Operationalise the NIST AI RMF at runtime
The NIST AI RMF gives US enterprises a common language for AI risk. WalledAI is the runtime layer that turns Govern, Map, Measure, and Manage from a document into enforced controls, telemetry, and audit-grade evidence - across every LLM your teams use.
What is the NIST AI Risk Management Framework?
Published by the US National Institute of Standards and Technology in January 2023 (AI RMF 1.0), the framework is a voluntary, sector-agnostic guide for managing the risks of designing, developing, deploying, and using AI systems. It is rapidly becoming the de facto reference for US federal agencies, contractors, and enterprises building AI governance programmes - and it maps cleanly to ISO/IEC 42001, the EU AI Act, and SOC 2.
The framework is organised into four core functions - Govern, Map, Measure, and Manage - each with concrete categories and subcategories. Adoption is a process, not a project: it requires policies, telemetry, controls, and evidence that keep pace with your AI footprint.
Four core functions. One runtime control plane.
WalledAI operationalises each NIST AI RMF function with enforced controls and audit-grade telemetry.
Govern
Establish the culture, policies, roles, and accountability structures that make AI risk management an organisation-wide discipline - not a project.
WalledAI controls:
- Policy library to upload, version, and enforce AI acceptable-use and risk policies
- Enterprise RBAC maps roles, responsibilities, and approval chains to every AI interaction
- Audit logs give the board defensible evidence that AI governance is operating as designed
- Data classification enforces sensitivity-based handling across every LLM your teams touch
Map
Understand the context in which each AI system operates - the users, data, downstream impacts, and specific risks it introduces.
WalledAI controls:
- Interaction-level telemetry surfaces which teams use which models for which data types
- Automated data classification flags sensitive data flowing into any AI surface
- Model and vendor inventory across ChatGPT, Copilot, Claude, Gemini, and self-hosted LLMs
- Risk mapping for high-impact use cases: customer decisions, code generation, agentic actions
Measure
Analyse, benchmark, and monitor AI risks using quantitative and qualitative techniques so decisions rest on evidence, not intuition.
WalledAI controls:
- Walled Correct scores hallucination rates against your ground truth in real time
- Walled Protect measures prompt injection and jailbreak attempt volume and block rate
- Walled Redact reports PII exposure prevented, per model, per team, per time window
- Governance Dashboard trends KPIs auditors and regulators expect to see over time
Manage
Prioritise, respond to, and communicate about AI risks - allocating resources and controls to the highest-impact issues.
WalledAI controls:
- Runtime blocking of prompts and outputs that violate policy - before harm is done
- Configurable escalation and human-in-the-loop for low-confidence or high-risk interactions
- SIEM and GRC integrations (Splunk, Sentinel, Archer, ServiceNow) for incident response
- Continuous policy tuning based on incident telemetry and evolving regulatory expectations
Turn NIST AI RMF from framework into enforced controls
Our team will map WalledAI capabilities to your Govern, Map, Measure, and Manage priorities.
Frequently Asked Questions
Authoritative references
Primary sources behind the standards, regulations and research referenced on this page.
- NIST AI Risk Management FrameworkUS framework for governing, mapping, measuring and managing AI risk.
- NIST AI RMF PlaybookImplementation guidance for AI RMF functions and controls.
- NIST AI 600-1 Generative AI ProfileGenerative-AI-specific risks and suggested actions from NIST.
- ISO/IEC 42001:2023 AI management systemsFirst certifiable international standard for AI management.