AI models change fast, explain less, and now act.
Model risk management was built for models that are validated, documented, and relatively stable. AI breaks those assumptions, continuous change, limited explainability, and autonomy. Stratenity extends model risk discipline to AI, so the models making decisions stay accountable.
The assumptions behind model risk broke.
Traditional MRM assumes a model you can validate once and monitor against a stable baseline. AI models retrain, drift, and increasingly take actions, none of which the old cadence anticipated.
The answer is not to abandon model risk discipline but to extend it: continuous validation, drift monitoring, explainability where decisions affect people, and accountability for models that act.
What AI adds to model risk.
The three shifts that make AI a distinct model-risk problem.
models retrain and drift continuously, so one-time validation is not enough.
Stratenity POVlower explainability raises the burden of proof for fairness and adverse decisions.
Stratenity POVagentic models act, so accountability must reach the decisions they take.
Stratenity POVYou feel this if any of these are true.
This is for risk and data leaders extending model governance to AI systems.
- AI models enter use faster than they can be validated.
- You cannot fully explain decisions that affect customers or employees.
- There is no continuous monitoring for model drift and performance.
- Accountability for agentic models that act is unclear.
- You need audit-ready evidence of how AI models are governed.
Model risk, extended to AI.
The controls that keep AI models accountable through continuous change.
Continuous validation
Validation designed for models that retrain and change, not a one-time sign-off.
Drift & performance
Ongoing monitoring for drift, degradation, and unexpected behavior.
Decision transparency
Explainability where AI decisions affect people, with adverse-action logic.
Ownership & escalation
Named owners and escalation for models, including agents that take action.
Evidence & audit trail
Documentation structured for internal audit and external review.
Standards mapping
Controls mapped to the EU AI Act, NIST AI RMF, and ISO 42001.
Discipline, applied to a faster problem.
We bring model-risk rigor to AI, without pretending AI behaves like the models MRM was built for.
Our AI Risk & Model Risk area sits inside a six-part governance practice mapped to the major frameworks, so validation and monitoring connect to the wider program. Senior-led, across all 50 U.S. states.
Start with the models that decide.
Begin where AI models carry the most risk, then extend the controls.
Strategy Scan
A fast, senior-led read on your highest-stakes decision, framing, evidence, risk, and a 90-day plan. Days, not weeks.
Explore ›Advisory & Execution
Ongoing senior support that turns the decision into owners, KPIs, and an operating cadence.
Explore ›AI Governance program
Stand up the council and controls mapped to the EU AI Act, NIST AI RMF, and ISO 42001, governance you can evidence.
Explore ›AI model risk advisory, delivered across all 50 U.S. states.
Delivered virtually, so senior support reaches your team wherever it is based, from Alabama to Wyoming.
What leaders ask first.
What is AI model risk management? +
How is it different from traditional MRM? +
Do you work with organizations in my state? +
How is Stratenity different from a traditional top consulting firm? +
Keep the models that decide accountable.
Framework references: EU AI Act; NIST AI RMF; ISO/IEC 42001.