AIW — Decisions. Probabilistic. Defensible.
Compliance & Governance

Governance Is the Product.

AIW was not built with compliance added on top. Governance is the architecture — every layer of the system exists to make AI decisions auditable, bounded, and defensible under scrutiny.

P(compliant | governed) = 1.00Governance is the prior
0Compliance Incidents
100%Decisions Logged
6Framework Alignments
AIW // BAYESIAN AUDIT RECORD
RECORD_IDAIW-2026-04-0047-AUD
TIMESTAMP2026-04-24T14:32:11Z
USER_IDUSR-0291 // CLEARED
CLEARANCECUI // CONTROLLED
MODEL_USEDENSEMBLE-v4.2 // BAYESIAN
CONFIDENCEP = 0.874
POSTERIORP(H|E) = 0.874
SOURCES_CITED14 VERIFIED
POLICY_CHECKPASS // P(violation) = 0.00
APPROVERUSR-0044 // AUTHORIZED
APPROVAL_TIME2026-04-24T14:35:02Z
LOG_HASHsha256:a3f9c2...d841
STATUSFINALIZED // IMMUTABLE
Governance Pillars

Six Layers of Control.

Each pillar addresses a specific failure mode of ungoverned AI in federal and defense environments. Each carries a Bayesian probability of failure — all zero.

01

Immutable Audit Trail

Every decision, every output, every approval — permanently recorded and cryptographically signed.

P(tampered log) = 0.00
Timestamped log entry generated for every inference request
Cryptographic signing ensures logs cannot be altered post-hoc
Full decision reconstruction: any output can be replayed with original context
Audit records include model used, confidence score, sources cited, and approver identity
Exportable in standard formats for IG, legal, and compliance review
02

Human-in-the-Loop Controls

AI cannot finalize consequential decisions without explicit human authorization.

P(autonomous action) = 0.00
Mandatory approval gates on all decision-class outputs
Role-based reviewer assignment — decisions route to the correct authority
Override and rejection logging — human corrections feed back into model weighting
Configurable escalation thresholds: low-confidence outputs require senior review
No autonomous action — AIW produces recommendations, humans execute
03

Clearance-Level Data Handling

Data is segmented, bounded, and enforced at the model layer — not just the UI.

P(cross-domain contamination) = 0.00
CUI, Secret, and TS/SCI segmentation enforced at inference time
Cross-domain contamination prevention — outputs cannot blend data across classification levels
Need-to-know enforcement: users only access data within their authorized scope
No data persistence outside authorized boundaries
Classification markings propagated through all generated outputs
04

Policy Enforcement at Inference

Governance rules are enforced where they matter — at the moment of generation, not after.

P(policy violation) = 0.00
Policy constraints compiled into the inference pipeline — not applied as post-processing filters
Real-time violation detection with hard stops before output delivery
Mission-scope bounding: AI cannot reason outside its authorized domain
Hallucination suppression via source-anchored generation with evidence requirements
Policy version control — rule changes are logged and auditable
05

Documented Process Architecture

Every workflow is documented, repeatable, and defensible — meeting the new federal standard.

P(undocumented process) = 0.00
Process documentation generated automatically from workflow execution logs
Standard operating procedures (SOPs) derived from actual system behavior
Gap analysis against solicitation requirements — compliance deficiencies surfaced before submission
Workflow versioning: process changes are tracked and attributed
Audit-ready documentation packages exportable on demand
06

Zero-Incident Track Record

Across all deployments, AIW has recorded zero compliance incidents.

P(incident | AIW) = 0.00
No unauthorized data access events across any deployment
No policy violations reaching output delivery
No audit trail gaps or missing records
No cross-classification contamination incidents
Continuous monitoring with automated anomaly detection and alerting
Framework Alignment

Built to the Standards That Matter.

AIW is designed in alignment with the regulatory and policy frameworks governing AI in federal and defense environments. Each carries a Bayesian alignment confidence score.

NIST AI RMF
P = 0.97Aligned

Govern, Map, Measure, Manage functions implemented

FedRAMP
P = 0.94Ready

Architecture designed for FedRAMP authorization path

CMMC Level 2/3
P = 0.96Aligned

CUI handling and access controls meet CMMC requirements

EO 14110
P = 0.98Aligned

Safe, secure, trustworthy AI per executive order requirements

FISMA
P = 0.95Aligned

Information security controls and continuous monitoring

DoD AI Ethics
P = 0.97Aligned

Responsible, equitable, traceable, reliable, governable

Operational Implications

What This Means for Federal Agencies.

Decisions that hold up under IG review

Every AIW output includes the evidence, rationale, and approval chain needed to survive an inspector general audit.

AI you can brief to leadership

Confidence scores, source citations, and human approval records make AIW outputs presentable at the executive and congressional level.

Compliance documentation on demand

Audit packages, process documentation, and decision records are exportable at any time — no manual reconstruction required.

For GovCon Firms

What This Means for Contractors.

Prove how you operate — not just that you're eligible

Agencies now evaluate process maturity. AIW gives you documented, auditable AI workflows that differentiate your proposal.

Compliance gaps caught before submission

Automated gap analysis against solicitation requirements flags deficiencies before they become evaluation weaknesses.

Win rates backed by data, not instinct

Bayesian Pwin scoring with 87% accuracy means bid/no-bid decisions are grounded in evidence — and documented for review.

The federal standard has shifted. Undocumented firms are being filtered out. AIW is the documentation.

See AIW Governance in a Live Environment.

Walk through a real decision cycle — audit trail, approval chain, and all. Classified and unclassified briefing options available.

Schedule a Briefing