Certified AI Data Trust Architect
Certified AI Data Trust Architect is a vendor-neutral certification that helps security, governance, risk, compliance, data, legal, and technology leaders understand how to build trust in AI by proving the trustworthiness, provenance, and integrity of the data AI systems use.
100% Vendor-Neutral Training
Learn practical AI Data Trust concepts that apply across tools, vendors, data environments, and AI architectures.
7 In-Depth Modules
Build a practical foundation across trust decisions, provenance, rules, data trust targets, compliance proof, and trust validation.
4 weeks to complete
Recommended pace: 3–4 hours per week.
Enroll to Become a Certified AI Data Trust Architect
Build the expertise needed to understand, design, and mature trusted data practices for AI systems with confidence.This certification helps you understand why AI systems can only be trusted when the data they use can be trusted. You will learn how provenance, rules, decisional information, trust scores, and evidence-based controls help organizations move from presumed trust to calculated, measurable AI Data Trust.
What you’ll learn
Explain why AI adoption has created a crisis in trust across data assets, synthetic content, AI outputs, and business decisions.
Understand trust as a calculated, evidence-based decision that begins at zero and requires measurable proof before reliance.
Use the AI Data Trust Decision Model to define AI agents, AI Trust Targets, data boundaries, rules, Data Trust Targets, and time-based recalculation.
Apply the Unified Rules Model to organize laws, regulations, policies, standards, access controls, and execution-layer requirements for trusted AI data use.
Apply the Unified Information Model to identify provenance, activity history, evaluative signals, navigational evidence, and content validation needed to trust data.
Understand how provenance, transparency, and trustworthiness are becoming legal, operational, and competitive requirements for AI systems.
Translate broad trust requirements into machine-checkable rules using Rules for Composing Rules and Compliance Proof.
Validate whether AI-visible files, records, images, and datasets are trustworthy enough for AI use through provenance validation, integrity checks, source trust, and trust scoring.
Skills & tools
AI Data Trust
Data Provenance
Trust-by-Design
Zero-Trust Data Governance
AI Trust Decisioning
Data Trust Target Design
AI Data Boundary Design
Provenance Validation
Trust Scoring
Data Integrity Assessment
Evidence-Based Governance
AI Data Risk Analysis
Compliance Proof
Trust Maturity Planning
Frameworks, models & standards
AI Data Trust Decision Model
Trust Decision Model
Unified Rules Model
Unified Information Model
Rules for Composing Rules
Compliance Proof
NIST AI RMF
ISO/IEC 42001 and related AI standards
EU AI Act
EU GPAI Code of Practice concepts
OASIS / Data & Trust AI Alliance provenance concepts
Data Provenance Standards
ISO/IEC trust validation concepts
Outcomes and takeaways
Build a practical vocabulary for explaining trust, provenance, decisional information, Trust Decision Targets, Data Trust Targets, and AI Trust Targets.
Understand how AI trust depends on proving the trustworthiness of the data, sources, rules, evidence, and controls behind AI decisions.
Create structured AI Data Trust profiles that define what data an AI agent may see, must not see, or may only use conditionally.
Translate legal, regulatory, policy, and technical requirements into measurable data trust rules and execution controls.
Identify the provenance and trust evidence needed to determine whether a file, record, dataset, source, or output is fit for AI use.
Understand how provenance and evidence can increase the velocity, usability, and economic value of trusted data.
Understand how provenance and evidence can increase the velocity, usability, and economic value of trusted data.
5000+ community of AI defenders
“What stood out most was the practical approach to AI security fundamentals and the clear framework for understanding governance, risk, and security maturity.”
Oksana Riabichko
Vice President, North America
"The course is well put-together and covers everything one needs to know about AI Security. Great job! Kudos to everyone who worked hard to put this training materials and certification exam together. I will be proudly displaying my badge anywhere I can!"
May Ledesma
Educator, Systems Analyst, Silicon Labs
"Very informative introduction into Data Lifecycle Management and Data Secure Posture Management."
Kapil Choudhary
Chief Manager, State Bank of India
“Most certifications add letters after your name, the DSPM Architect credential adds real leverage. It hands you a battle-tested playbook for discovering and locking down sensitive data across sprawling cloud and SaaS estates, so you can translate risk into business terms that resonate from the boardroom to the dev squad.”
Ari Harrison
Director of IT, BAMKO
“I genuinely believe this certification fills a critical gap in today’s security landscape. It gave me the structure and language to lead data security initiatives confidently—across teams, tools, and business units. Whether you're hands‑on or leading strategy, it’s one of the most practical, forward‑looking certifications I’ve seen.”
Amy Mayo
CyberSecurity Analyst, Finance of America
Expert-Led Curriculum
The Certified AI Data Trust Architect curriculum is designed to help security, governance, legal, compliance, risk, data, and technology leaders understand how trusted AI begins with trusted data.
Learners move from the trust problem to practical trust models, rules, evidence, provenance, validation, and maturity planning. The course introduces a structured operating language and a set of reusable tools for designing AI Data Trust across enterprise environments.
Module 0
Introduction to the Certification
Textual
3 Hours
Establish the purpose of the certification, introduce AI Data Trust as a discipline, explain the learning journey, and frame why trusted AI begins with trusted data, provenance, and evidence.
Module 1
Confronting a Crisis in Trust
Textual
3 Hours
Explore why AI, synthetic content, persistent data, and business decisions now face a crisis in trust. Learn why trust is not an emotion, but a calculated decision that begins at zero and requires measurable evidence.
Module 2
The AI Data Trust Decision Model
Textual
3 Hours
Learn how to structure AI trust decisions using the AI Data Trust Decision Model. Define AI agents, AI Trust Targets, rules, data boundaries, Data Trust Targets, and time-based auditability.
Module 3
Discovering the Building Blocks
Textual
3 Hours
Use the Unified Rules Model and Unified Information Model to translate trust principles into practical design inventories. Learn how rules define AI visibility and how information, provenance, and evidence prove whether visible data is trustworthy.
Module 4
The 21st Century Rules for Provenance
Textual
3 Hours
Understand why provenance is becoming a legal, operational, and competitive requirement. Explore how external laws, standards, transparency obligations, and provenance metadata shape AI Data Trust design.
Module 5
Building the Rules for AI Data Trust
Textual
3 Hours
Turn broad trust requirements into enforceable controls. Learn how to use Rules for Composing Rules and Compliance Proof to convert ambiguity into measurable, machine-checkable, auditable AI Data Trust rules.
Module 6
Validating AI Data Trust in Practice
Textual
3 Hours
Learn how to prove whether AI-visible files, records, datasets, and images are trustworthy enough for AI use. Validate provenance, integrity, source trust, peer evidence, trust scores, and recalculation triggers.
Earn Your
AI Data Trust Architect Certification!
AI Data Trust Architect Certification!
Enroll to Become a Certified
AI Data Trust Architect
Sign up today to gain the expertise and recognition needed to build, explain, and mature trusted data practices for AI systems. Learn how to move beyond assumed trust and build AI Data Trust using provenance, rules, evidence, validation, and measurable trust decisions.
.svg)
