OverviewMethodology
First Edition·2026·Zytra Technologies

AI Governance
Maturity Model

for Banking & Financial Services
9 Domains  ·  3 Capability Dimensions  ·  27 Scored Cells  ·  5 Levels  ·  4 Gate Tests

The first BFSI-specific AI governance maturity model that scores People, Process, and Technology as separate evidence-gated capability axes in every governance domain — so a maturity level is a verifiable capability state, not a self-reported claim.

DCAM ANALOGUE
The EDM Council's DCAM is the industry benchmark for data management capability in financial services. The Zytra AGMM applies that rigour to AI governance — and goes further: every domain is scored across three capability dimensions with evidence-anchored criteria and gate tests per level. Where DCAM governs data, AGMM governs intelligence.
The Scoring Architecture
Three dimensions. Nine domains. Twenty-seven cells.
Each governance domain is scored 1–5 on three separate capability dimensions. The weakest dimension sets the domain's level — because it is the operating ceiling.
DIMENSION · PEOPLE

Who owns and staffs it

Named owners, funded capacity, specialist skills, board competence, succession. Process without people is paper. Governance that lives in one person's head fails the bus test.

DIMENSION · PROCESS

What actually operates

Documented, approved, and — critically — executed. Gates with exception logs, cadences producing minutes, escalations exercised. Written-but-not-run scores as not run.

DIMENSION · TECHNOLOGY

What generates evidence

Inventory, registry, monitoring, scoring engines, audit trails. Tooling without process is shelfware — but at scale, evidence cannot be produced manually. Tech is the L3→L4 gate.

The floor rule: a domain's level is the minimum of its three dimension scores. The organisation's achieved level is the minimum across applicable domains — reported alongside the frontier (best domain) and the spread between them. A wide spread is an imbalance problem, not a maturity problem, and it has a different fix.
Gate Tests
One verifiable test per level
Questionnaire scores are claims. Each level has a single gate test an assessor can run. Fail the gate, and the level is not achieved — regardless of the questionnaire.
LevelGate test
L2 · DefinedProduce a complete inventory of every AI system in production, reconciled against IT asset records. No inventory, no Level 2 — regardless of policy quality.
L3 · OperationalisedAn examiner walks in cold and audits without any preparation on your part. If preparation is needed, the organisation is Level 2 with good marketing.
L4 · MeasuredShow two consecutive control-testing cycles with unchanged metric definitions. One snapshot is a point; measurement is a trend.
L5 · OptimisedShow one complete improvement loop — incident → catalogue update → control change → measured effectiveness shift — that nobody outside the governance system initiated.
Five Levels
The maturity scale
LEVEL 01
Ad Hoc
Governance informal or absent. Decisions case by case. Incidents find you.
LEVEL 02
Defined
Owner named, policy approved, inventory complete. Exists in writing; execution inconsistent.
LEVEL 03
Operationalised
Gates enforced, cadences running, evidence generated. Survives a cold audit.
LEVEL 04
Measured
Effectiveness quantified over consecutive cycles. Untested controls score zero. Heat maps drive investment.
LEVEL 05
Optimised
Self-improving system. Board-approved appetite. The loop closes without external prompting.
Nine Governance Domains
What the AGMM covers
DOMAIN 01 · RM
Risk Management
Identification, classification, and residual quantification of AI failure modes. Grounded in 138 BFSI-specific catalogued risks.
Deep Coverage
DOMAIN 02 · SR
Security & Robustness
AI-specific attack surface: adversarial inputs, prompt injection, model theft, data poisoning, provider concentration risk — and resilience under attack.
DOMAIN 03 · MV
Model Lifecycle & Validation
Validation before deployment, versioning through life, approval gates, drift monitoring in production.
DOMAIN 04 · DG
Data Governance for AI
Training data lineage, quality gates, provenance, sensitive-attribute management across AI pipelines.
DOMAIN 05 · FE
Fairness & Ethics
Bias testing, ethics review, operationalised principles, proactive customer-harm management.
DOMAIN 06 · ET
Explainability & Transparency
Decision audit trails, explanation capability, complexity governance, disclosure standards.
DOMAIN 07 · RC
Regulatory Compliance
Multi-framework orchestration: RBI, SEBI, EU AI Act, SR 11-7 MRM, NIST AI RMF, DORA — reconciled, not duplicated.
DOMAIN 08 · SO
Strategy & Operating Model
Board-approved policy, ownership, culture and incentives, governance velocity and ROI, resourcing proportional to portfolio growth.
DOMAIN 09 · AG
Agentic AI Governance
Autonomy boundaries, multi-agent controls, tool authority, per-step observability. N/A only if no deployments and none on a 12-month roadmap.
First in Industry
Training & Certification
Workshops built on the AGMM
Every participant leaves with their 27-cell grid, imbalance flags, and a dimension-targeted 90-day plan.
SESSION 01 · 90 MIN

Maturity Sprint

Facilitated 27-cell assessment with the risk committee. Leave with the grid, flags, and top three gaps prioritised.

SESSION 02 · HALF DAY

Risk Domain Deep-Dive

The full 138-risk atlas: inherent scoring, control effectiveness, residual quantification by domain.

SESSION 03 · HALF DAY

Agentic AI Governance

The only BFSI-specific training on autonomous agent governance — 75 agentic risks, MAESTRO alignment, multi-agent controls.

SESSION 04 · 60 MIN

Board Briefing

Executive session: the AGMM, your grid, and the investment case — dimension by dimension, not a single opaque score.