Training Module · Banking Sector · 2026
Chapter — Foundations

The Critical Importance
of Data Governance

Why data governance is no longer optional. Data Lineage as an indispensable regulatory requirement. And what Generative AI and autonomous agents are changing — radically — about data challenges.

Part I
Regulatory Challenges
& Data Lineage
Part II
GenAI & Agentic AI
Critical Governance
Part III
Failures, Examples
& Warning Signs
Instructor
Pejman Gohari · CDO · Chief AI Officer · ORBii
Audience
Data Owners · CDO · CIO · Compliance · Management · IT
ORBii.Academy · The Critical Importance of Data GovernanceConfidential · 202601
Importance of Data Governance · 02
Introduction

Poorly governed data: the silent risk turning systemic

"Without data governance, Generative AI is merely a machine that amplifies your errors at high speed. A false data point in an LLM produces a false decision — but with the appearance of confidence."
— Pejman Gohari · CDO · Chief AI Officer · ORBii

What data governance actually protects

Data governance is not an IT project. It is the organization's immune system against three types of risks that have accelerated simultaneously.

Regulatory Risk

DORA, EU AI Act, GDPR, Basel III require documented data traceability. Without Data Lineage, no financial institution can demonstrate audit compliance. Penalties reach EUR 10M or 2% of global revenue (GDPR). DORA: up to 1% of daily revenue for unreported incidents.

AI Risk

An LLM trained on or fed with poorly qualified, untraced or unclassified data produces incorrect but confident results. The trust placed in AI amplifies error propagation. In banking: biased credit decisions, fraudulent scoring, erroneous regulatory reporting.

Operational Risk

Autonomous AI agents act on data without systematic human oversight. If they access unclassified data, share sensitive data between systems or execute actions on incorrect data, the impact can be irreversible within minutes.

The 5 symptoms of absent governance

These situations are commonplace in organizations that have not yet structured their data governance. Each one constitutes a real risk.

S1
"We don't know where this data comes from"
A figure in a regulatory report that no employee can trace back to its origin with certainty. Source system A or B? How was it transformed? By whom?
→ Audit risk · Non-auditable data · DORA non-compliant
S2
"The same client has 3 different IDs in 3 systems"
Lack of MDM (Master Data Management). The LLM interprets 3 different clients. The AI model produces 3 different scores for the same person.
→ Incorrect AI decisions · EU AI Act Art.13 violation (accuracy)
S3
"We don't have a list of sensitive data"
No inventory of personal, financial or confidential data. An AI agent can access everything — and share this data with an external LLM (OpenAI, etc.).
→ Data breach · GDPR violation · Critical Shadow AI
S4
"The Data Owner? That's... IT, right?"
No Data Owner role defined. Nobody validates data quality or accuracy. In case of incident, no accountability is established.
→ No accountability · No reference system · Audit impossible
S5
"We use ChatGPT to analyze this customer file"
Shadow AI: an employee sends a customer export to a public LLM because no secure internal tool exists. The AI policy either doesn't exist or isn't known.
→ GDPR violation · Banking secrecy compromised · IP exposure
ORBii.Academy · The Critical Importance of Data GovernanceConfidential · 202602
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