AI Security
Develop the skills to assess a controlled AI application for trust-boundary failures through a learning path centred on AI Security. Explore concepts, guided practice and an applied project.
Build practical AI Security skills.
Skills and knowledge developed in this course.
- AI threat modelling
- Input and context risks
- Tool and infrastructure controls
- Security validation
- Applied project: Assess a controlled AI application for trust-boundary failures
- AI Security — validation and review
About this course
This course develops practical capability in AI Security. Its course-specific content covers AI threat modelling, Input and context risks, Tool and infrastructure controls, Security validation, followed by an applied project focused on assess a controlled ai application for trust-boundary failures. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with AI Security or plan to take on responsibilities in this area.
Before you start
Working knowledge of artificial intelligence & generative ai and practical familiarity with the named platform or subject. Review the course topics before enrolment to confirm the lab starting point.
Assess a controlled AI application for trust-boundary failures
Deliver assess a controlled ai application for trust-boundary failures. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01AI threat modelling
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- Model, data and tool attack surfaces
- Asset ownership and authorised assessment scope
Practical exerciseApply ai threat modelling to the assess a controlled ai application for trust-boundary failures and capture the validation evidence.
02Input and context risks
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- Prompt injection and untrusted retrieval
- Data isolation and output validation
Practical exerciseApply input and context risks to the assess a controlled ai application for trust-boundary failures and capture the validation evidence.
03Tool and infrastructure controls
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- Permission-scoped execution and approvals
- Secret handling and dependency integrity
Practical exerciseApply tool and infrastructure controls to the assess a controlled ai application for trust-boundary failures and capture the validation evidence.
04Security validation
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- Abuse-case tests and evidence collection
- Mitigations, monitoring and residual-risk reporting
Practical exerciseApply security validation to the assess a controlled ai application for trust-boundary failures and capture the validation evidence.
05Applied project: Assess a controlled AI application for trust-boundary failures
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- Define inputs, assumptions and acceptance criteria for: Assess a controlled AI application for trust-boundary failures.
- Implement the project using ai threat modelling, input and context risks, tool and infrastructure controls, security validation.
Practical exerciseAssess a controlled AI application for trust-boundary failures.
06AI Security — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
AI threat modelling · Input and context risks
Establish the AI Security scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Tool and infrastructure controls · Security validation
Apply the core AI Security techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for assess a controlled ai application for trust-boundary failures. - 04Learning milestone 4 In progress
Deliver the applied project
Complete assess a controlled ai application for trust-boundary failures, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