KRISID
Data Governance & AI Governance

Turn Scattered Data Into Governed, AI-Ready Assets

Trusted Data. Intelligent Decisions.

KRISID builds practical data governance foundations — operating models, clear ownership, catalogs, lineage and quality KPIs — implemented on Collibra, Microsoft Purview, Informatica CDGC, Atlan and Alation, and extended to govern the AI agents your business is starting to deploy.

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How KRISID governs your data Four scattered source systems — warehouse, lakehouse, SaaS applications and streams — feed into a governed core operated by KRISID. The core is ringed by four capabilities: catalog, lineage, quality and policy. From it flow two trusted outputs: business teams making decisions on certified data, and AI agents operating under policy with a full audit trail. Scattered sources Warehouse Lakehouse SaaS Apps Streams Catalog Lineage Quality Policy Business Teams Certified & findable CERTIFIED AI Agents Scoped tools, audited POLICY-BOUND Trusted outputs
20+

Years in data

5

Governance platforms

100%

Business-led delivery

SLA

Managed support

The problem

Most Governance Programmes Fail on Adoption, Not Technology

Organisations invest in catalogs, quality tools and workflow platforms — and months later the business is still working from spreadsheets, email threads and tribal knowledge. Governance was implemented for the business instead of with it.

Unclear Ownership

Nobody is accountable for a data asset, so quality issues go unresolved and definitions drift apart across teams.

Data Nobody Can Find

Without a curated catalog and lineage, analysts rebuild the same logic repeatedly and leaders lose confidence in reporting.

AI on Ungoverned Data

AI and agentic initiatives inherit unreliable data and undefined authority — creating risk faster than they create value.

What we do

Governance Services, End to End

From strategy and operating model through platform implementation, custom workflows and steady-state support.

Data Governance Strategy

Operating model, roles, policies, glossary and quality KPIs — plus the adoption, literacy and training programme that makes it stick.

Collibra Implementation

Domains, catalog onboarding, lineage, policies and privacy, Data Marketplace and Edge — plus managed support and upgrades.

Informatica CDGC

Scanners, curation model, glossary, lineage and stewardship — with source integration and automated onboarding.

Microsoft Purview

Data map, scanners, classifications, sensitivity labels, lineage and insights — integrated across your Azure estate.

Atlan

Active metadata, personas and purposes, column-level lineage and governance workflows embedded where teams already work.

Alation

Discovery and stewardship driven by real usage — query log analysis, trust flags, glossary and policy management.

Custom Workflow Development

BPMN-driven Collibra workflows for approvals, stewardship tasks, access requests and issue management — auditable by design.

Custom Integration

Secure connectors and APIs that synchronise metadata, lineage and policies for near-real-time, consistent catalogs.

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Industries

Where We Work

Governance obligations differ by sector. We work where the data is regulated, high-volume, or both.

BFSI

Risk data aggregation, regulatory reporting lineage, and the evidence supervisors ask for.

Technology

Product telemetry at scale, domain-owned data products, and governance for AI features.

Telecom

Subscriber data, network analytics, and consent management across very large volumes.

Media

Audience data, rights and licensing metadata, and privacy across syndication chains.

Healthcare

Patient data protection, consent and purpose limitation, and clinical data quality.

Where governance is heading

AI Governance Now Needs an Agent Registry

Enterprises are no longer deploying AI that only generates answers. They are introducing agents that discover information, call tools, access systems and execute business actions.

An agent catalog has to be more than an inventory. For every agent you need to know who owns it, what business purpose it serves, which systems it can reach, what it is allowed to execute, where human approval is required, and how its behaviour is audited.

Our AI governance approach →

An Agent That Assists ≠ an Agent That Executes

The two carry very different risk profiles, and your governance model should say so explicitly. We help you define authority tiers, approval gates and audit trails before agents touch a system of record.

  • Agent identity and ownership
  • Tool and data access boundaries
  • Human-in-the-loop approval gates
  • Behaviour monitoring and audit
  • Model and prompt lineage
How we work

Assess, Prioritise, Implement, Embed

A delivery sequence designed to show value in weeks — not after an eighteen-month programme.

  1. Assess

    We map your current state across people, process, policy and platform, and identify where accountability is genuinely missing rather than merely undocumented.

  2. Prioritise

    Quick wins first. We sequence domains and use cases so business teams feel the benefit before the programme asks anything of them.

  3. Implement

    Configuration, metadata harvesting, lineage, workflows and integrations on Collibra, Purview, CDGC, Atlan or Alation — engineered, not clicked together.

  4. Embed

    Enablement, data literacy, stewardship operating rhythms and SLA-driven support so governance survives after the consultants leave.

Insights

White Papers From Delivery Work

Written from what we see repeatedly in governance programmes. Free to read, no registration.

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Good Data Doesn't Happen by Accident

It happens through accountability. Tell us where your governance programme is stuck and we'll tell you honestly what it would take.

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