AI Risk Management Framework
HRMLESS has adopted the NIST AI Risk Management Framework (AI RMF 1.0) as the foundation for governing AI systems. The framework provides a structured, risk-based approach to identifying, assessing, and managing AI risks across the full lifecycle of our AI-powered products.
Govern
Establish policies, roles, and accountability structures that embed AI risk management into organizational decision-making.
Key Activities
- AI Governance Policy defining organizational risk tolerance and acceptable use boundaries
- Designated AI governance roles: AI Risk Owner, Model Owner, and Human Oversight Lead
- Quarterly AI governance reviews with executive leadership
- AI system inventory maintained with risk-tier classifications (Critical, High, Medium, Low)
- Third-party AI vendor assessment requirements integrated into procurement process
- Incident response procedures specific to AI failures, bias events, and adversarial attacks
Related Artifacts
- AI Governance Policy
- Acceptable AI Use Policy
- AI Vendor Assessment Template
- AI Incident Response Playbook
Map
Identify and characterize AI systems, their contexts of use, stakeholders, and potential impacts to inform risk assessment.
Key Activities
- AI system cataloging with purpose, data inputs, outputs, and downstream dependencies
- Stakeholder impact analysis for each AI system including candidates, employers, and internal users
- Data lineage documentation covering training data sources, preprocessing, and retention
- Intended use and known limitation documentation published as Model Cards
- Regulatory applicability mapping across U.S. state AI employment laws
- Context-of-use analysis to identify where AI decisions affect individual rights or opportunities
Related Artifacts
- AI System Inventory
- Model Cards (per system)
- Data Lineage Documentation
- Regulatory Applicability Map
Measure
Quantify AI risks through metrics, testing, and monitoring to provide evidence for risk management decisions.
Key Activities
- Bias testing across protected characteristics using demographic parity and equalized odds metrics
- Performance monitoring with drift detection for model accuracy and data distribution shifts
- Fairness metric baselines established before deployment with ongoing regression testing
- Red-team exercises for adversarial robustness including prompt injection and data poisoning scenarios
- Explainability assessments to ensure AI outputs can be interpreted by human reviewers
- Third-party bias audit readiness for jurisdictions requiring independent assessment
Related Artifacts
- Bias Testing Reports
- Model Performance Dashboards
- Fairness Metric Baselines
- Red Team Exercise Reports
Manage
Prioritize and act on identified AI risks through mitigation, monitoring, and continuous improvement processes.
Key Activities
- Risk treatment decisions documented for each identified AI risk (mitigate, accept, transfer, avoid)
- Human-in-the-loop controls enforced for high-risk AI decisions affecting employment outcomes
- Model rollback procedures and circuit breakers for rapid response to detected issues
- Continuous monitoring alerts for bias drift, performance degradation, and anomalous outputs
- Quarterly AI risk register reviews with treatment plan updates
- Lessons-learned integration from incidents and near-misses into governance improvements
Related Artifacts
- AI Risk Register
- Human Oversight Procedures
- Model Rollback Runbooks
- Quarterly Review Reports
NIST Trustworthiness Characteristics
Our AI governance program addresses all seven trustworthiness characteristics defined by the NIST AI RMF.
Valid & Reliable
AI systems produce accurate, consistent outputs that perform as intended across expected conditions.
Safe
AI systems do not endanger human life, health, property, or the environment under normal or foreseeable conditions.
Secure & Resilient
AI systems withstand adversarial attacks, unexpected inputs, and operational disruptions.
Accountable & Transparent
AI decision processes are documented, auditable, and communicated to stakeholders.
Explainable & Interpretable
AI outputs can be understood by human reviewers in context, with reasoning that can be articulated.
Privacy-Enhanced
AI systems protect individual privacy through data minimization, anonymization, and purpose limitation.
Fair with Harmful Bias Managed
AI systems are tested and monitored for bias, with active mitigation of discriminatory outcomes.
SOC 2 Crosswalk
Our NIST AI RMF controls are mapped to SOC 2 Trust Services Criteria, ensuring AI governance activities contribute directly to our compliance posture.