Home » Courses » IT and Technology Courses » Artificial Intelligence (AI), Machine Learning (ML), GenAI/LLM and Agentic AI » AI Risk Management & Compliance » NIST TEVV Guidance – AI Testing and Assurance

NIST TEVV Guidance – AI Testing and Assurance

Length: 2 Days
Print Friendly, PDF & Email

NIST TEVV Guidance – AI Testing and Assurance Training by Tonex

NIST TEVV Guidance - AI Testing and Assurance

NIST TEVV Guidance – AI Testing and Assurance Training by Tonex provides professionals with a structured understanding of Testing, Evaluation, Verification, and Validation (TEVV) for Artificial Intelligence systems. TEVV refers to the processes used to test system behavior, evaluate performance, verify that requirements have been implemented correctly, and validate that a system performs effectively for its intended purpose. The course introduces guidance and assurance concepts associated with the National Institute of Standards and Technology (NIST), including trustworthy Artificial Intelligence, risk assessment, performance measurement, evidence development, and lifecycle assurance.

Strong Testing, Evaluation, Verification, and Validation practices also have an important cybersecurity impact. They help organizations identify security weaknesses, adversarial vulnerabilities, data integrity concerns, and unsafe Artificial Intelligence behavior. Effective assurance provides cybersecurity teams with documented evidence that Artificial Intelligence systems remain reliable and secure under expected, unexpected, and potentially hostile conditions.

Learning Objectives

Upon completion of this course, participants will be able to:

  • Explain Testing, Evaluation, Verification, and Validation and its role throughout the Artificial Intelligence system lifecycle.
  • Interpret National Institute of Standards and Technology concepts supporting trustworthy and reliable Artificial Intelligence assurance.
  • Establish measurable requirements, acceptance criteria, test objectives, and evaluation metrics for Artificial Intelligence systems.
  • Develop risk-informed testing and assurance strategies addressing reliability, robustness, transparency, safety, and operational suitability.
  • Evaluate evidence generated through testing, verification, validation, independent review, and continuous monitoring activities.
  • Apply Testing, Evaluation, Verification, and Validation practices to strengthen cybersecurity by identifying vulnerabilities, adversarial exposure, data integrity risks, and security control weaknesses.
  • Develop traceable documentation supporting technical reviews, governance decisions, assurance reporting, and ongoing Artificial Intelligence risk management.

Audience

This course is designed for:

  • Artificial Intelligence Engineers
  • Artificial Intelligence System Developers
  • Artificial Intelligence Testing Professionals
  • Artificial Intelligence Assurance Professionals
  • Cybersecurity Professionals
  • Artificial Intelligence Governance Professionals
  • Risk Management Professionals
  • Systems Engineers
  • Technical Leads
  • Software Quality Professionals
  • Verification and Validation Professionals
  • Data Scientists
  • Compliance Professionals
  • Internal Auditors
  • Technology Program Managers
  • Government and Defense Technology Professionals
  • Artificial Intelligence Acquisition Professionals
  • Procurement Professionals
  • Enterprise Risk Management Professionals

Course Modules

The course uses a practical training approach that includes exercises, real-world case studies, and examples of processes and documentation used in Artificial Intelligence Testing, Evaluation, Verification, and Validation projects.

Module 1: Foundations of Artificial Intelligence TEVV

  • Meaning and purpose of Testing, Evaluation, Verification, and Validation
  • Testing, Evaluation, Verification, and Validation across the Artificial Intelligence lifecycle
  • National Institute of Standards and Technology trustworthy Artificial Intelligence concepts
  • Differences among testing, evaluation, verification, and validation activities
  • Assurance objectives for Artificial Intelligence-enabled systems and applications
  • Roles, responsibilities, independence, and stakeholder accountability

Module 2: Requirements and Assurance Planning

  • Defining measurable Artificial Intelligence system requirements and expectations
  • Translating stakeholder needs into testable assurance criteria
  • Establishing testing objectives, scope, assumptions, and constraints
  • Creating acceptance thresholds and measurable performance indicators
  • Identifying dependencies, operating conditions, and intended system uses
  • Maintaining requirements traceability throughout assurance activities

Module 3: Artificial Intelligence Testing and Evaluation

  • Developing structured testing strategies for Artificial Intelligence systems
  • Selecting appropriate datasets and representative evaluation conditions
  • Measuring accuracy, reliability, robustness, and operational performance
  • Evaluating system behavior under expected and unexpected conditions
  • Identifying edge cases, limitations, failures, and performance degradation
  • Documenting test results, observations, deviations, and supporting evidence

Module 4: Verification and Validation Practices

  • Verifying implementation against documented technical requirements
  • Validating system behavior against intended operational objectives
  • Establishing independent review and evidence assessment practices
  • Evaluating system assumptions, constraints, and decision boundaries
  • Managing discrepancies, corrective actions, and revalidation activities
  • Building defensible verification and validation evidence packages

Module 5: Risk and Security Assurance

  • Applying risk-informed approaches to Artificial Intelligence assurance planning
  • Evaluating cybersecurity threats affecting Artificial Intelligence-enabled systems
  • Assessing adversarial inputs, manipulation, and data integrity risks
  • Examining robustness under abnormal and hostile operating conditions
  • Connecting assurance findings with cybersecurity and risk controls
  • Prioritizing remediation using impact, likelihood, and assurance evidence

Module 6: Governance and Assurance Documentation

  • Establishing repeatable organizational assurance governance processes
  • Creating test plans, evaluation reports, and assurance records
  • Maintaining evidence traceability for technical reviews and approvals
  • Communicating technical findings to decision-makers and stakeholders
  • Supporting continuous monitoring and post-deployment reassessment
  • Integrating assurance results into enterprise Artificial Intelligence governance

Develop the knowledge and methods needed to evaluate, verify, validate, and assure Artificial Intelligence systems using structured National Institute of Standards and Technology-aligned practices. NIST TEVV Guidance – AI Testing and Assurance Training by Tonex helps professionals build trustworthy, reliable, secure, and evidence-driven Artificial Intelligence assurance programs.

Request More Information