Library / Artificial Intelligence

AI Security Readiness Assessment

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Learning Objectives — AI Security Readiness Assessment (ASRA)By the end of this course, learners will be able to:1. Identify and classify AI-specific threats and attack techniques Recognize real-world attack scenarios — including prompt injection, model extraction, backdoor attacks, adversarial evasion, and supply chain compromise — and map them to their correct categories in OWASP LLM Top 10 (2025) and MITRE ATLAS frameworks, enabling accurate threat identification during security assessments and customer conversations.2. Apply AI governance and compliance frameworks to real-world deployments Evaluate AI systems against NIST AI RMF (Govern, Map, Measure, Manage), EU AI Act risk classifications, ISO/IEC 42001, and GDPR requirements — and determine the appropriate compliance obligations, risk controls, and documentation needed for each deployment scenario.3. Design and recommend security controls for LLM and agentic AI systems Select the right technical and organizational safeguards — including guardrails, least privilege for AI agents, MLOps pipeline security, multi-tenant isolation, differential privacy, and human-in-the-loop oversight — to protect generative AI and agentic systems across the full deployment lifecycle.4. Conduct and communicate AI security risk assessments with customers Apply a structured methodology to assess an organization's AI security posture, identify gaps across people, process, and technology dimensions, and articulate findings in business-relevant language — building the credibility and technical depth needed to lead presales conversations, PoCs, and executive briefings on AI security.5. Distinguish between privacy-preserving AI techniques — including federated learning, differential privacy, k-anonymity, and synthetic data — and advise organizations on which approach is appropriate given their threat model, regulatory environment, and model utility requirements.

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