AI Governance for Regulated Industries
Build trust in your AI systems with governance frameworks that satisfy regulators, protect patients and customers, and stand up to audit. Aligned to EU AI Act, NIST AI RMF, ISO 42001, and FDA AI/ML guidance.
Framework Coverage
We build governance programs mapped to the standards your regulators and auditors expect.
EU AI Act
Full compliance mapping for the EU AI Act risk classification system. We identify which of your AI systems fall under high-risk categories, build conformity assessment documentation, and establish the technical requirements for transparency, human oversight, and data governance.
NIST AI RMF
Implementation of the NIST AI Risk Management Framework across all four core functions: Govern, Map, Measure, and Manage. We build organizational AI risk profiles, define risk tolerances, and create measurement protocols tied to your business context.
ISO 42001
Readiness assessment and implementation support for the ISO 42001 AI Management System standard. Gap analysis against certification requirements, policy development, and internal audit preparation for organizations pursuing formal certification.
FDA AI/ML Guidance
Governance programs aligned with FDA guidance on AI/ML-based Software as a Medical Device (SaMD). Predetermined change control plans, real-world performance monitoring, and Good Machine Learning Practice (GMLP) documentation.
What We Deliver
Concrete governance capabilities, not slide decks. Every deliverable is designed to satisfy auditors and regulators.
Model Risk Management
Structured risk assessment for every AI model in production. Risk scoring, validation protocols, and ongoing monitoring thresholds tailored to regulated environments.
Bias Testing & Fairness Audits
Statistical testing for demographic bias, disparate impact analysis, and fairness metric dashboards. Documented testing methodology that satisfies regulatory scrutiny.
Explainability & Transparency
Model-agnostic explainability methods including SHAP values, feature importance reports, and plain-language decision summaries for non-technical stakeholders and regulators.
Audit Trails & Documentation
Immutable logging for every model decision, data lineage tracking, and version-controlled model registries. Complete traceability from input data to output decision.
Governance Policies & Procedures
Board-ready AI governance policies, acceptable use standards, procurement review checklists, and incident response playbooks. Role definitions for AI oversight committees.
Continuous Monitoring
Production monitoring for model drift, data quality degradation, and performance anomalies. Automated alerting with defined escalation paths and remediation workflows.
How It Works
A structured, four-phase approach that takes you from uncontrolled AI to auditable governance.
Assessment
Inventory all AI systems in production and development. Classify risk levels against EU AI Act categories and NIST AI RMF profiles. Identify governance gaps and regulatory exposure.
Policy Design
Draft AI governance policies, risk management frameworks, and operational procedures. Define roles, responsibilities, and escalation paths. Align documentation to ISO 42001 and applicable industry standards.
Implementation
Deploy bias testing pipelines, explainability tooling, and audit trail infrastructure. Integrate monitoring into existing MLOps workflows. Train governance committees and model owners.
Monitoring
Establish ongoing model risk reviews, quarterly fairness audits, and regulatory change tracking. Continuous improvement cycles with documented evidence for auditors and regulators.
Industries We Serve
AI governance built for the regulatory realities of your industry.
Pharma & Life Sciences
FDA AI/ML guidance compliance, GxP-validated AI systems, and clinical trial AI governance.
Healthcare
HIPAA-aligned AI governance, clinical decision support oversight, and patient safety monitoring.
Financial Services
SR 11-7 model risk management, fair lending AI audits, and algorithmic trading governance.
Manufacturing
Quality system AI integration, predictive maintenance model governance, and safety-critical AI oversight.
Frequently Asked Questions
What is AI governance and why does it matter for regulated industries?
AI governance is the set of policies, processes, and controls that ensure AI systems operate safely, fairly, and in compliance with applicable regulations. For regulated industries like pharma, healthcare, and financial services, AI governance is not optional. Regulators including the FDA, EMA, and SEC increasingly require documented evidence that AI-driven decisions are explainable, auditable, and free from prohibited bias. Without formal governance, organizations face regulatory penalties, reputational damage, and operational risk from uncontrolled AI deployments.
How does the EU AI Act affect organizations using AI in healthcare and financial services?
The EU AI Act classifies AI systems by risk level. Many AI applications in healthcare (clinical decision support, diagnostic AI) and financial services (credit scoring, fraud detection, algorithmic trading) fall under the high-risk category. High-risk systems require conformity assessments, technical documentation, human oversight mechanisms, data governance standards, and post-market monitoring. Organizations deploying these systems in the EU or serving EU customers must demonstrate compliance or face fines up to 35 million euros or 7% of global turnover.
What is the difference between NIST AI RMF and ISO 42001?
The NIST AI Risk Management Framework is a voluntary framework published by the U.S. National Institute of Standards and Technology. It provides a structured approach to identifying, assessing, and managing AI risks through four functions: Govern, Map, Measure, and Manage. ISO 42001 is an international standard for AI Management Systems that organizations can certify against. NIST AI RMF focuses on risk management practices while ISO 42001 provides a certifiable management system. Many organizations implement both: NIST AI RMF for risk methodology and ISO 42001 for management system certification.
How long does it take to implement an AI governance program?
A foundational AI governance program typically takes 8 to 16 weeks to implement, depending on the number of AI systems in scope and organizational complexity. The 2-Week AI Assessment ($9,500) covers the initial inventory, risk classification, and gap analysis. Policy design and documentation typically follow over 4 to 6 weeks. Technical implementation of bias testing, explainability tooling, and audit trails runs in parallel over 6 to 10 weeks. Ongoing monitoring and continuous improvement are established before handover.
Can QueryNow help with AI governance for existing AI systems already in production?
Yes. Most of our governance engagements involve AI systems already deployed in production. We start with a comprehensive inventory and risk assessment of existing models, then retroactively build governance documentation, implement monitoring, and establish ongoing review cycles. For systems identified as high-risk under the EU AI Act or other frameworks, we prioritize remediation to close compliance gaps while minimizing disruption to business operations.
Start Building Trust in Your AI Systems
Our 2-Week AI Assessment identifies governance gaps, classifies risk across your AI portfolio, and delivers a prioritized remediation roadmap. $9,500 fixed price.