Strategies in AI Systems From GenAI and Agentic AI

Mastering Security's Semantic Shift
What you'll learn
Explain the fundamental semantic shift in AI security and why traditional security tools and approaches have critical gaps when applied to AI systems.
Recognize and categorize the types of GenAI architectures and assess their relative security risk levels.
Apply the NIST AI Risk Management Framework to structure AI risk management activities
Compare and contrast major AI security frameworks including and determine when to apply each framework.
Identify novel attack surfaces unique to AI systems including prompt injection, model poisoning, data exfiltration through reasoning, and agent exploitation
Evaluate AI systems against regulatory frameworks including the EU AI Act risk classifications and US federal/state requirements
Incorporate AI-specific security considerations into existing cybersecurity programs
Requirements
An understanding of core cybersecurity concepts
Familiarity with Application Security concepts and software development lifecycle
Exposure to AI concepts and an understanding of how AI systems operate
Description
Master AI Security in the Age of Autonomous Systems: The Complete GenAI & Agentic AI Defense StrategyAre you ready to defend against the next generation of AI threats? The attack surface has fundamentally changed—and traditional security is no longer enough.In an era where AI systems autonomously make decisions, generate content, and interact with critical infrastructure, a new paradigm of vulnerabilities has emerged. Welcome to the "Semantic Shift"—where attackers no longer exploit code syntax but manipulate meaning and intent itself.Why This Course Is Essential for Your CareerFor Security Professionals: Traditional application security focused on SQL injection and buffer overflows. Today's threats? Prompt injection attacks that hijack AI reasoning, data poisoning that corrupts model behavior, and cascading failures across multi-agent systems. This course bridges the gap between classic AppSec and the emerging AI threat landscape.For AI/ML Engineers: Building cutting-edge AI systems means nothing if they can be compromised through semantic manipulation. Learn to architect secure-by-design AI applications that withstand real-world adversarial tactics documented in MITRE ATLAS™.For Compliance & Risk Leaders: Navigate the complex web of AI regulations—from the EU AI Act's risk tiers to US Executive Order 14110 and FDA Predetermined Change Control Plans. Transform regulatory requirements into actionable security controls.What Makes This Course DifferentIndustry-Leading Frameworks Integrated:OWASP Top 10 for LLM Applications (2025) – Master the latest vulnerabilities from Prompt Injection to Supply Chain attacksOWASP Top 10 for Agentic AI (ASI) – Learn unique risks in autonomous systems: Agent Goal Hijacking, Tool Misuse, Identity AbuseNIST AI RMF – Implement GOVERN, MAP, MEASURE, MANAGE functions for enterprise-scale AI risk managementMITRE ATLAS™ – Understand real-world ML attack tactics and techniques used by adversariesHands-On with the LLMSecOps Infinity Loop: Go beyond theory with a complete 9-stage secure lifecycle framework covering everything from initial scoping through continuous monitoring—specifically designed for AI systems.Quantify Risk Like Never Before: Learn the groundbreaking AIVSS Scoring System that combines traditional CVSS metrics with the Agentic AI Risk Score (AARS), giving you a standardized way to communicate AI-specific risks to stakeholders and calculate security ROI.Privacy-Enhancing Technologies (PETs) Mastery: Implement cutting-edge protection with Differential Privacy, Federated Learning, Homomorphic Encryption, and Trusted Execution Environments—securing sensitive training data without sacrificing model performance.Real-World Application: The PHAIMIS Case StudyCulminate your learning with an intensive examination of a safety-critical pharmacy inventory system. Apply NIST, MITRE, and OWASP standards to a real-world scenario where AI security failures have direct patient safety implications.Who Should EnrollApplication Security Engineers transitioning to AI securityDevSecOps professionals implementing AI/ML pipelinesAI/ML Engineers responsible for production systemsSecurity Architects designing AI-powered applicationsCompliance Officers navigating AI regulationsRisk Managers quantifying AI system vulnerabilitiesTechnical Leaders building AI security programsCourse Outcomes: What You'll MasterBy completion, you will:Identify and mitigate the OWASP Top 10 for both LLM and Agentic AI applicationsImplement comprehensive AI red teaming across Model, Implementation, System, and Runtime layersNavigate global AI regulations (EU AI Act, US EO 14110, sector-specific mandates)Build an AI Governance structure with CAIO appointment and risk tolerance frameworksDeploy LLMSecOps practices for secure AI development lifecyclesQuantify AI-specific risks using AIVSS and communicate ROI to leadershipArchitect secure multi-agent systems resistant to cascading failuresProtect multimodal systems (VLM, deepfakes) from cross-modal attacksMaintain audit-ready documentation with AIBOM, SBOM, and Model CardsThe Bottom LineTraditional cybersecurity prepared you to defend code. This course prepares you to defend intelligence, autonomy, and meaning—the new attack surface of the AI era.The semantic shift is here. Are you prepared?Enroll now and become the AI security expert your organization desperately needs.
Who this course is for
Application security professionals expanding into AI security
Software developers working with or integrating AI systems
Security engineers and architects responsible for AI deployments
IT risk and compliance professionals overseeing AI initiatives
Product managers and technical leaders implementing AI solutions
Homepage

https://www.udemy.com/course/strategies-in-secure-ai-systems/

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