How Cichocki Advisory Approaches NIST AI RMF Alignment

The NIST AI Risk Management Framework is, in our experience at Cichocki Advisory, the most useful single document for enterprises trying to operationalize AI governance. Its four functions — Govern, Map, Measure, Manage — give organizations a shared vocabulary that survives leadership transitions and audit cycles.

But there's a gap between reading NIST AI RMF and being able to defend your alignment in front of a board, an auditor, or a regulator. This is where most enterprises get stuck. Below is how Cichocki Advisory bridges that gap.

The four functions, translated into operating reality

NIST publishes the framework as a flexible structure. Our methodology translates each function into a concrete artifact that an executive team can ship.

Govern → Decision rights and accountability

The Govern function asks: who decides, who approves, and who is accountable? Our deliverable is a documented AI decision-rights matrix that names — by role, not by individual — who owns each AI-specific decision class. This artifact is the single most important board-facing output of our engagements. Without it, every other governance investment is fragile to leadership change.

Map → Risk-tiered AI inventory

The Map function asks: what AI is being used, and what is its risk surface? Our deliverable is a tiered inventory that classifies every AI system in the organization by autonomy, consequence, and data sensitivity — not just "which model are we using." This inventory becomes the foundation for everything downstream: a system that's classified Tier 1 (high autonomy, high consequence) gets a different control bundle than a Tier 4 internal productivity tool.

Measure → Embedded telemetry

The Measure function asks: how do we know if our controls are working? Our deliverable is a measurement plan that's specific to each tier — what gets logged, what gets sampled, what gets human-reviewed, and what gets escalated. The work here is mostly resisting the temptation to measure everything; effective Measure is selective.

Manage → Lifecycle workflow

The Manage function asks: how does an AI system move from idea to production to retirement, with controls applied throughout? Our deliverable is a lifecycle workflow with explicit gates: ideation, design review, pre-deployment validation, deployment approval, post-deployment monitoring, and decommissioning. Each gate has a named approver and an evidence package that proves the prior gate's exit criteria were met.

What boards actually need to see

Boards don't read NIST AI RMF cover-to-cover. They need a one-page picture and the confidence that the underlying machinery is real.

Our standard board-ready output is a four-quadrant view: each NIST function on one axis, current maturity on the other. Maturity is grounded in observable evidence — not self-assessed scores. The view is updated quarterly with a delta from the prior quarter and a forward-look at the next two quarters of investment.

This is what makes the difference between "we have an AI governance program" and "the board can defend the AI governance program in front of a regulator." The first is a slide; the second is a system.

Common gaps we see

Across recent Cichocki Advisory engagements, three NIST AI RMF gaps appear consistently:

1. Govern is over-indexed; Map is under-built. Organizations write AI policies without first inventorying the AI they actually run. The result is policy that doesn't bind to anything operational.
2. Measure is generic. Telemetry is built once for all AI systems, regardless of risk tier. This creates noise, and the noise gets ignored.
3. Manage is ungated. AI systems deploy without explicit gate evidence, then fail post-deployment because nobody validated specific exit criteria.

Our 90-day implementation roadmap is structured to close exactly these gaps.

How NIST AI RMF connects to ThreadSync

Our advisory work and ThreadSync (the platform Cichocki Advisory founded) share a single design philosophy: governance frameworks should produce shippable controls, not just documents. ThreadSync's two products operationalize specific NIST AI RMF functions:

• LLM Gateway ships the Measure function for LLM-based systems — per-request audit trails, policy enforcement, model routing.
• Magic Runtime ships the Manage function for AI-generated business logic — contract validation, capability-based execution, sandboxed runtime.

The platform isn't a substitute for governance work; it's the substrate that makes governance enforceable. The connection between advisory and platform is documented separately.

Where to start

If you're an executive responsible for AI governance and you've read NIST AI RMF but don't know how to translate it into next month's roadmap:

1. Start with the inventory. Map every AI system, classify by tier. This usually takes 2-4 weeks and surfaces 40-60% more AI than executives believed existed.
2. Define decision rights for the top tier first. Don't try to govern Tier 4 productivity tools before you've governed Tier 1 customer-facing AI.
3. Pick three controls per function. Twelve total. Implement them. Show the board.
4. Iterate quarterly. NIST AI RMF alignment is not a one-time project; it's a continuous cycle of evidence-building.

Cichocki Advisory engagements typically run this cycle alongside the executive team for the first two quarters and then transition into a quarterly review cadence.