In 2025, every serious AI organization claims to be building "Trustworthy and Responsible AI." The NIST AI Risk Management Framework (RMF) is the playbook that separates talk from action.
The RMF is not a simple checklist you "pass." It is a continuous, lifecycle-based framework designed to Govern, Map, Measure, and Manage the unique risks of AI.
While the framework is voluntary, it is the new standard of care. In the event of an AI-driven incident, your RMF alignment will be the first thing auditors and courts ask for.
You cannot manually Map, Measure, and Manage the complex, high-speed risks of modern AI. You need a dedicated, continuous monitoring solution.
Our platform is purpose-built to operationalize the NIST AI RMF. We provide the technical evidence, real-time metrics, and automated controls you need to move from theory to practice.
Ground Truth for Oversight
True Risk Context
Quantification Engine
Real-Time Response
The Govern function is the foundation. It requires you to establish a risk-aware culture and accountability.
Your leadership (C-suite, board) cannot govern what it cannot see. They need a clear, non-technical view of the organization's AI risk posture.
Our platform provides a centralized, executive-level dashboard that translates complex model vulnerabilities into clear business risks.
We provide the "ground truth" on model integrity and security, giving your leadership the data they need to make informed risk management decisions and establish effective governance.
The Map function requires you to identify your risks and understand your AI's context.
Your "context" includes a rapidly evolving, invisible threat landscape. You must "map" not just your own systems but the novel, external attacks being developed against them, such as data poisoning and model evasion.
We act as your external intelligence service. Our platform continuously maps the threat landscape, identifying new adversarial techniques and data poisoning methods.
We help you categorize your AI systems based on their actual vulnerabilities to these known, real-world attack vectors.
The Measure function is where you "evaluate, assess, and benchmark" AI risks. This is the most difficult part of the RMF to do internally.
How do you "measure" your model's robustness against an unknown, adversarial attack? How do you "test" your training data for subtle, malicious poisoning?
Data Integrity
We scan and validate your training, testing, and validation datasets to quantify their integrity and detect signs of data poisoning.
Model Robustness
We run continuous, automated adversarial testing (a form of "red-teaming") against your models to measure their resilience to prompt injection, evasion attacks, and other manipulations.
Trust Benchmarking
We provide consistent metrics on your model's security and robustness over time, creating the audit trail you need to prove your AI is trustworthy.
The Manage function is about acting on the risks you've measured.
An AI attack can happen in milliseconds. You cannot rely on a quarterly manual audit. You must detect and respond to AI-specific incidents in real-time.
When our "Measure" function detects a live threat—like a real-time prompt injection attack or a malicious query pattern indicating model theft—we trigger an immediate alert.
This moves "Manage" from a passive, after-the-fact process to an active, real-time defense, enabling your team to respond before a catastrophic failure or data breach occurs.
You cannot "achieve" RMF compliance and be done. The framework demands continuous, ongoing risk management.
Manually testing your models is like manually checking your network firewall for vulnerabilities once a quarter. It's a compliance gap that attackers will exploit.
Our platform provides the automated, continuous monitoring required to make the NIST AI RMF a living, effective part of your organization—transforming "Trustworthy AI" from a marketing promise into a provable, technical reality.
Operationalize the NIST AI Risk Management Framework with continuous monitoring, automated controls, and real-time threat detection. Move from buzzwords to provable, technical excellence.
NIST AI RMF Overview: Released in January 2023, the NIST AI Risk Management Framework provides a voluntary, flexible framework for managing risks to individuals, organizations, and society associated with AI. It's organized around four core functions: Govern, Map, Measure, and Manage.
GOVERN Function: Establishes and nurtures a culture of AI risk management. This includes leadership commitment, clear policies, stakeholder engagement, and accountability structures. It's the foundation that enables the other three functions to work effectively.
MAP Function: Identifies and documents AI risks in context. This includes understanding your AI systems, their intended use, potential impacts, and the broader threat landscape. Mapping is about situational awareness—you can't manage risks you haven't identified.
MEASURE Function: Quantifies and evaluates identified AI risks. This includes testing, validation, benchmarking, and monitoring. The Measure function transforms abstract risks into concrete data that can guide decisions.
MANAGE Function: Takes action based on measured risks. This includes implementing controls, responding to incidents, and continuously improving. The Manage function is where risk management becomes operational.
Why Continuous Monitoring Matters: AI systems operate in dynamic environments with evolving threats. The RMF explicitly recognizes that AI risk management is a continuous lifecycle, not a one-time assessment. Manual, periodic reviews cannot keep pace with the speed of AI deployment and the sophistication of AI-specific attacks.