associated with expanded access to dangerous knowledge.
- Understanding confabulation and the operational consequences of confidently generated inaccurate, misleading, or unsupported information.
- Recognition of dangerous, violent, abusive, degrading, or otherwise harmful Generative AI outputs.
- Assessment of toxicity, bias, homogenization, and inappropriate patterns that may affect individuals, groups, and organizational decisions.
- Evaluation of environmental impacts and resource considerations associated with developing and operating Generative AI capabilities.
- Prioritizing GenAI risks according to likelihood, severity, context of use, affected stakeholders, and organizational risk tolerance.
Module 3: Privacy Integrity and Trust
- Assessing personally identifiable, confidential, proprietary, biometric, and sensitive information exposure within GenAI inputs and outputs.
- Identifying privacy risks involving memorization, inference, unauthorized disclosure, de-anonymization, retention, and inappropriate secondary use.
- Evaluating misinformation, disinformation, manipulated content, synthetic media, provenance, and other information integrity concerns.
- Managing intellectual property considerations involving copyrighted material, proprietary information, licensing, attribution, and generated content.
- Establishing transparency, disclosure, documentation, and traceability expectations appropriate to organizational GenAI use cases.
- Defining validation, review, provenance, and human oversight practices that support trustworthy information and responsible decision-making.
Module 4: GenAI Cybersecurity Risk Treatments
- Identifying cybersecurity threats involving prompt injection, data poisoning, unauthorized access, malicious inputs, compromised dependencies, and vulnerable integrations.
- Assessing how Generative AI may lower barriers for phishing, social engineering, malware development, exploitation, and other offensive cyber activities.
- Protecting GenAI data, models, applications, pipelines, interfaces, plugins, credentials, and supporting infrastructure against unauthorized manipulation.
- Applying access controls, secure configuration, monitoring, filtering, validation, logging, and other risk treatments appropriate to GenAI environments.
- Integrating GenAI security concerns into established vulnerability management, incident response, secure development, and enterprise cybersecurity processes.
- Defining escalation, containment, remediation, fallback, suspension, and deactivation procedures when unacceptable GenAI security risks are detected.
Module 5: Measurement Monitoring and Evaluation
- Establishing meaningful metrics, indicators, thresholds, and acceptance criteria for GenAI reliability, validity, safety, security, and trustworthiness.
- Measuring erroneous outputs, harmful content, information integrity issues, privacy concerns, security weaknesses, and other identified GenAI risks.
- Evaluating system behavior under conditions representative of intended users, operational environments, and organizational contexts.
- Reviewing generated content, claims, references, code, outputs, and supporting evidence to identify reliability and safety concerns.
- Documenting assessment methods, assumptions, limitations, results, residual risks, exceptions, and management decisions for organizational accountability.
- Establishing continuous monitoring practices to identify changing risk conditions, emerging vulnerabilities, incidents, and deteriorating GenAI performance.
Module 6: Governance and Risk Treatments
- Developing GenAI policies, procedures, control requirements, approval criteria, accountability structures, and documented risk acceptance processes.
- Applying organizational risk treatments to internal models, external services, third-party datasets, fine-tuned models, and integrated GenAI components.
- Performing supplier, procurement, dependency, and value-chain reviews covering security, privacy, intellectual property, provenance, and operational concerns.
- Establishing human oversight, review, escalation, incident reporting, responsible disclosure, and remediation processes for high-impact GenAI applications.
- Maintaining documentation including risk registers, impact assessments, evaluation records, control evidence, model information, incident records, and management decisions.
- Integrating lessons learned, changing threat conditions, new use cases, stakeholder feedback, and monitoring results into ongoing GenAI risk treatment activities.
Build the expertise to identify, evaluate, and treat emerging Generative AI risks using a structured NIST-based approach. Enroll in NIST GenAI Profile – Generative AI-Specific Risks and Treatments Training by Tonex and strengthen your organization’s AI governance, cybersecurity, risk management, and assurance capabilities.