Federal / Defense Vertical -- Sample Snapshot

What your $499 Snapshot report looks like

Full sample: 8-layer determination + 3 fix-first items + 3 distributional-shape charts + counterparty-question rehearsal + independent-verifier declaration. Same structure your program office's actual Snapshot delivers within 3 business days.

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Category C -- Insufficient For ATO + RMF Attestation

This is what a failed defense-AI-reproducibility audit looks like — $499 to know if yours is next

12 high-severity distributional-drift events. Cohort F3 (mid-life austere-environment assets) red-lane UNDER-flagging rate collapsed 34% -> 8% silently -- safety-critical under-flagging. Your program's Snapshot uses the same structure -- your data, your AI, your operating context. $499. 3 business days.

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Sample scenario

Defense-industrial-base program office / prime contractor (fleet size 500-5,000 assets, multi-theater operating footprint) running an AI-driven predictive-maintenance classifier scoring each fleet asset per mission-cycle and routing to maintenance lane:

Green -- Mission Capable (score 0-33): no scheduled action
Yellow -- Watch / Scheduled (score 34-66): scheduled preventative window
Red -- Ground Urgent (score 67-100): ground for urgent repair / mission-critical component replacement

Silent drift (SAFETY UNDER-FLAGGING): post-Day 45, Cohort F3 (mid-life combat assets, mixed-utilization, austere-environment operating) systematically UNDER-routed to red_ground_urgent lane at 8% vs baseline 34% -- a 26 percentage-point cohort-differential in the WRONG direction that never triggered an alarm on the program office's fleet-readiness dashboard.

This vertical demonstrates that the sensor detects distributional drift in EITHER direction -- over-flagging (unnecessary groundings inflating sustainment cost) AND under-flagging (missed red-lane decisions creating safety-of-flight + mission-abort exposure).

Overall Determination

CATEGORY C — INSUFFICIENT FOR ATTESTATION. 12 high-severity distributional-drift events detected. Program office should not represent the predictive-maintenance AI as ATO-defensible, RMF-compliant, or DoD Ethical Principles adherent (Reliable / Traceable / Governable) until named remediation completes.

The 3 sample charts (your Snapshot delivers similar for your data)

Rolling mean readiness-risk score by cohort

Rolling mean readiness-risk score by asset cohort -- silent Cohort F3 DOWNWARD divergence starts Day 45 (under-flagging)

Baseline vs recent distribution for Cohort F3

Cohort F3 baseline vs recent readiness-risk-score distribution -- mass shifted LEFT out of red_ground_urgent lane (SAFETY UNDER-FLAGGING)

Maintenance-lane rate per cohort baseline vs recent -- F3 red_ground_urgent rate collapsed while others stable

The one sentence your Program Manager and Chief Engineer would care about

The AI predictive-maintenance classifier's red_ground_urgent lane rate for Cohort F3 (mid-life combat assets, mixed-utilization, austere-environment operating) collapsed from 34% to 8% over the audit period — a 26 percentage-point cohort-differential in the WRONG direction (SAFETY UNDER-FLAGGING) that occurred silently while the aggregate fleet-readiness dashboard showed all-green throughout.

Under DoD Directive 3000.09 (Autonomy in Weapon Systems) + DoD AI Ethical Principle Reliable + NIST SP 800-53 rev5 RMF continuous-monitoring requirements, any AI-driven readiness decision from the affected period is subject to reproducibility duty under GAO / DoD IG / Congressional Armed Services Committee / accident-investigation-board (JAG) inquiry standards.

The OMB M-24-10 AI Use Case Inventory attestation item — "does your program office monitor AI-driven readiness decisions for cohort-differential patterns?" — now has a materially harder-to-answer shape.

Regulatory framework applied

Framework	Applies to
NIST SP 800-53 rev5 (RMF)	AI-inclusive-system overlays + AU-11 audit-record-retention
CMMC 2.0 Level 2 + Level 3	Cybersecurity Maturity Model Certification for DIB contractors
DoD Instruction 5000.02	Adaptive Acquisition Framework -- AI sustainment
DoD Directive 3000.09	Autonomy in Weapon Systems
DoD AI Ethical Principles	Responsible, Equitable, Traceable, Reliable, Governable (Feb 2020)
CDAO AI Governance Framework 2024	Chief Digital and AI Officer operational cadence
Executive Order 14110 (+ 2025-2026 revisions)	Safe, Secure, and Trustworthy Development of AI
OMB M-24-10	Advancing Governance, Innovation, and Risk Management for Agency Use of AI
OMB M-24-18	Advancing the Responsible Acquisition of AI in Government
FedRAMP High	Cloud-hosted federal AI systems
ITAR 22 CFR 120-130	Export controls on AI models w/ defense applications
Contract Disputes Act 41 USC 71	Contractor liability for AI-driven decisions
FAR Part 12 + DFARS 252.239	Federal acquisition AI-specific clauses

What your program office's actual Snapshot includes

Section	What you get
Scope + regulatory framework	Named ATO scope + NIST SP 800-53 rev5 RMF + CMMC 2.0 + DoD 5000.02 + DoD 3000.09 + DoD AI Ethical Principles + OMB M-24-10/18 citations tied to your AI Use Case Inventory entry
Sensor summary	Independent-observer specifics, distinct model family, retention pipeline distinct from your production stack; bi-directional drift detection (over- AND under-flagging)
Findings summary	Count of drift events + high/medium severity + cohort-differential flag + first-drift-day
Overall determination	Category A / B / C w/ specific meaning + ATO-package-alignment remediation path
3 fix-first items	Scoped to YOUR AI surface, YOUR ATO scope, YOUR next ATO renewal cycle
Detailed drift events	Every drift event day + type + affected cohort + severity + plain-language detail
Counterparty-question rehearsal	5 sample decisions from target-day w/ cryptographic decision-hash reproducibility verification -- suitable for GAO / DoD IG / Congressional inquiry / accident-investigation-board response
Upgrade paths	Baseline ($2,500 / 5 days) OR Enterprise Attestation ($35-55K / 3-6 weeks board-ready + regulator-facing signed statement) + $499 Snapshot credit applies
Independent-verifier declaration	Signed by Kevin Luddy personally + CMMC 2.0 Level 2 alignment statement
Charts + artifacts	3 distributional-shape charts + machine-readable JSON + decision-hash lookup table

Big-4-equivalent cost benchmark

Booz Allen / Leidos / SAIC / CACI AI-assurance practice engagement pricing for equivalent-scope Enterprise Attestation typically $400K-$1.2M+ with 4-9 month timeline.

contrarianAI Enterprise Attestation delivers equivalent-substance deliverable at ~5-10% of that cost with 3-6 week timeline via the independent-verifier operating model.

The $499 Snapshot lets you test the discipline below-procurement-threshold before organizational commitment.

$499. Three business days. Your program's actual Snapshot.

Same structure as this sample. Your program's data, your program's AI system, your program's ATO scope, your program's regulatory-framework citations. No classified / CUI / ITAR data required to run the Snapshot -- anonymized decision records are sufficient.

Buy your Snapshot — $499

Snapshot credit ($499) applies to Baseline ($2,500) or Enterprise Attestation ($35-55K) upgrade within 30 days. Direct-buy Stripe. Below-procurement-threshold. Report PDF via email within 3 business days. CMMC 2.0 Level 2 aligned; Level 3 path available on engagement scope requiring it.