Certified AI Forensics and Incident Response Specialist (CAFIRS)

Certified AI Forensics and Incident Response Specialist (CAFIRS) Certification Program by Tonex

Duration: 2 Days | Format: In-Person / Virtual / Hybrid
Level: Advanced – Cybersecurity, AI, Forensics
Credential: Certification + Exam + Digital Badge

The CAFIRS certification is designed for digital forensics professionals, cybersecurity analysts, and AI/ML engineers who need to investigate, mitigate, and report incidents involving AI systems. This includes forensic analysis of AI model misuse, adversarial manipulation, data poisoning, LLM-based attack surfaces (e.g. prompt injection), model theft, and supply chain compromise in AI pipelines.

CAFIRS is aligned with NIST AI RMF, OWASP LLM Top 10, MITRE ATLAS, and ISO/IEC 27041.

Learning Objectives

By the end of this certification, participants will be able to:

• Investigate AI/ML systems for misuse, compromise, or adversarial manipulation.
• Identify and respond to incidents involving LLM prompt injection, RAG poisoning, or unauthorized model access.
• Perform forensic analysis on AI models, vector stores, training datasets, and inference APIs.
• Apply AI-specific threat models (e.g., ATLAS, OWASP LLM Top 10) to real-world incidents.
• Collect evidence from AI pipelines, inference endpoints, and MLOps environments.
• Document and report AI-related incidents in compliance with NIST and ISO frameworks.
• Coordinate with red and blue teams during incidents involving AI misuse or compromise.

Target Audience:

• Digital forensics and incident response (DFIR) teams
• AI/ML engineers in high-risk or regulated environments
• Cybersecurity professionals in SOCs and CERTs
• AI red/blue/purple teams
• Threat hunters and intel analysts
• Cloud and MLOps security engineers
• Risk, audit, and governance personnel handling AI assurance

Program Agenda & Modules:

1 – Foundations of AI Forensics & Threat Modeling

• Anatomy of an AI system (model, data, inference, storage, MLOps)
• AI incident types: model exfiltration, RAG prompt injection, adversarial inputs, training data leaks
• MITRE ATLAS, NIST AI RMF, OWASP LLM Top 10
• Chain of custody in AI pipeline artifacts
• AI attack trees and incident taxonomies

2 – Forensic Analysis of AI Pipelines

• Evidence sources: model weights, prompts, embeddings, logs, API calls
• Investigating prompt injections and context manipulation
• Detecting vector store tampering and data poisoning
• RAG forensic workflow (retriever → embedding → chunk provenance)
• LLM red team payload traceability and forensics

3 – MLOps, Cloud, and API Incident Response

• AI pipeline compromise in CI/CD
• Inference API abuse, model serving compromise (Triton, TorchServe, SageMaker)
• Audit logging and telemetry for model calls
• AI-related cloud forensics (AWS/GCP/Azure)
• Supply chain risk and artifact integrity (model registries, HuggingFace/ModelHub)

4 – Containment, Eradication, and Recovery

(Optional Advanced)

• Incident response plans tailored to AI use cases
• Reverting poisoned datasets and corrupted checkpoints
• Model rollback and contamination tracing
• Token abuse and unauthorized access to multi-tenant LLMs
• Digital signatures for models and evidence documentation

Capstone Forensics Lab & Report Generation

(Optional for Certification Track)

• Incident scenario: RAG system exploited via multi-level prompt injection
• Artifact collection and root cause analysis
• Attack path reconstruction and mitigation
• Report generation using NIST incident response format
• Forensics hand-off and legal considerations

Certification Exam Domains:

 Certification Exam:

• Format: 60–75 questions (MCQ + scenario-based)
• Passing Score: 70%
• Duration: 90 minutes
• Credential: Certified AI Forensics and Incident Response Specialist (CAFIRS)
• Badge: Digital certificate + Open Badges-compliant credential
• Validity: 3 years
• CEU Requirement: Optional for renewal or CAFIRS-Advanced track