About this book:

*Forensics of AI Systems* provides a comprehensive methodology for investigating security incidents within machine learning lifecycles. The book establishes that traditional digital forensics is insufficient for AI, necessitating specialized techniques to capture volatile artifacts like GPU memory residues, non-deterministic training states, and model weights. It categorizes a new breed of threats—including data poisoning, adversarial evasion, model extraction, and prompt injection—and maps these attacks to specific forensic signatures found in datasets, training pipelines, and inference telemetry.

The core of the book details a rigorous process for evidence preservation and chain of custody across distributed MLOps environments. It emphasizes the importance of model provenance, cryptographic signing, and artifact attestation to create an auditable history of an AI’s "education." By treating components like Jupyter Notebooks, Kubernetes clusters, and feature stores as digital crime scenes, the text provides playbooks for collecting immutable logs and snapshots that can withstand legal scrutiny.

A significant portion of the work is dedicated to incident reconstruction and attribution. Through the use of isolated forensic labs and differential analysis, investigators can reproduce malicious behaviors to confirm attack vectors. The book also addresses the legal dimensions of AI forensics, offering guidance on crafting expert testimony and meeting evidentiary standards such as the Daubert principle.

Ultimately, the book advocates for a proactive "forensics-by-design" approach. It concludes with strategies for building defensible AI systems through robust security controls, continuous monitoring via SIEM integration, and the development of readiness playbooks. By uniting machine learning engineering with cybersecurity operations, the text equips professionals to restore trust in AI systems after a compromise.

What You'll Find Inside:

• AI crime scene scoping and triage: How to identify, bound, and stabilize an AI incident while preserving volatile evidence across distributed ML components.
• Comprehensive threat taxonomy for ML systems: Data poisoning, adversarial evasion, model backdoors, prompt injection, model theft, membership inference, and supply chain attacks with forensic signatures.
• Evidence preservation techniques for AI artifacts: Collecting model weights, dataset fingerprints, pipeline logs, GPU/TPU memory, and inference telemetry while maintaining chain of custody.
• Legal foundations and evidentiary standards: Authenticating AI evidence, establishing chain of custody, preparing expert testimony, and ensuring admissibility in court or regulatory proceedings.
• Building defensible AI systems: Proactive controls, monitoring, hardening, and readiness playbooks to detect, attribute, and remediate AI incidents before they cause harm.

Who's It For:

This book is intended for digital forensic examiners, incident responders, MLOps engineers, data scientists, and legal counsel who need to investigate, attribute, and explain AI system compromises. It equips technical practitioners with forensic methodologies for ML pipelines and provides legal professionals with the grounding to assess AI evidence admissibility and support litigation or regulatory inquiries.