aerais
Data & AI Governance Systems

Every AI decision, traceable to the data behind it.

aerais governs the whole path — the data that feeds your AI, and the AI itself — as one tamper-evident chain of custody. So when a regulator or a board says show me, you hand over proof, not posture. And we don't sell you a platform to run: we build the governance into your systems, and operate it.

Chain of custody — data to AI decision A closed governance circuit: governed data feeds the AI along the top path; every AI decision traces back to its source data along the highlighted return path — one unbroken chain of custody. DATA AI FEEDS → ← TRACES BACK
Alignment ISO/IEC 42001 · NIST AI RMF · EU AI Act · DAMA‑DMBOK · DCAM · CDMC
Scope Data → AI decision · one chain of custody
Position Boutique — we build & run it, not license it
Mandate · Why now

AI is making decisions no one can trace.

Enterprises are wiring AI into core decisions faster than oversight can keep up — on data that is largely uncatalogued, unlineaged, and unproven. Run AI on data you can't vouch for and you're not just audit-exposed; you're model-exposed. When a decision is challenged — by a regulator, a board, or a court — the answer can't be a shrug.

You can't defend a decision you can't trace to its data.

R‑01Shadow AI is already widespread. Most enterprises cannot see their full AI footprint.
R‑02Approval and oversight remain inconsistent, fragmented, and undocumented.
R‑03Model behavior — drift, hallucination, bias — is becoming enterprise risk.
R‑04Third‑party and vendor AI multiply exposure across the supply chain.
R‑05Regulation is accelerating and diverging across jurisdictions — for data and AI alike.
R‑06The data feeding AI is largely uncatalogued, unclassified, and unlineaged — exposure with no provenance.
R‑07When a model misbehaves, few can trace it back to the data that caused it.
Position · What we are

Not a platform to run. Governance, operated.

The AI-governance tools stop at the model. The data catalogs stop at lineage. Both hand you software to staff and run. aerais works one layer deeper — across the seam between data and AI — and we don't license it to you. We embed governance into your operating model, delivery lifecycle, and the data beneath them, then operate it.

The result is governance that scales with innovation and holds up under examination. We sit above generalist boutiques and beneath Big-4 overhead — deeper specialization, real implementation, and a chain of custody you own.

Not a platform to run
Not policy decks
Not ethics consultants

We are a governance systems and controls firm — built and run for you.

The Chain · Data → AI → decision

Both halves. One chain of custody.

Data governance and AI governance have converged. Governed data feeds the AI; when the AI is questioned, the answer traces back to the data. aerais runs both halves as one chain — one graph, one evidence chain, one control crosswalk — so provenance and proof never break at the seam.

  Data feeds AI
Data plane Discover & catalog Classify · sensitive‑data / DSPM Lineage & metadata Data quality Privacy · DSAR / RoPA / DPIA / consent
The seam · only aerais Data‑to‑AI lineage & training‑data provenance Quality‑gated AI release Contaminated‑data blast radius Right‑to‑be‑forgotten across data & models Unified control crosswalk
AI plane AI inventory Assurance & evaluations Red‑team & adversarial testing Runtime guardrails Monitoring & incident
AI risk traces back to its data  

Five capabilities live on the seam — and only run if you govern both sides:

LOOP‑01Data‑to‑AI lineageEvery model and feature traced to its source data and training‑data provenance.
LOOP‑02Quality‑gated releaseData‑quality thresholds gate AI deployment. Bad data cannot ship a model.
LOOP‑03Blast radiusFind a source contaminated? Trace every model, feature, and vector store it touched.
LOOP‑04Right‑to‑be‑forgottenErase a subject end to end — across data stores and into features, training sets, models, and vector stores.
LOOP‑05Unified crosswalkOne control, evidenced once, satisfies both data and AI regimes.

Data catalogs govern the left and stop at lineage. AI-governance tools govern the right and start at the model. aerais owns the whole chain — data to decision, on tamper-evident evidence — and operates it.

Practice · Capabilities

One integrated practice — across data and AI.

Two planes, one system: a data-governance service line and an AI-governance service line that share one control crosswalk and one chain of custody. Enter at any point; scale into a full transformation and a managed-governance function we run.

