NIST AI RMF

The Seven Characteristics of Trustworthy AI

NIST AI RMF defines trustworthy AI by seven characteristics. Here is what each means, and where VIDIZMO has concrete evidence behind it today:

• Valid and reliable: the system performs as intended, consistently, across the conditions it is meant to operate in.
• Safe: the system does not endanger human life, health, property, or the environment.
• Secure and resilient: the system withstands adverse events and keeps operating. VIDIZMO backs this with platform-wide encryption, access control, and incident response, the same practices covered elsewhere in this Trust Center.
• Accountable and transparent: the organization behind the system is answerable for how it is built and used. VIDIZMO’s published Responsible AI Policy and model inventory, the “bill of models,” provide that visibility.
• Explainable and interpretable: outputs can be understood in the context of the system’s design and intended use.
• Privacy-enhanced: the system respects how data is collected, used, and retained. VIDIZMO does not train models on customer data without explicit written authorization.
• Fair, with harmful bias managed: the system avoids unjust or systematically unfavorable treatment of individuals or groups.

What NIST AI RMF Majorly Requires, and How VIDIZMO Supports It

NIST AI RMF is organized around four functions, each broken into more specific categories. They are meant to work together on an ongoing basis, not as a one-time checklist:

• Govern: builds a culture of AI risk management across the organization, and cuts across the other three functions rather than sitting apart from them.
  • Policies, processes, and practices for AI use, and how transparently they are enforced
  • Accountability structures: defined roles, responsibilities, and reporting lines for AI decisions
  • Documented organizational risk tolerance for AI use
  • Workforce culture: diversity, equity, and risk-awareness across the teams building and operating AI
  • Processes for managing third-party and supply chain AI risk
  VIDIZMO’s published Responsible AI Policy sets that accountability structure, covering fairness, accountability, transparency, and safety.
• Map: establishes the context for a specific AI system before it is assessed further.
  • The context: what the system is intended to do, and the business purpose behind it
  • Who uses the system and how, including the specific use case it is deployed for
  • What could realistically go wrong for the people affected by that use case
  This is reflected in VIDIZMO’s model inventory, the “bill of models,” available under NDA.
• Measure: analyzes and monitors AI risk on an ongoing basis, not just at launch.
  • Quantitative methods: measurable metrics tested against defined risk thresholds
  • Qualitative methods: structured review and expert judgment where behavior is not easily reduced to a number
  • Tracking identified risks over time, not just at a single point of testing
  VIDIZMO applies the same secure development lifecycle to AI features as the rest of the platform: threat modeling, static and dynamic application security testing, and independent penetration testing.
• Manage: allocates resources to the risks identified through Map and Measure.
  • Deciding whether to mitigate, transfer, avoid, or accept each identified risk
  • Planning and implementing responses that maximize benefit and minimize harm
  • Monitoring risk treatments after deployment, not only before launch
  VIDIZMO does not train models on customer data by default, only with explicit written authorization, and AI-driven features are covered by the same incident response process as the rest of the platform.

Scope & Limitations

NIST AI RMF is voluntary: there is no certification or independent audit to hold against it. What is described above is how VIDIZMO's existing practices map to the framework's structure, not a formal attestation. VIDIZMO's documented evidence maps most directly to the secure and resilient, accountable and transparent, and privacy-enhanced characteristics.