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Section 1
Use an AI-specific threat-modeling resource for the stated system
Preview
Preview: 2.0 Securing AI Systems (40%)
Preview includes- 4 of 5 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- Resource Selection
- Attack-Chain Mapping
- Risk Taxonomy Use
- Vulnerability Evidence
Overview
Select the resource that matches the question being asked. SecAI+ explicitly names OWASP LLM Top 10, OWASP ML Security Top 10, the MIT AI Risk Repository, MITRE ATLAS, the CVE AI Working Group, and threat-modeling frameworks.
Core Concepts
- OWASP LLM Top 10 focuses on common security risks in LLM-enabled applications.
- OWASP Machine Learning Security Top 10 is the closer fit for traditional ML pipelines and ML-specific application risks.
- MITRE ATLAS is organized around adversary tactics and techniques; use it to map attacker behavior and attack chains.
Exam Tips
- Read the noun in the stem: LLM application, traditional ML pipeline, attack chain, risk catalog, or vulnerability.
- Do not select a governance framework when the question asks for attacker tactics and techniques.
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Section 2
Use AI to improve threat intelligence and security analysis
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Section 3
Apply AI governance roles, policies, and accountability
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Section 4
Compare AI types and learning techniques used in cybersecurity
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Section 5
Implement model and gateway guardrails
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Section 6
Use AI to support detection, triage, investigation, and response
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Section 7
Implement least-privilege model, data, agent, API, and network access
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Section 8
Map, measure, prioritize, and manage AI risk continuously
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Section 9
Protect AI data in transit, at rest, in use, and throughout its lifecycle
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Section 10
Interpret model training, validation, tuning, and inference decisions
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Section 11
Apply AI to vulnerability management and secure development
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Section 12
Monitor prompts, outputs, logs, costs, quality, and policy compliance
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Section 13
Evaluate privacy, transparency, fairness, safety, and security tradeoffs
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Section 14
Analyze an AI attack and select compensating controls
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Section 15
Use AI-assisted automation with validation and human oversight
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Section 16
Assess data quality, lineage, bias, and feature requirements
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Section 17
Secure the AI software, model, dependency, and data supply chain
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Section 18
Assess third-party AI services, models, data, and contracts
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Section 19
Evaluate AI-generated security output before operational use
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Section 20
Distinguish AI model, application, agent, and supporting infrastructure responsibilities
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Section 21
Protect sensitive security data supplied to an AI tool
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Section 22
Document testing, limitations, incidents, decisions, and residual risk
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Section 23
Align AI controls and evidence to applicable frameworks and obligations
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