GOVERN AI BEFORE RISK SCALES

AI Governance for Responsible Enterprise AI

Inventory every AI system, classify risk, enforce guardrails, and prove compliance—from first experiment to production.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Trusted by data leaders across Banking, Insurance & Telecom

Koinworks-logounravel-carbon-logosightly-logosightly-logoxepelin-logocompany-logoKollect-logofloward-logoadda-247-logo
sightly-logounravel-carbon-logoKollect-logoxepelin-logosightly-logoKoinworks-logoadda-247-logofloward-logocompany-logo
sightly-logounravel-carbon-logoKollect-logoxepelin-logosightly-logoKoinworks-logoadda-247-logofloward-logocompany-logo

THE GOVERNANCE GAP

AI is moving faster than you can govern it.

AI systems are multiplying across teams, vendors, and workflows. Without a shared inventory, risk model, and evidence trail, every review becomes a manual fire drill.

"We run Snowflake, Databricks, and three BI tools. And our Chief Risk Officer still can't tell the regulator where our PII sits, or prove which version of Net Revenue is authoritative. Every audit is a month of pain."

Head of Data Governance, Tier 1 financial institution

Unknown AI exposure

Models, copilots, and agents are being adopted without a complete inventory or accountable owner.

Inconsistent risk decisions

Teams assess similar use cases differently, leaving controls dependent on judgement and spreadsheets.

Evidence scattered everywhere

Approvals, assessments, policies, and test results live across tickets, documents, and inboxes.

Point-in-time compliance

Risk changes after launch, but most governance processes stop once an AI system reaches production.

Build trust in your data.

Automatically Classify Sensitive Data and PII with Customizable Policies

Automatically identify and classify sensitive data and PII using customizable, predefined policies, or categorize assets manually in the catalog, so governance and control over critical information stays consistent across your stack.

Streamline Data Governance with Advanced Automation, Classification, and Tagging

Our advanced Governance module automates the management and protection of your most valuable data assets, ensuring robust security, regulatory compliance, and data privacy. With intelligent classification and tagging, your organization can streamline governance processes and stay ahead of evolving compliance requirements.

Automate the management and protection of your most valuable data assets with intelligent classification and tagging, so your team keeps up with evolving compliance requirements without manual governance work.

Data Stewardship, Ownership, and Approval Workflows

All changes and requests within these modules are subject to an intuitive approval workflow, ensuring full oversight and control before implementation. This process safeguards your data governance policies, promoting accountability and minimizing the risk of unauthorized modifications.

Assign stewards and owners to data assets and terms, and route every change and access request through an approval workflow, so accountability is clear and no critical data element goes unowned.

Tailored Access Controls for Precise Asset Management

Decube’s workspace enforces robust access controls by assigning user permissions to specific groups, ensuring only authorized personnel can access sensitive data. This granular approach to asset management safeguards your data, promoting both security and compliance across your organization

Field-Level Access Governance and RBAC

Implement precise access controls that allow you to restrict user access to specific data assets, rather than broad access to the entire source. This granular approach enhances data security, ensuring that users only interact with the information they are authorized to handle.

Assign role-based permissions and restrict access to specific data assets and fields, not just whole sources, so only authorized people can see sensitive data and every access change is controlled and auditable.

ONE GOVERNANCE CONTROL PLANE

Know your AI. Govern it with confidence.

Connect AI inventory, data context, risk, policy, and evidence in one place—so governance becomes part of delivery, not a gate at the end.

AI registry

Discover and register every model, agent, use case, vendor, and dataset in one governed inventory.

Automated risk classification

Score AI systems against business impact, data sensitivity, autonomy, and regulatory exposure.

Policy guardrails

Turn governance principles into enforceable controls, approval gates, and evidence requirements.

AI lineage & context

Trace each AI system to its models, prompts, datasets, owners, decisions, and downstream applications.

Continuous oversight

Monitor risk posture, control status, incidents, drift signals, and expiring approvals throughout the lifecycle.

Audit-ready evidence

Generate a defensible record of assessments, approvals, controls, and change history for every AI system.

Connect

Your existing stack — cloud, lakehouse, warehouse and APIs.

Data stack
LLM Models
AI Apps
Discover
Data Catalog Column-level Lineage Full Context
Observe
Data Quality Data Health Incident Alerts
Govern
Policies Access Compliance
Act

Delivered to your teams, AI agents, and audits.

