AI Governance
AI Governance & Model Risk Management
Independent governance, model risk management, and audit-ready controls for AI systems operating under regulatory scrutiny, from financial institutions to critical infrastructure.
Why It Matters
AI you can defend
to a regulator, a board,
and an auditor.
The same discipline that keeps industrial AI reliable in the field is what keeps AI trustworthy in banking, financial services, insurance, and other heavily regulated sectors. We build the governance, assessments, controls, and evidence that let leaders deploy AI with confidence, and prove it holds up under review.
Our Framework
The Incyra Trust Framework
Five pillars that turn "responsible AI" from a principle into an operating system, applied end to end, from model design through live monitoring and audit.
Model Risk & Validation
Independent validation, including benchmarking, fairness and bias assessments, explainability, and stress testing, to ensure models are reliable, equitable, compliant, and production-ready.
Monitoring & Drift Control
Continuous tracking of performance, data quality, and drift, with thresholds and alerts that catch degradation before it reaches a decision.
Human Oversight & Accountability
Clear ownership, escalation paths, and human-in-the-loop controls so accountability for every AI-assisted decision is never ambiguous.
Regulatory Alignment
Controls mapped to the frameworks your regulators expect: the EU AI Act, NIST AI RMF, ISO/IEC 42001, and model risk standards like SR 11-7.
Audit & Evidence
Documentation, data lineage, and audit-ready reporting generated as a by-product of the process, not scrambled together the week before a review.
Who It's For
Built for Regulated Industries
Governance calibrated to the oversight each sector operates under.
Model risk management, validation, and controls for credit, fraud, trading, and AML models.
Governance for lending, underwriting, and customer decisioning under supervisory scrutiny.
Fairness, explainability, and audit trails for pricing, claims, and risk-selection models.
Safety, traceability, and oversight for AI in clinical, diagnostic, and operational decisions.
Trustworthy AI for high-risk industrial and physical-asset environments where failure has consequences.
Transparent, accountable AI for services held to the highest standard of public trust.
Aligned to the Standards That Matter
We map your controls to the frameworks regulators and auditors actually reference.
Standard-Aware, Not Standard-Bound
We meet the frameworks that apply to you without turning governance into a paperwork exercise. The controls are real, and the evidence is a by-product of running them.EU AI Act
Risk classification and obligations for high-risk AI systems, including documentation and oversight.
NIST AI RMF
A structured approach to identifying, measuring, and managing AI risk across the lifecycle.
ISO/IEC 42001
An AI management system standard that signals certifiable, enterprise-grade governance.
Model Risk (SR 11-7)
Supervisory model risk management expectations for banks and financial institutions.
Engagement Outputs
What You Receive
Concrete artifacts your risk, compliance, and leadership teams can act on.
Comprehensive risk assessments across traditional AI, ML, generative AI, and agentic AI, evaluating governance, security, privacy, fairness, compliance, operational, and third-party risk, with prioritized remediation recommendations.
A mapped register of AI/ML systems, their risk tier, ownership, and control status.
A practical operating model covering roles, review gates, and decision rights for AI.
Independent validation reports covering performance, fairness, bias, robustness, and explainability, with continuous monitoring, drift detection, and escalation thresholds.
Executive dashboards and board-ready reporting with AI governance KPIs and KRIs, compliance status, validation outcomes, risk trends, incidents, and audit-ready evidence.
Your controls mapped to the frameworks that apply to you: the EU AI Act, NIST AI RMF, ISO/IEC 42001, and model risk standards like SR 11-7, with a clear view of where you comply and where the gaps are.
Deploying AI under regulatory scrutiny?
Start with a governance review that maps your AI risk and the controls you need to close the gap.