AI-Enabled Systems Software Safety & Assurance Essentials Training by Tonex
Modern intelligent software changes faster than requirements can be written. This program gives safety and engineering leaders a rigorous, practical blueprint for assuring AI-enabled systems where learning behavior, data shifts, and autonomy complicate traditional safety cases. You will translate hazard analysis, testing, and governance into artifacts that satisfy auditors and program risk gates.
Cybersecurity ramifications are treated as first-order safety concerns, from adversarial manipulation and model theft to data integrity risks. You will connect threat models to safety goals, align controls with secure-by-design practices, and structure continuous assurance for deployed AI in contested environments.
Learning Objectives
- Distinguish deterministic and non-deterministic safety behaviors in AI software
- Map operational design domain to AI hazards, loss scenarios, and controls
- Identify failure modes including bias, drift, and data quality weaknesses
- Design verification strategies for ML performance, robustness, and stability
- Build assurance arguments and evidence across the lifecycle and toolchain
- Integrate cybersecurity into safety risk reduction with traceable controls
Audience
- Safety and assurance leads
- Software and ML engineers
- Systems and mission assurance managers
- Test and validation specialists
- Product and compliance owners
- Cybersecurity Professionals
Course Modules
Module 1 – Foundations
- Safety vs assurance scope
- AI system lifecycle mapping
- ODD and mission context
- Risk taxonomy alignment
- Safety goals and constraints
- Evidence and traceability
Module 2 – Determinism & Behavior
- Deterministic assumptions limits
- Learning components interfaces
- Runtime non-determinism sources
- Data dependency risks
- Requirement patterns for AI
- Contracts and guardrails
Module 3 – Hazard & Failure Modes
- Hazard analysis for AI
- STPA and HAZOP adaptations
- Bias and unfair outcomes
- Concept drift and data drift
- Human factors and misuse
- Degradation and fallback design
Module 4 – Testing & Validation
- Test oracles for ML outputs
- Dataset design and coverage
- Robustness and stress testing
- Performance monitoring metrics
- Model change management
- Continuous validation pipeline
Module 5 – Security & Adversaries
- Threat modeling for AI
- Adversarial input attacks
- Poisoning and data integrity
- Model extraction and IP risk
- Secure-by-design mitigations
- Security-safety interaction tracking
Module 6 – Assurance & Governance
- Explainability and transparency
- Human oversight and handover
- Assurance case construction
- Evidence packages and audits
- NIST AI RMF alignment
- ISO 42001 and EU AI Act mapping
Ready to operationalize trustworthy AI at scale with defensible safety and assurance artifacts Join Tonex to equip your team with methods, templates, and practices that satisfy regulators and program risk owners while accelerating delivery.