Generative AI Safety & Alignment Engineer (GAISE) Certification Program by Tonex
GAISE prepares engineers to design, assess, and ship safer GenAI systems. The program blends alignment theory with practical controls for real-world LLM deployments. You will learn how to specify objectives, evaluate behavior, and implement guardrails that hold under pressure. The curriculum covers objective design, RLHF/RLAIF and constitutional AI, interpretability, adversarial testing, and governance for regulated settings.
GenAI introduces new attack surfaces. Prompt injection, data exfiltration, model abuse, and poisoned inputs can evade traditional controls. GAISE equips you to fuse AI safety with security engineering, reduce breach risk, and protect sensitive data. You will architect threat-aware evaluations, enforce runtime policies, and plan effective incident response.
The course emphasizes rigorous thinking, clear documentation, and traceable decisions. No simulations or labs are required; learning focuses on expert briefings, interactive discussions, guided exercises, and case reviews. By the end, you will produce an actionable safety plan and an evaluation dossier suitable for audit and assurance. Graduates demonstrate the ability to align models with organizational values while preserving utility and performance.
Learning Objectives:
- Define and scope GenAI safety risks across the lifecycle.
- Design alignment objectives and measurable safety metrics.
- Apply interpretability methods to analyze model behavior.
- Build red-teaming strategies and prioritize fixes.
- Implement guardrails and runtime policy enforcement.
- Integrate safety with secure MLOps and governance.
- Plan incident response for safety/security failures.
- Communicate trade-offs to leaders and auditors.
Audience:
- Cybersecurity Professionals
- AI/ML Engineers and Researchers
- Security Architects and DevSecOps Leads
- MLOps/Platform Engineers
- Risk, Compliance, and Privacy Officers
- Product and Engineering Managers
Program Modules:
Module 1: Safety Foundations for GenAI
- Risk taxonomy and safety principles
- Failure modes: jailbreaks, leakage, hallucinations
- Safety-by-design lifecycle
- Safety metrics and acceptance criteria
- Documentation: model cards and safety cases
- Mapping safety to business goals
Module 2: Alignment Methods & Objective Design
- Goal specification and constraints
- Preference modeling fundamentals
- RLHF/RLAIF concepts and pitfalls
- Constitutional AI patterns
- Reward design and reward hacking risks
- Balancing utility, safety, and cost
Module 3: Interpretability & Monitoring
- Probing and attribution basics
- Behavioral testing suites
- Safety classifiers and filters
- Telemetry, drift, and anomaly signals
- Incident triage playbooks
- Evidence capture for audits
Module 4: Adversarial Red Teaming
- Threat modeling for LLM/GenAI
- Jailbreak and prompt-injection patterns
- Data exfiltration and sensitive info risks
- Abuse cases and misuse detection
- Automated attack harness concepts
- Remediation workflows and SLAs
Module 5: Controllability & Guardrails
- System prompts and policy hierarchies
- Function/tool use restrictions
- Identity, role, and rate controls
- Context management and input validation
- Fail-safes and escalation paths
- Human oversight and approval gates
Module 6: Governance, Compliance & Deployment
- NIST AI RMF, ISO/IEC 42001 overview
- Secure pipelines and change control
- Privacy, PII, and data retention
- Evaluation gates and sign-off boards
- Vendor and model supply-chain risk
- Continuous assurance and reporting
Exam Domains:
- Safety Architecture & Risk Management
- Security Threats to GenAI Systems
- Safety Evaluation & Benchmarking
- Responsible Data, Privacy, and Policy
- Operational Readiness & Incident Response
- Regulatory Landscape & Audit Readiness
Course Delivery:
The course is delivered through lectures, expert briefings, interactive discussions, guided exercises, and case-based reviews. Participants receive curated online resources, checklists, and templates for practical application.
Assessment and Certification:
Participants are assessed via quizzes, short assignments, and a capstone safety plan with evaluation evidence. Upon successful completion, participants receive the Generative AI Safety & Alignment Engineer (GAISE)™ certificate.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Generative AI Safety & Alignment Engineer (GAISE) Certification Training exam, candidates must achieve a score of 70% or higher.
Elevate your AI safety practice. Protect users, data, and brand. Enroll in GAISE™ by Tonex and build trustworthy GenAI—at scale.