Playbook

Data Foundation Playbook

Prepare documents, databases, and internal knowledge for AI agents and SaaS platforms with inventory, classification, permissions, retrieval testing, and governance.

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Briefings. Checklists. Reference architectures.

01Data inventory model

02Classification guide

03Document ingestion workflow

04RAG architecture starter

05Vector search planning

06Permission model

What Is Inside

A practical planning guide for real teams.

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Download Data Foundation Playbook

Best for Data teams, AI teams, Operations teams. Recommended next step: Build an AI Knowledge System.

Data inventory model01

Classification guide02

Document ingestion workflow03

RAG architecture starter04

Vector search planning05

Permission model06

Data quality checklist07

Governance workflow08

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Printable Resource

Data Foundation Playbook

Prepare documents, databases, and internal knowledge for AI agents and SaaS platforms with inventory, classification, permissions, retrieval testing, and governance.

Section 01

Turn scattered data into AI-ready knowledge

AI agents and SaaS platforms need usable data. A strong data foundation connects the right sources, organizes knowledge, applies permissions, and makes retrieval reliable.

Section 02

Data foundation stages

Inventory

Documents

Databases

Cloud drives

CRMs

ERPs

Emails

Spreadsheets

Policies

Contracts

Manuals

Classify

Public

Internal

Confidential

Restricted

Personal information

Financial

Legal

Security-sensitive

Structure and permission

Create metadata for owner, department, date, version, access level, source system, approval status, retention, and AI use approval.

Retrieve and govern

Test source retrieval, source-linked answers, outdated document filtering, restricted document blocking, explainability, review cycles, and user feedback.

Section 03

RAG starter architecture

Document intake

Data cleaning

Chunking

Metadata

Embedding

Vector search

Retrieval rules

AI response layer

Source references

User permissions

Admin dashboard

Monitoring

Section 04

Data quality checklist

The source is known

The owner is known

The latest version is clear

Sensitive information is classified

Permissions are documented

The document is searchable

The content is accurate

The retrieval result is testable

Human review is available

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Map systems, data, workflows, risk, and first AI use cases.

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Related Resources

Continue planning with adjacent guides.

Brief

AI Readiness Brief

Assess your business, data, workflows, infrastructure, security, and governance before adopting AI.

Pattern

Agent Guardrail Pattern

A practical pattern for controlling AI agents with permissions, approvals, logs, tool limits, and human oversight.

Reference

Vision Ops Reference

A reference architecture for computer vision and video intelligence workflows.

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Request the Bassar resource and use the printable HTML version as a working checklist for SaaS, Agentic AI, automation, data foundations, and secure infrastructure planning.

Practical framework

Implementation checklist

Architecture prompts

Next-step planning model

References

Public guidance alignment

This Bassar resource is an original advisory guide. It is not a certification, legal advice, audit evidence, or an official standard. It is designed to help teams structure discovery, planning, and implementation conversations.

Public references used for alignment include NIST AI RMF, NIST CSF 2.0, NIST SSDF, NIST SP 800-207, OWASP Top 10 for LLM Applications, ISO/IEC 42001, PIPEDA guidance from the Office of the Privacy Commissioner of Canada, CISA Zero Trust Maturity Model, and Canadian Centre for Cyber Security cloud guidance.

PIPEDA

PIPEDA requirements in brief

NIST AI RMF

AI Risk Management Framework

NIST CSF 2.0

The NIST Cybersecurity Framework 2.0

NIST SSDF

SP 800-218 Secure Software Development Framework

OWASP GenAI

OWASP Top 10 for LLM and Generative AI Applications

ISO/IEC 42001

ISO/IEC 42001 AI management systems

NIST Zero Trust

SP 800-207 Zero Trust Architecture

CISA Zero Trust

Zero Trust Maturity Model