Data Governance · DG — where the chain begins
DG‑1 · Data Discovery & CatalogFind & inventory

See the data estate

  • Automated discovery
  • Business & technical catalog
  • Ownership & stewardship
  • Data product registry
DG‑2 · Classification & DSPMFind the sensitive

Classify & protect

  • Sensitive‑data classification
  • DSPM posture
  • Access & exposure
  • Policy tagging
DG‑3 · Lineage & MetadataProve provenance

Trace every flow

  • End‑to‑end lineage
  • Metadata management
  • Data‑to‑AI provenance
  • Impact analysis
DG-4 · Data QualityProve the inputs

Gate on quality

  • Quality rules & scoring
  • Profiling & monitoring
  • Quality gates for AI
  • Remediation workflow
DG‑5 · Privacy OperationsHonor the subject

Privacy, embedded

  • DSAR / RoPA / DPIA
  • Consent & purpose
  • Data mapping
  • Right‑to‑be‑forgotten
DG → AI‑G One practice, both planes Every data control feeds the AI plane — and the same evidence proves both. One graph, one crosswalk.
AI Governance · AI-G — where the chain ends
A · Strategy & AssessmentWhere are we now?

Establish current state

  • Governance maturity assessment
  • AI risk exposure diagnostic
  • ISO/IEC 42001 readiness
  • AI inventory & shadow‑AI discovery
B · Governance DesignWhat should it be?

Architect the system

  • Operating‑model design
  • Risk taxonomy & control framework
  • Responsible‑AI framework
  • Policy & standards suite
C · ImplementationMake it real

Operationalize governance

  • Lifecycle governance implementation
  • Control operationalization
  • Monitoring & assurance setup
  • Governance tooling enablement
D · ISO 42001 & RegulatoryDefensible

Certifiable & audit‑ready

  • AI Management System (AIMS) build
  • Regulatory alignment & mapping
  • Audit readiness & assurance
  • Mock certification review
E · Managed GovernanceRun it for them

Governance as a service

  • Governance office as a service
  • Risk & compliance monitoring
  • Control testing & assurance
  • Executive & board reporting
F · SpecializedPremium edge

High‑value programs

  • Shadow‑AI detection program
  • AI incident response & governance
  • Model‑risk governance
  • Agentic & RAG governance

→ Not two silos. Every engagement spans both planes — data flows into AI, AI risk traces back, and one chain of custody covers it.

Method · How we deliver

One system. Four phases.

This is a transformation program we run — not a point solution. Each phase produces working governance, and compounds into the next.

01 / Assess
Assess

Establish current‑state maturity, risk exposure, and the data‑and‑AI footprint the organization cannot yet see.

02 / Design
Design

Architect the operating model, risk taxonomy, control framework, and policies that fit the risk profile.

03 / Build
Implement

Embed governance into intake, delivery, deployment, and monitoring. Controls that run — not controls on paper.

04 / Operate
Operate

Assure, monitor, and report continuously — or run the entire governance office as a managed service.

→ Land and expand. Enter at any maturity level; each phase becomes the mandate for the next.

Operating model · Structure

Federated governance with central oversight.

Governance that balances innovation and control — accountability distributed to the business, standards held at the center, assurance kept independent across three lines of defense.

Board & Executive Strategic oversight
Enterprise AI Governance Council Decision authority
AI Center of Excellence Standards & enablement
Risk · Security · Legal 2nd lineIndependent oversight
Business Units 1st lineAI ownership & execution
Engineering & Data Technical implementation
Internal Audit 3rd lineIndependent assurance
Framework · Our IP

Proprietary governance IP, not slideware.

Every engagement configures the same governance system — reusable, testable, regulator-defensible assets that span data and AI. The signature asset is the unified control crosswalk: one control, evidenced once, satisfying both regimes.

IP · REF‑ARCH Reference Architecture The standardized governance model across data and AI — structure, control layers, lifecycle gates, and operational integration.
IP · RSK‑TAX Data & AI Risk Taxonomy Domains → categories → scenarios across data and AI, scored for inherent and residual risk and mapped directly to controls.
IP · CTL‑LIB Control Library Specified, evidenced, testable controls spanning data and AI, with implementation guidance and maturity criteria per control.
IP · LIFE‑CYC Lifecycle Framework Governance gates from data intake through model design, validation, deployment, monitoring, and retirement.
IP · MAT‑MOD Maturity Model A 1–5 scoring system across data and AI governance — risk, control, lifecycle, monitoring, and regulatory alignment.
IP · ASSESS‑KIT Assessment Toolkit Interview guides, evidence‑based scoring matrices, and heatmaps that produce board‑ready findings across data and AI in weeks.
Signature IP · Unified control crosswalk
Control · evidenced once ISO 42001 NIST AI RMF EU AI Act DAMA‑DMBOK DCAM* CDMC* Evidence
CTL‑07Sensitive‑data access & purpose
CTL‑12Training‑data lineage & provenance
CTL‑21Subject erasure · data & models

One control, evidenced once, satisfies every mapped regime — evidence captured once, reused across audits.
*Aligned to DCAM and CDMC: aerais maps to them and does not reproduce EDM Council proprietary content.

