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Securing Enterprise AI: A Vendor-Agnostic Method
New
2 students
Securing Enterprise AI: A Vendor-Agnostic Method
Assess, control, red team and govern enterprise AI and agents using the AI Control Architecture and RCDM
Created byAnkush Chowdhary
Last updated 10/2026
English
English
What you'll learn
- Inventory your AI estate, including shadow AI, and assess each system by what it can see, decide and do
- Assign one of five risk tiers to any AI use case and select the controls each tier requires
- Design controls across the ten pillars, from data boundaries and access to containment and recovery
- Threat model AI systems and run safe, evidence-based red team tests against live AI
- Govern autonomous AI agents at runtime by securing the action, not the actor, using Recursive Cyber Defense Model
- Map your AI supply chain and build an AI bill of materials for the models, data and tools you depend on
- Produce audit-ready evidence that proves AI controls work, moving from declared to enforced
- Apply one vendor-agnostic method to any AI stack, whether it's your own code, a cloud agent service or a SaaS product
Explore related topics
Course content
19 sections • 74 lectures • 7h 0m total length
Requirements
- A general familiarity with IT or cybersecurity concepts is helpful, but you don't need to be a security specialist
- No prior AI or AI security experience is needed: every term is explained in plain language as you go
- No coding or technical setup is required, and no specific vendor tools or products
- A curiosity about how AI is used in your organisation, and the risks that come with it
Description
AI doesn't break your security model because it's clever. It breaks it because it's given authority: to see data, influence decisions and take actions, often faster than any human can review.
This course teaches a complete, vendor-agnostic method for securing enterprise AI. It's built on two frameworks I developed, the AI Control Architecture (ACA) and the Recursive Cyber Defense Model (RCDM), and on 30 years building security for some of the most regulated and complex environments there are.
You'll follow one realistic AI system, a customer refund agent, through the whole method:
Assess: find every AI in your estate and judge its risk by what it can see, decide and do, then assign one of five risk tiers.
Control: apply the ten control pillars and turn risk into specific controls you can build on any stack.
Attack: threat model AI, red team it step by step, and test live systems safely.
Runtime: govern autonomous agents at machine speed by securing the action rather than trusting the actor, and earn autonomy safely through Shadow Mode.
Supply chain: understand what your AI is really made of, build an AI bill of materials, and run evidence-based vendor due diligence.
Prove: produce evidence that controls work, not just that they exist, and report it in terms a board understands.
Every module includes a worksheet and a quiz, and the course ends with a graded capstone. No product is required. The method works with whatever stack you run: your own code, a cloud agent service, or a SaaS product.
About AI use in this course: course materials were developed with AI assistance and reviewed and refined by the instructor. All narration is recorded by the instructor in his own voice.
Who this course is for:
- Security architects and engineers responsible for securing AI systems and agents in their organisation
- CISOs and security leaders who need a defensible, practical program for controlling AI adoption
- Risk, compliance, governance and audit professionals who assess AI and need evidence that controls actually work
- AI, ML and platform engineers who deploy models, copilots and agents and want to build them securely
- Consultants and advisors who help clients secure and govern enterprise AI
- IT and security professionals moving into AI security who want a structured method from the ground up