Advanced Security for AI Systems
Equip participants with the knowledge and skills to architect, implement, and govern secure AI systems against advanced adversarial threats.
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Audience: AI/ML Engineers and Architects • Security Engineers supporting AI deployments • DevSecOps Professionals • Technical Leads implementing AI solutions • Security Researchers and Consultants
Duration: 3 days (24 hours)
Format: Lectures and hands-on labs
Overview
AI systems introduce unique and evolving security challenges that go far beyond traditional cybersecurity. This advanced 3-day course equips engineers, architects, and security professionals with the skills to identify, model, and mitigate threats specific to GenAI and Agentic AI systems. From prompt injection defenses and OWASP LLM Top 10 to zero-trust agent architectures and AI governance frameworks, participants gain deep technical expertise in securing the full AI lifecycle.
Objective
Equip participants with the knowledge and skills to architect, implement, and govern secure AI systems against advanced adversarial threats.
What You Will Learn
- Identify and mitigate advanced AI-specific security threats
- Develop structured threat models for GenAI and Agentic AI systems
- Design robust guardrails for LLM-based applications
- Analyze prompt injection and jailbreak attack techniques
- Architect secure AI data pipelines and model deployments
- Apply secure-by-design principles to AI system architecture
- Implement zero-trust frameworks for autonomous AI agents
- Establish monitoring, auditing, and governance strategies for AI systems
Course Details
Audience: AI/ML Engineers and Architects • Security Engineers supporting AI deployments • DevSecOps Professionals • Technical Leads implementing AI solutions • Security Researchers and Consultants
Duration: 3 days (24 hours)
Format: Lectures and hands-on labs
- Foundational understanding of Machine Learning and Large Language Models (LLMs)
- Familiarity with API security and authentication
- General cybersecurity knowledge
- Working knowledge of software development principles
Setup: Zero-install cloud lab • Laptop with unrestricted Internet • Chrome browser
Detailed Outline
- AI Security vs Traditional Cybersecurity: emergent vulnerabilities, expanded attack surfaces, dynamic threat models
- The Current Threat Environment: state-sponsored AI weaponization, sophisticated adversaries, AI breach case studies, economic & reputational impact
- Paradigm Shift in AI Security: content-aware security models, trust boundaries in multi-agent systems, model emergence and scheming risks
- LLM Application Security Architecture: secure RAG patterns, vector database security, embedding poisoning prevention, context window security
- OWASP Top 10 for LLM Applications: Prompt Injection, Sensitive Information Disclosure, Supply Chain Vulnerabilities, Data & Model Poisoning, Improper Output Handling, Excessive Agency, System Prompt Leakage, Vector & Embedding Weaknesses, Misinformation Exploitation, Unbounded Consumption
- Secure Design Patterns: input validation & sanitization, output filtering & moderation layers, rate limiting, stateless vs stateful agent design
- Prompt Injection Taxonomy: direct vs indirect injection, multi-turn attacks, cross-agent injection, time-delayed payloads
- Advanced Attack Techniques: system prompt extraction, jailbreaking, multi-language injection, multimodal injection vectors
- Defense-in-Depth Strategies: prompt detection frameworks, structured output enforcement, context isolation & sandboxing, adversarial training, continuous red-team testing
- HarmBench Framework: automated red-teaming, benchmarking attack resistance, continuous testing pipelines
- Agentic AI Security Risks: autonomous decision-making exposure, tool & API access control, multi-agent communication risks, emergent behaviors
- AI Scheming and Self-Preservation: goal misalignment, reward hacking, oversight evasion, model theft & sabotage
- Secure Agent Architecture: isolation & sandboxing, tool governance & least privilege, behavioral monitoring, emergency kill switches
- Zero-Trust for AI Agents: continuous verification, micro-segmentation, provenance tracking, attestation mechanisms
- Threat Modeling Frameworks: STRIDE adapted for AI, PASTA methodology, MITRE ATLAS, AI attack trees
- Attack Surface Analysis: training pipeline risks, model serving endpoints, data preprocessing vulnerabilities, third-party dependencies
- Data Poisoning Threats: backdoor injections, adversarial data insertion, label flipping, clean-label poisoning
- Model Extraction and Inversion: model stealing, membership inference, training data reconstruction, IP theft risks
- Adversarial ML Attacks: evasion techniques, Byzantine attacks, Sybil attacks in federated systems
- Guardrail Architecture Patterns: pre-processing controls, in-processing constraints, post-processing filters, feedback loops
- Content Safety and Moderation: toxicity filtering, PII detection & redaction, malicious payload sanitization, cultural safeguards
- Implementation Frameworks: AWS Bedrock Guardrails, Azure AI Content Safety, Guardrails AI, NVIDIA NeMo Guardrails
- Monitoring and Adaptation: real-time anomaly detection, feedback-driven tuning, A/B testing of safety controls
- Data Pipeline Security: data provenance & lineage, encryption at rest & in transit, governance & access control, differential privacy
- Model Security: model signing & verification, secure storage & versioning, supply chain integrity
- MLOps Security: secure CI/CD, container security, secrets management, deployment risk management
- Monitoring and Observability: security logging, drift detection, inference anomaly monitoring
- REST API Security: OAuth 2.0 & OpenID Connect, JWT validation, rate limiting, key management
- GraphQL Security: query complexity limits, resolver-level authorization
- gRPC Security: mTLS authentication, interceptor-based controls
- AI-Specific API Attacks: token abuse, cost amplification, model extraction, cache poisoning
- Defense-in-Depth Architecture: layered controls, incident response playbooks, security orchestration
- Adversarial Training and Red Teaming: robust model development, purple team exercises, continuous validation
- Secure Design Principles: least privilege, complete mediation, secure defaults
- Privacy-Preserving AI: differential privacy, federated learning security, confidential computing
- Regulatory Landscape: EU AI Act, NIST AI Risk Management Framework, ISO/IEC 42001, GDPR & CCPA
- Governance Frameworks: AI security policies, vendor risk management, incident response planning, audit trails
- Ethics and Responsible AI Security: transparency vs security, responsible disclosure, fairness considerations
- Quantum computing implications
- Multimodal attack vectors
- Deepfake detection
- Autonomous AI malware
- AI-powered security operations
- Enterprise multi-agent RAG architectures
- STRIDE-AI and MITRE ATLAS mapping
- Guardrails implementation strategies
- Incident response simulations
- Governance and compliance models
Ready to Get Started?
Contact us to learn more about this course and schedule your training.