Data & AI governance maturity — scoring scale
01
Initial
02
Emerging
03
Established
04
Managed
05
Optimized
Signal states Active · live junction Compliant · guardrail met Drift · review Breach · kill⁠-switch
Engagement · Investment

A commercial model built to land and expand.

Four tiers, from a focused diagnostic to a fully managed governance function — each spanning data and AI as one continuum. Ranges are indicative and scoped to enterprise size, data‑and‑AI footprint, and regulatory exposure.

Land → expand · the value ladder
01
DiagnosticEstablish exposure & roadmap
Maturity assessment · data & AI risk exposure · executive roadmap.
$100K – $300KFixed fee
02
DesignArchitect the system
Operating model · control framework · risk taxonomy · policies — across data and AI.
$300K – $1MFixed fee
03
TransformationOperationalize at scale
Implementation · lineage & lifecycle integration · assurance enablement.
$1M – $5M+Milestone‑based
04
Managed GovernanceRun it continuously
Governance operations · assurance · regulatory tracking · reporting — data and AI.
$300K – $3MPer year

Indicative ranges, USD. Fixed‑fee engagements are scoped from medium time estimates, with overruns absorbed — pricing confidence, not client friction.

Sectors · Who we serve

Built for regulated and AI‑scaling enterprises.

S‑01Financial ServicesModel risk and explainability — with data lineage back to every input.
S‑02InsuranceUnderwriting fairness and claims explainability, grounded in governed data.
S‑03HealthcarePatient‑data governance flowing into clinical AI, with consent end to end.
S‑04Public SectorTransparency and procurement governance across data and AI.
S‑05Energy & UtilitiesOperational AI risk and sensor‑data integrity in safety‑critical settings.
S‑06TechnologyData and AI governance at the scale of AI‑native operations.
Ideal fit $1B–$50B revenue· low‑to‑medium governance maturity· moderate‑to‑high regulatory exposure
FAQ · Common questions

Straight answers.

Q‑01

How is aerais different from a Big‑4 firm?

We build and operate governance systems — we don't hand you software to staff. Faster delivery, deeper specialization, and far less overhead than broad compliance advisory, with a chain of custody you own.

Q‑02

Do you only work on ISO/IEC 42001?

No. ISO/IEC 42001 readiness is one entry point. We also align to the NIST AI RMF and the EU AI Act, and integrate AI governance with your existing risk, security, and privacy frameworks.

Q‑03

Do you advise, or do you build?

We build. aerais embeds governance into intake, delivery, deployment, and monitoring — controls that run inside your systems, not documents that sit on a shelf.

Q‑04

How quickly can we see value?

A diagnostic produces board‑ready findings — current‑state maturity, risk exposure, and a prioritized roadmap — in a matter of weeks.

Q‑05

Who is the right fit?

Regulated and AI‑scaling enterprises, typically $1B–$50B in revenue, with low‑to‑medium governance maturity and moderate‑to‑high regulatory exposure.

Q‑06

Can you operate governance for us?

Yes. Our managed‑governance tier runs the governance office continuously — intake approvals, control testing, assurance, regulatory tracking, and board reporting.

Q‑07

How does aerais connect data governance and AI governance?

As one chain of custody. Governed data — cataloged, classified, lineaged, quality-gated, and privacy-controlled — feeds your AI; when the AI is questioned, the answer traces back to the data. We run both planes on one graph, one evidence chain, and one control crosswalk.

Q‑08

What is data‑to‑AI lineage?

The traceable path from a source dataset through features, training sets, and models to a specific AI output — so any model or decision can be traced back to the exact data that shaped it, and any contaminated source can be traced forward to everything it touched.

Q‑09

Can you handle a right‑to‑be‑forgotten request across AI models?

Yes — end to end. We locate a subject's data not only in data stores but inside features, training sets, models, and vector stores, and evidence the erasure across all of them. Privacy is embedded across the chain, not bolted on.

Q‑10

Do you align to DCAM and CDMC?

On the data side we align to DAMA‑DMBOK, DCAM, and CDMC; on the AI side to ISO/IEC 42001, the NIST AI RMF, and the EU AI Act — mapped through one crosswalk. We map to DCAM and CDMC; we do not reproduce EDM Council proprietary content.

Q-11

How is aerais different from data-catalog and AI-governance point tools?

Those are software platforms you buy, implement, and staff — and each governs one side: the data catalogs stop at lineage, the AI-governance tools start at the model. aerais is a firm that builds and operates governance across the whole chain — data to decision — and hands you the evidence. A different category: an operated outcome, not a license.

Contact · Next step

Make your data and AI provable.

Start with a diagnostic: current-state risk exposure, governance gaps, and a roadmap to a chain of custody from data to decision. Then we build it — and run it.