All Teams
Your AI Agents
Audit Reports

FROM DISCOVERY TO EVIDENCE

Govern AI in four connected steps

A repeatable operating model that moves at the speed of your AI portfolio.

Inventory

Auto-discover AI assets and capture ownership, purpose, users, and dependencies.

Assess

Classify risk and map applicable controls using one consistent assessment workflow.

Govern

Route reviews, enforce policy gates, and assign remediation with clear accountability.

Prove

Maintain continuous evidence and produce audit-ready reports on demand.

Built for every team responsible for AI

Built for regulated AI teams accountable for sensitive data, where a governance gap turns into a compliance problem.

AI Governance

Define the operating model, policies, control library, and reporting cadence across every AI initiative.

Blue tick icon

Portfolio-wide AI visibility

Blue tick icon

Reusable policy and controls

Blue tick icon

Executive risk reporting

Risk & Compliance

See which regulations apply, where controls are missing, and what evidence supports each decision.

Blue tick icon

Consistent risk classification

Blue tick icon

Traceable approvals

Blue tick icon

Audit-ready evidence

Data & AI Teams

Ship responsibly with clear requirements, reusable assessments, and fewer late-stage review surprises.

Blue tick icon

Clear launch requirements

Blue tick icon

Integrated review workflow

Blue tick icon

Governed data context

Trusted by organizations operating under OJK, BNM, MAS, and APRA regulatory frameworks across APAC.

Amet minim mollit non deserunt ullamco est sit aliqua dolor do amet sint. Velit officia conseq uat duis enim velit mollit.

Trusted by Governance teams across industries

Rated 4.6/5 on

Your data never leaves your environment

decube discovery icon
SOC 2 Compliant

Safeguarding your information with industry-leading standards.

decube discovery icon
ISO 27001

Ensuring your information is protected with the highest level of integrity.

decube discovery icon
HIPAA Compliant

Ensuring the confidentiality and integrity of your healthcare data.

decube discovery icon
GDPR Compliant

Protecting personal data with robust privacy and security measures.

decube discovery icon
Encryption

Your data is encrypted in motion with TLS and at rest with AES-256.

Our partners

Frequently asked questions

What is data governance and why is it important?

Data governance is the practice of managing data availability, usability, integrity, and security across an organization. It ensures that data is trustworthy and consistent so that business decisions and AI initiatives are based on reliable information.

What are the key components of a data governance framework?

A strong data governance framework typically includes data ownership, data quality management, metadata management, data lineage, business glossary, and access control. Together, these components create a foundation of trust in enterprise data.

How does data governance support AI and machine learning initiatives?

AI systems are only as good as the data they consume. Data governance ensures data is accurate, consistent, and contextualized—helping organizations achieve higher ROI from AI and reducing the risk of biased or incorrect outputs.

What challenges do companies face in implementing data governance?

Common challenges include siloed data systems, lack of clear data ownership, inconsistent policies, and resistance from business teams. Modern platforms help simplify governance by automating metadata capture, lineage, and quality checks.

How is data governance different from data management?

Data management focuses on the technical handling of data (storage, integration, processing), while data governance defines the rules, roles, and policies that guide how data should be used responsibly and effectively.

Who is responsible for data governance in an organization?

Data governance involves collaboration between multiple stakeholders: data stewards, data engineers, business analysts, compliance officers, and executives. Increasingly, organizations are forming Data Governance Councils to drive accountability.

What tools or technologies can help streamline data governance?

Modern data governance tools unify cataloging, lineage tracking, observability, and business glossaries in one platform. Decube does this on a metadata-only architecture, so classification, access control, stewardship, and column-level lineage all work against one connected view of your data.

Is Decube a data governance tool or a full platform?

Both. Decube is a unified data trust platform, so governance runs on the same foundation as its catalog, lineage, and observability. Classification, access, stewardship, and compliance all work against one connected view of your data.

Does our data leave our environment when we use Decube?

No. Decube uses a metadata-only, query-pushdown architecture, so your data never leaves your environment. This is why regulated banks trust Decube for governance.

Does Decube's data governance work for regulated industries like financial services (for example BCBS 239)?

Yes. Banks and regulated enterprises use Decube to define critical data elements, enforce GDPR/HIPAA/BCBS 239-style policies, and prove data derivation with column-level lineage.

All in one place

Comprehensive and centralized solution for data governance, and observability.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
decube all in one image