Certified AI Security & Adversarial Defense Specialist (C-AISADS) Certification Program by Tonex
AI systems change how organizations deliver value—and how attackers operate. This program prepares you to secure models, data pipelines, and AI-enabled applications across the full lifecycle. You will learn adversarial ML tactics and practical defenses, from robust training to runtime monitoring and incident response. You will design actionable threat models, stress-test systems with structured red teaming, and harden operations with blue team controls.
The curriculum aligns security engineering with governance so teams can ship AI responsibly, meet regulatory expectations, and reduce risk at scale. Outcomes are concrete: stronger model robustness, tighter access and data controls, faster detection, and cleaner rollback paths. Participants leave with patterns, playbooks, and metrics that fit real production constraints. The impact on cybersecurity is direct—less model drift exposure, fewer data leakage paths, and measurable resilience against evasion, poisoning, extraction, and prompt-based attacks.
Learning Objectives:
- Identify AI attack surfaces and adversary capabilities.
- Build threat models for ML pipelines and LLM applications.
- Apply defenses to mitigate evasion, poisoning, and prompt attacks.
- Engineer secure MLOps with controls, gates, and audits.
- Detect abuse with monitoring, telemetry, and anomaly signals.
- Orchestrate red/blue exercises and drive remediation.
Audience:
- Cybersecurity Professionals
- Security Architects and Engineers
- AI/ML Engineers and MLOps Leads
- Blue/Red Team Practitioners
- Risk, Compliance, and Governance Leaders
- Product and Platform Owners
Program Modules:
Module 1: Foundations of Secure AI
- AI threat surface and attacker goals
- Attack taxonomy: evasion, poisoning, extraction, inference
- Security patterns for data, model, and serving layers
- Secure SDLC for ML and LLM apps
- Risk frameworks (NIST AI RMF, ISO/IEC landscape)
- Baseline controls and hardening checklist
Module 2: Adversarial Machine Learning
- Crafting perturbations and evaluating robustness
- Robust training, regularization, and ensembling
- Verification and certified defenses (overview)
- OOD detection and distribution shift handling
- Privacy risks: membership inference and DP mitigations
- Robustness metrics and evaluation pipelines
Module 3: Threat Modeling for AI Systems
- System and data-flow mapping for ML stacks
- Assets, trust boundaries, and supply chain risks
- STRIDE and attack trees adapted for ML/LLM
- Abuse/misuse cases and safety constraints
- Risk scoring, prioritization, and controls selection
- Model/system cards with risk annotations
Module 4: Red Teaming AI
- Exercise design, scope, and rules of engagement
- Prompt injection, jailbreaks, and tool-use abuse
- Data poisoning and content manipulation scenarios
- Model extraction and API abuse pathways
- Attack tooling, fuzzing, and automation basics
- Reporting, severities, and fix-tracking playbooks
Module 5: Blue Teaming & Defense Operations
- Telemetry for data, model, and inference layers
- Drift, canaries, and anomaly detection strategies
- Runtime safeguards: filters, rate limits, policies
- Incident response for AI failures and abuse
- Secrets, keys, and access control hygiene
- Secure MLOps: rollout gates and rollback plans
Module 6: Governance, Compliance, and Enterprise Fit
- Policy guardrails and risk acceptance criteria
- Regulatory outlook (e.g., EU AI Act) and mapping
- Third-party/vendor model risk management
- Secure deployment patterns and reference architectures
- KPIs, ROI, and executive reporting
- Adoption roadmaps and operating models
Exam Domains:
- AI Risk Governance & Compliance
- Adversarial Attack Analysis & Testing
- Secure ML Engineering & MLOps Controls
- AI Threat Modeling & Architecture Defense
- Detection, Monitoring, and Incident Response for AI
- Red/Blue Team Strategy and Remediation
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in the field of Certified AI Security & Adversarial Defense Specialist (C-AISADS). Participants will have access to online resources, including readings, case studies, and tools for guided exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified AI Security & Adversarial Defense Specialist (C-AISADS).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI Security & Adversarial Defense Specialist (C-AISADS) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to harden your AI stack? Enroll now to build robust defenses and align teams on secure AI. Bring your team to accelerate adoption with confidence.