Certified Security for AI Fundamentals
Certified Security for AI Fundamentals is a practical certification for security, data, risk, compliance, DevOps, and technology leaders responsible for securing enterprise AI adoption. Learn how to protect the data, tools, models, agents, and workflows that power modern AI systems.
100% tool-agnostic training
Learn AI security concepts that apply across public AI tools, embedded SaaS AI, homegrown AI apps, RAG systems, and agentic workflows.
6 in-depth modules
Build fluency across AI architecture, adoption paths, risks, controls, lifecycle stages, maturity modeling, and action planning.
4 weeks to complete
Recommended pace: 3–4 hours per week.
Enroll to become certified in AI Security Fundamentals
Build the expertise to assess and mature your organization’s security posture for AI adoption. This certification covers AI risks, practical controls across the AI lifecycle, and how to align governance with frameworks like NIST AI RMF, ISO 42001, the EU AI Act, and OWASP for LLMs using a maturity-based roadmap.
What you’ll learn
Understand modern AI architecture, including foundation models, LLM engines, RAG indexes, AI services, context layers, and agentic orchestration.
Identify how AI enters the enterprise through Public AI, Embedded AI, and Homegrown AI.
Recognize key AI risks, including shadow AI, prompt injection, data leakage, hallucinations, over-permissioned agents, model drift, and weak auditability.
Map AI risks to controls such as DSPM, AISPM, IAM, AI gateways, DLP, AIDR, audit trails, red teaming, and data provenance.
Apply the 5-stage Security for AI Journey: Discover & Classify, Govern, Protect & Control, Detect & Recover, and Assure.
Use the Security for AI Maturity Model to assess current maturity, define target maturity, and prioritize improvements.
Build phased near-, mid-, and long-term action plans to mature AI security sustainably.
Skills
AI Security Fundamentals
AI Risk Identification
AI Adoption Path Assessment
Shadow AI Discovery
Prompt Injection Awareness
AI Data Security
RAG and Vector Index Risk Management
Agentic AI Risk Management
AI Access Control
AI Runtime Protection
AI Detection & Response
AI Security Maturity Assessment
AI Security Action Planning
Framework Alignment
Cross-Functional AI Security Leadership
Technologies & control domains
DSPM
AISPM
Identity & Access Management
AI Firewalls, DLP, and Gateways
AI Detection & Response
Audit Trail
Data Integrity and Provenance
AI Red Teaming
AI Red Teaming
Model, Agent, and Supply Chain Security
Resilience, Rollback, and Recovery Controls
Concepts & frameworks
NIST AI RMF
ISO/IEC 42001
EU AI Act
OWASP Top 10 for LLMs
AI Security Lifecycle Controls
Enterprise Security, Risk, and Compliance Practices
Outcomes and takeaways
Understand how AI systems are built, deployed, integrated, and secured in enterprise environments.
Distinguish Public AI, Embedded AI, and Homegrown AI, and map each path to data, access, and auditability risks.
Apply the Security for AI Journey to structure ownership, lifecycle controls, incident response, and assurance.
Translate AI risks into control priorities across DSPM, AISPM, IAM, DLP, AI gateways, AIDR, audit trails, red teaming, and provenance.
Assess maturity across the five AI security stages and define a realistic target state.
Build a prioritized roadmap that balances risk reduction, operational feasibility, and long-term maturity.
Lead AI security conversations across security, data, engineering, legal, compliance, and business teams.
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 Security for AI Fundamentals curriculum helps security, data, DevOps, compliance, risk, and business leaders understand how AI changes the enterprise risk surface. Learners gain practical, lifecycle-based methods for securing AI adoption across data, models, tools, agents, and workflows.
Module 0
Introduction to certification
Textual
3 Hours
Understand why AI security is now a critical enterprise discipline. Learn who the certification is for, why AI risk is outpacing traditional governance, and how the course builds the structure, vocabulary, and maturity needed to secure AI responsibly.
Module 1
Origin and taxonomy
Textual
3 Hours
Explore AI’s evolution from symbolic systems to foundation models, copilots, RAG systems, and autonomous agents. Learn core AI terminology and architecture, including models, engines, services, RAG indexes, context layers, and agents.
Module 2
Common AI adoption paths, risk, and controls
Textual
3 Hours
Identify how AI enters the enterprise through Public AI, Embedded AI, and Homegrown AI. Analyze risks such as shadow use, opaque SaaS AI features, self-hosted model exposure, agent overreach, data leakage, and weak auditability.
Module 3
Security for AI journey
Textual
3 Hours
Learn the 5-stage Security for AI Journey: Discover & Classify, Govern, Protect & Control, Detect & Recover, and Assure. Map each stage to AI risks, control domains, governance responsibilities, and leading AI security frameworks.
Module 4
Security for AI maturity modeling
Textual
3 Hours
Assess AI security maturity across the five journey stages. Use the Cyera Security for AI Maturity Model to define current and target maturity, evaluate people, process, and technology capabilities, and set realistic goals.
Module 5
Establishing & prioritizing action plans
Textual
3 Hours
Turn maturity assessment into execution. Create a phased AI security roadmap using near-, mid-, and long-term goals, then apply it to realistic scenarios such as MedSecure Health and BrightTech Solutions.
Earn your
Security for AI Fundamentals
certification!
certification!
Enroll to become certified in Security for AI Fundamentals
Sign up today to build your DSPM foundation and begin your pathway toward Certified Security for AI Fundamentals.
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